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It’s primetime for conspiracy theorist video creatorsIn the days since this year’s White House Correspondents’ Dinner was cut short when shots were fired at the event, there has been a boom of conspiracy theory videos created by people who insist that the entire situation was a false flag operation. These kinds of theories are nothing new, but the way they’re spreading now is a reflection of how reaction video culture is reshaping our social media landscape. And even though the initial chaos around the shooting has started to die down, content creators are still posting about what “really” happened.There is still much we do not know about Cole Allen, the 31-year-old suspected shooter who allegedly traveled from Los Angeles to Washington, DC, ahead of the WCHD and was staying in the same Hilton where the event was held. But that has not stopped content creators from flooding platforms like YouTube, TikTok, Instagram, and X with videos purporting to have more insightful takes on the situation than what’s being reported by the mainstream media.None of these videos reveal anything that hasn’t already been reported out via traditional media outlets. But each of them speaks to the way that this brand of content has become a normal part of people’s media consumption habits and something that creators see as a viable way to capture attention. In the US, trust in traditional media outlets is at a historic low and more people are turning to social media to stay informed about world events. And that shift has given conspiracy-minded content creators a choice opportunity to influence the way people understand reality.All of this is similar to what happened in 2024 when Donald Trump survived an assassination attempt while campaigning for the presidency. Then, creators rushed to capitalize on the event while also writing it off as a false flag designed to garner sympathy for the Republican nominee. That news cycle and subsequent discourse dragged on for weeks, both because it was a significant moment in an election year and because it was difficult to understand how Trump could have been shot in his ear without sustaining any visible damage afterward.Many of the newer videos about the WHCD shooting suggest that we should look at these events as a response to the Trump administration’s propensity for spreading misinformation. And while there is no evidence to suggest that the WHCD shooting was, in fact, orchestrated with Trump’s approval, one could argue the administration is at least partially responsible for the way that this idea has gained traction across the internet.As easy as it is to laugh at the constant barrage of shitposts coming out of the president’s social media accounts and other official governmental channels, they have undoubtedly had an impact on the way that the public thinks about the current administration. By sharing ugly, immature memes and AI-generated images of Trump as a Christlike figure, the White House has told people that nothing is to be taken seriously and everything can be turned into a crude joke. And at a time when all of the internet’s biggest social media platforms have begun encouraging their users to upload videos of themselves while chasing engagement, it makes sense that many would see this past weekend’s shooting as a chance to boost their profiles.Trump has made nonsensical “jokes” a significant part of his political brand, and people are responding with very similar energy.#primetime #conspiracy #theorist #video #creatorsCreators,Instagram,Meta,Streaming,Tech,TikTok,YouTube

It’s primetime for conspiracy theorist video creators

In the days since this year’s White House Correspondents’ Dinner was cut short when shots were fired at the event, there has been a boom of conspiracy theory videos created by people who insist that the entire situation was a false flag operation. These kinds of theories are nothing new, but the way they’re spreading now is a reflection of how reaction video culture is reshaping our social media landscape. And even though the initial chaos around the shooting has started to die down, content creators are still posting about what “really” happened.

There is still much we do not know about Cole Allen, the 31-year-old suspected shooter who allegedly traveled from Los Angeles to Washington, DC, ahead of the WCHD and was staying in the same Hilton where the event was held. But that has not stopped content creators from flooding platforms like YouTube, TikTok, Instagram, and X with videos purporting to have more insightful takes on the situation than what’s being reported by the mainstream media.

None of these videos reveal anything that hasn’t already been reported out via traditional media outlets. But each of them speaks to the way that this brand of content has become a normal part of people’s media consumption habits and something that creators see as a viable way to capture attention. In the US, trust in traditional media outlets is at a historic low and more people are turning to social media to stay informed about world events. And that shift has given conspiracy-minded content creators a choice opportunity to influence the way people understand reality.

All of this is similar to what happened in 2024 when Donald Trump survived an assassination attempt while campaigning for the presidency. Then, creators rushed to capitalize on the event while also writing it off as a false flag designed to garner sympathy for the Republican nominee. That news cycle and subsequent discourse dragged on for weeks, both because it was a significant moment in an election year and because it was difficult to understand how Trump could have been shot in his ear without sustaining any visible damage afterward.

Many of the newer videos about the WHCD shooting suggest that we should look at these events as a response to the Trump administration’s propensity for spreading misinformation. And while there is no evidence to suggest that the WHCD shooting was, in fact, orchestrated with Trump’s approval, one could argue the administration is at least partially responsible for the way that this idea has gained traction across the internet.

As easy as it is to laugh at the constant barrage of shitposts coming out of the president’s social media accounts and other official governmental channels, they have undoubtedly had an impact on the way that the public thinks about the current administration. By sharing ugly, immature memes and AI-generated images of Trump as a Christlike figure, the White House has told people that nothing is to be taken seriously and everything can be turned into a crude joke. And at a time when all of the internet’s biggest social media platforms have begun encouraging their users to upload videos of themselves while chasing engagement, it makes sense that many would see this past weekend’s shooting as a chance to boost their profiles.

Trump has made nonsensical “jokes” a significant part of his political brand, and people are responding with very similar energy.

#primetime #conspiracy #theorist #video #creatorsCreators,Instagram,Meta,Streaming,Tech,TikTok,YouTube

In the days since this year’s White House Correspondents’ Dinner was cut short when shots were fired at the event, there has been a boom of conspiracy theory videos created by people who insist that the entire situation was a false flag operation. These kinds of theories are nothing new, but the way they’re spreading now is a reflection of how reaction video culture is reshaping our social media landscape. And even though the initial chaos around the shooting has started to die down, content creators are still posting about what “really” happened.

There is still much we do not know about Cole Allen, the 31-year-old suspected shooter who allegedly traveled from Los Angeles to Washington, DC, ahead of the WCHD and was staying in the same Hilton where the event was held. But that has not stopped content creators from flooding platforms like YouTube, TikTok, Instagram, and X with videos purporting to have more insightful takes on the situation than what’s being reported by the mainstream media.

None of these videos reveal anything that hasn’t already been reported out via traditional media outlets. But each of them speaks to the way that this brand of content has become a normal part of people’s media consumption habits and something that creators see as a viable way to capture attention. In the US, trust in traditional media outlets is at a historic low and more people are turning to social media to stay informed about world events. And that shift has given conspiracy-minded content creators a choice opportunity to influence the way people understand reality.

All of this is similar to what happened in 2024 when Donald Trump survived an assassination attempt while campaigning for the presidency. Then, creators rushed to capitalize on the event while also writing it off as a false flag designed to garner sympathy for the Republican nominee. That news cycle and subsequent discourse dragged on for weeks, both because it was a significant moment in an election year and because it was difficult to understand how Trump could have been shot in his ear without sustaining any visible damage afterward.

Many of the newer videos about the WHCD shooting suggest that we should look at these events as a response to the Trump administration’s propensity for spreading misinformation. And while there is no evidence to suggest that the WHCD shooting was, in fact, orchestrated with Trump’s approval, one could argue the administration is at least partially responsible for the way that this idea has gained traction across the internet.

As easy as it is to laugh at the constant barrage of shitposts coming out of the president’s social media accounts and other official governmental channels, they have undoubtedly had an impact on the way that the public thinks about the current administration. By sharing ugly, immature memes and AI-generated images of Trump as a Christlike figure, the White House has told people that nothing is to be taken seriously and everything can be turned into a crude joke. And at a time when all of the internet’s biggest social media platforms have begun encouraging their users to upload videos of themselves while chasing engagement, it makes sense that many would see this past weekend’s shooting as a chance to boost their profiles.

Trump has made nonsensical “jokes” a significant part of his political brand, and people are responding with very similar energy.



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Deadspin | Reports: NCAA finalizing plan to expand March Madness to 76 teams <div id=""><section id="0" class=" w-full"><div class="xl:container mx-0 !px-4 py-0 pb-4 !mx-0 !px-0"><img src="https://images.deadspin.com/tr:w-900/28564350.jpg" srcset="https://images.deadspin.com/tr:w-900/28564350.jpg" alt="Syndication: The Oklahoman" class="w-full" fetchpriority="high" loading="eager"/><span class="text-0.8 leading-tight">The March Madness logo is pictured during a second-round game in the NCAA men’s basketball tournament between Nebraska Cornhuskers and Vanderbilt Commodores at Paycom Center in Oklahoma City, Saturday March 21, 2026.<!-- --> <!-- --> </span></div></section><section id="section-1"> <p>The men’s and women’s NCAA Tournament fields will expand from 68 to 76 teams in 2027.</p> </section><section id="section-2"> <p>The plans for expansion are expected to be approved by NCAA committees and formalized as soon as May, multiple reports said Tuesday.</p> </section><section id="section-3"> <p>CBS Sports reported that the NCAA plans for 52 teams to slot into the main bracket and the other 24 teams will face off in 12 games on the Tuesday and Wednesday after Selection Sunday, filling out the Round of 64 with the winners. It will no longer be called the “First Four,” with the terminology expected to be “opening round” for the play-ins and “first round” for the Round of 64.</p> </section><br/><section id="section-4"> <p>Per ESPN, the NCAA is completing contract negotiations with its media partners. That step must come before votes from the men’s and women’s basketball committees, the men’s and women’s basketball oversight committees, the Division I Cabinet and the Division I Board of Governors.</p> </section> <section id="section-5"> <p>It would mark the first expansion of the tournament since the field moved from 65 to 68 teams with the addition of the First Four games in 2011. The field had been 64 or 65 teams since 1985.</p> </section><section id="section-6"> <p>The Big 12 and Atlantic Coast Conference were the leading voices behind tournament expansion, Yahoo Sports reported earlier this month. NCAA president Charlie Baker has also voiced his support.</p> </section><section id="section-7"> <p>“I said all along that I think there are some very good reasons to expand the tournament,” Baker told ESPN in February. “So, I would like to see it expand.”</p> </section><br/><section id="section-8"> <p>–Field Level Media</p> </section> </div> #Deadspin #Reports #NCAA #finalizing #plan #expand #March #Madness #teams

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Deadspin | Lightning, Canadiens enter pivotal Game 5 of closely contested series <div id=""><section id="0" class=" w-full"><div class="xl:container mx-0 !px-4 py-0 pb-4 !mx-0 !px-0"><img src="https://images.deadspin.com/tr:w-900/28821104.jpg" srcset="https://images.deadspin.com/tr:w-900/28821104.jpg" alt="NHL: Stanley Cup Playoffs-Tampa Bay Lightning at Montreal Canadiens" class="w-full" fetchpriority="high" loading="eager"/><span class="text-0.8 leading-tight">Apr 26, 2026; Montreal, Quebec, CAN; Tampa Bay Lightning defenseman Darren Raddysh (43) defends the puck against Montreal Canadiens right wing Josh Anderson (17) during the third period in game four of the first round of the 2026 Stanley Cup Playoffs at Bell Centre. Mandatory Credit: David Kirouac-Imagn Images<!-- --> <!-- --> </span></div></section><section id="section-1"> <p>The Tampa Bay Lightning have returned home tied 2-2 in their Eastern Conference first-round matchup with the Montreal Canadiens, and according to coach Jon Cooper, it may not be that way if not for the play of Max Crozier. </p> </section><section id="section-2"> <p>Game 5 takes place in Tampa on Wednesday night after a two-day break following Sunday’s 3-2 Lightning victory in Montreal in front of a boisterous bunch of Habs fans, both inside the NHL’s largest arena and outside watching on a giant screen broadcast.</p> </section><section id="section-3"> <p>If one glaring point is gleaned through four contests between the Atlantic Division foes, it is that this best-of-seven series has been the tightest of the first round’s eight matchups, about as evenly played as is mathematically possible.</p> </section><section id="section-4"> <p>In addition to splitting the four matches, each side has produced 11 goals and three of the four meetings have required extra time.</p> </section><section id="section-5"> <p>On the power play thus far, Montreal, which finished 10th during the regular season, has connected on 5 of 19 chances (26.3%). The Lightning were middling, ranking 17th this season, but have potted four goals in their 20 times on the man advantage (20%). </p> </section><section id="section-6"> <p>When the numbers are that close, a play out of the ordinary that generally does not jump off the scoresheet can make a big difference.</p> </section><section id="section-7"> <p>Something like Crozier’s Sunday second-period high hit on Montreal’s star winger Juraj Slafkovsky, who netted a hat trick in Game 1 in Tampa on three power-plays tallies, including the game-winner in overtime. </p> </section><section id="section-8"> <p>The defenseman, who only played in 35 games due to surgery, waylaid Slafkovsky at center ice at high speed, sending the 2022 No. 1 overall selection straight to the dressing room to regroup.</p> </section><section id="section-9"> <p>The Lightning were outhit 50-28 by the Habs, but Crozier’s lone leveling body blow altered the tone.</p> </section><br/><section id="section-10"> <p>“The hit obviously got our bench out of their seats,” Cooper said. “But you still have to take advantage of that. We score in the last minute of the second and in the first (two minutes) of the third, and all of a sudden, the game’s completely changed. </p> </section> <section id="section-11"> <p>“(Crozier’s hit) helped take the crowd out of it.”</p> </section><section id="section-12"> <p>Instead of maintaining or building on its 2-0 lead that could have resulted in a 3-1 series advantage, Montreal watched it all slip away by allowing three unanswered goals to the visitors. </p> </section><section id="section-13"> <p>Brandon Hagel hit the net for the game-tying and game-winning markers in the third to send the series back to Tampa all square.</p> </section><section id="section-14"> <p>Montreal has relied on its top forward line of Cole Caufield (goal, three assists), Nick Suzuki (four helpers) and Slafkovsky (three tallies) for much of the offense, and second-line forward Alex Newhook said the Habs’ secondary scoring must improve.</p> </section><section id="section-15"> <p>Newhook plays with center Oliver Kapanen and right winger Ivan Demidov. Only Demidov has produced a point by assisting on Slafkovsky’s first power-play goal in Game 1’s 4-3 shocker.</p> </section><section id="section-16"> <p>“It’s something we talk over and try to find solutions (for) here throughout the series as to how,” said Newhook, who posted 13 goals and 25 points in 42 games after fracturing his ankle in mid-November. ” … Fundamentally, getting back to some basics is important this time of the year.</p> </section><section id="section-17"> <p>“I think we found some success when we’re keeping it simple and throwing it behind them. Then being able to go and win a battle.”</p> </section><section id="section-18"> <p>Game 6, the series’ first elimination game regardless of Wednesday’s result, is Friday in Montreal. </p> </section><section id="section-19"> <p>–Field Level Media</p> </section></div> #Deadspin #Lightning #Canadiens #enter #pivotal #Game #closely #contested #series

Indian serial entrepreneur Bhavin Turakhia is making a $30 million personal bet that there is still room for another enterprise AI company. His new venture, Neo, is built on a simple premise: workplace software designed before the AI era cannot simply be upgraded with chatbots — it has to be redesigned from the ground up.

Turakhia, 46, is no stranger to ambitious enterprise technology bets. Over the past two decades, he has co-founded companies including Directi, Radix, Titan, and banking software firm Zeta, largely backing them with his own cash before bringing in outside investors. He’s doing the same with Neo.

Turakhia told TechCrunch he is bootstrapping this much money because he believes AI marks a technology shift significant enough to justify rebuilding workplace software from scratch.

“If you want to build an iPhone, you can’t take the parts of a Nokia and somehow convert it into an iPhone,” he said.

Launched internally in April this year, Neo is an enterprise work platform that combines project management, documents, file storage, and AI into a single product. The goal, Turakhia said, is to make AI an active participant in day-to-day work rather than just another assistant employees turn to separately.

Turakhia argued most incumbents face a structural disadvantage when adding AI to products designed before generative AI. Neo, he said, was designed from the ground up for AI and is model-agnostic, allowing enterprises to switch between AI models rather than being tied to a single provider.

He’s not alone in thinking this way. Investor Chamath Palihapitiya initially launched enterprise AI coding venture 8090 with his own capital before raising a $135 million funding round this week.

Still, Turakhia’s bet comes as enterprise AI has emerged as one of the most competitive areas in technology. Microsoft, Google, and Salesforce are embedding AI across their workplace software. Meanwhile every startup from the giant labs like Anthropic and OpenAI, to the productivity companies like Notion and Superhuman are racing to reshape how businesses use AI in their daily workflow.

Turakhia argued enterprise software has never been a winner-takes-all market, saying even a small share of global enterprise AI spending would represent a sizeable company.

“Even if we end up with 2% to 5% market share, that’s larger than anything I’ve built so far,” he said.

For the past few months, Neo has been in internal use across Turakhia’s companies, including Zeta. The company plans to begin rolling out the software to mid-sized businesses in the coming months, initially targeting knowledge workers across technology, consulting, and professional services firms.

Turakhia said Neo’s initial platform was built in three months, with AI extensively used in the development process, work he estimates would have taken more than a year with a much larger engineering team before generative AI.

The Bengaluru-based startup currently employs about 45 people, including 18 engineers. Turakhia told TechCrunch that it expects to grow to around 100 employees by the end of the year, with most new hires focused on AI and software engineering.

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

#Indian #tech #tycoon #bets #30M #money #build #alternative #Microsoft #Office #TechCrunchBhavin Turakhia,microsoft office,neo">Indian tech tycoon bets M of his own money to build AI alternative to Microsoft Office | TechCrunch
Indian serial entrepreneur Bhavin Turakhia is making a  million personal bet that there is still room for another enterprise AI company. His new venture, Neo, is built on a simple premise: workplace software designed before the AI era cannot simply be upgraded with chatbots — it has to be redesigned from the ground up.

Turakhia, 46, is no stranger to ambitious enterprise technology bets. Over the past two decades, he has co-founded companies including Directi, Radix, Titan, and banking software firm Zeta, largely backing them with his own cash before bringing in outside investors. He’s doing the same with Neo.







Turakhia told TechCrunch he is bootstrapping this much money because he believes AI marks a technology shift significant enough to justify rebuilding workplace software from scratch.

“If you want to build an iPhone, you can’t take the parts of a Nokia and somehow convert it into an iPhone,” he said.

Launched internally in April this year, Neo is an enterprise work platform that combines project management, documents, file storage, and AI into a single product. The goal, Turakhia said, is to make AI an active participant in day-to-day work rather than just another assistant employees turn to separately.

Turakhia argued most incumbents face a structural disadvantage when adding AI to products designed before generative AI. Neo, he said, was designed from the ground up for AI and is model-agnostic, allowing enterprises to switch between AI models rather than being tied to a single provider.

He’s not alone in thinking this way. Investor Chamath Palihapitiya initially launched enterprise AI coding venture 8090 with his own capital before raising a 5 million funding round this week.


Still, Turakhia’s bet comes as enterprise AI has emerged as one of the most competitive areas in technology. Microsoft, Google, and Salesforce are embedding AI across their workplace software. Meanwhile every startup from the giant labs like Anthropic and OpenAI, to the productivity companies like Notion and Superhuman are racing to reshape how businesses use AI in their daily workflow.

Turakhia argued enterprise software has never been a winner-takes-all market, saying even a small share of global enterprise AI spending would represent a sizeable company.

“Even if we end up with 2% to 5% market share, that’s larger than anything I’ve built so far,” he said.







For the past few months, Neo has been in internal use across Turakhia’s companies, including Zeta. The company plans to begin rolling out the software to mid-sized businesses in the coming months, initially targeting knowledge workers across technology, consulting, and professional services firms.

Turakhia said Neo’s initial platform was built in three months, with AI extensively used in the development process, work he estimates would have taken more than a year with a much larger engineering team before generative AI.

The Bengaluru-based startup currently employs about 45 people, including 18 engineers. Turakhia told TechCrunch that it expects to grow to around 100 employees by the end of the year, with most new hires focused on AI and software engineering.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.#Indian #tech #tycoon #bets #30M #money #build #alternative #Microsoft #Office #TechCrunchBhavin Turakhia,microsoft office,neo

Neo, is built on a simple premise: workplace software designed before the AI era cannot simply be upgraded with chatbots — it has to be redesigned from the ground up.

Turakhia, 46, is no stranger to ambitious enterprise technology bets. Over the past two decades, he has co-founded companies including Directi, Radix, Titan, and banking software firm Zeta, largely backing them with his own cash before bringing in outside investors. He’s doing the same with Neo.

Turakhia told TechCrunch he is bootstrapping this much money because he believes AI marks a technology shift significant enough to justify rebuilding workplace software from scratch.

“If you want to build an iPhone, you can’t take the parts of a Nokia and somehow convert it into an iPhone,” he said.

Launched internally in April this year, Neo is an enterprise work platform that combines project management, documents, file storage, and AI into a single product. The goal, Turakhia said, is to make AI an active participant in day-to-day work rather than just another assistant employees turn to separately.

Turakhia argued most incumbents face a structural disadvantage when adding AI to products designed before generative AI. Neo, he said, was designed from the ground up for AI and is model-agnostic, allowing enterprises to switch between AI models rather than being tied to a single provider.

He’s not alone in thinking this way. Investor Chamath Palihapitiya initially launched enterprise AI coding venture 8090 with his own capital before raising a $135 million funding round this week.

Still, Turakhia’s bet comes as enterprise AI has emerged as one of the most competitive areas in technology. Microsoft, Google, and Salesforce are embedding AI across their workplace software. Meanwhile every startup from the giant labs like Anthropic and OpenAI, to the productivity companies like Notion and Superhuman are racing to reshape how businesses use AI in their daily workflow.

Turakhia argued enterprise software has never been a winner-takes-all market, saying even a small share of global enterprise AI spending would represent a sizeable company.

“Even if we end up with 2% to 5% market share, that’s larger than anything I’ve built so far,” he said.

For the past few months, Neo has been in internal use across Turakhia’s companies, including Zeta. The company plans to begin rolling out the software to mid-sized businesses in the coming months, initially targeting knowledge workers across technology, consulting, and professional services firms.

Turakhia said Neo’s initial platform was built in three months, with AI extensively used in the development process, work he estimates would have taken more than a year with a much larger engineering team before generative AI.

The Bengaluru-based startup currently employs about 45 people, including 18 engineers. Turakhia told TechCrunch that it expects to grow to around 100 employees by the end of the year, with most new hires focused on AI and software engineering.

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

#Indian #tech #tycoon #bets #30M #money #build #alternative #Microsoft #Office #TechCrunchBhavin Turakhia,microsoft office,neo">Indian tech tycoon bets $30M of his own money to build AI alternative to Microsoft Office | TechCrunch

Indian serial entrepreneur Bhavin Turakhia is making a $30 million personal bet that there is still room for another enterprise AI company. His new venture, Neo, is built on a simple premise: workplace software designed before the AI era cannot simply be upgraded with chatbots — it has to be redesigned from the ground up.

Turakhia, 46, is no stranger to ambitious enterprise technology bets. Over the past two decades, he has co-founded companies including Directi, Radix, Titan, and banking software firm Zeta, largely backing them with his own cash before bringing in outside investors. He’s doing the same with Neo.

Turakhia told TechCrunch he is bootstrapping this much money because he believes AI marks a technology shift significant enough to justify rebuilding workplace software from scratch.

“If you want to build an iPhone, you can’t take the parts of a Nokia and somehow convert it into an iPhone,” he said.

Launched internally in April this year, Neo is an enterprise work platform that combines project management, documents, file storage, and AI into a single product. The goal, Turakhia said, is to make AI an active participant in day-to-day work rather than just another assistant employees turn to separately.

Turakhia argued most incumbents face a structural disadvantage when adding AI to products designed before generative AI. Neo, he said, was designed from the ground up for AI and is model-agnostic, allowing enterprises to switch between AI models rather than being tied to a single provider.

He’s not alone in thinking this way. Investor Chamath Palihapitiya initially launched enterprise AI coding venture 8090 with his own capital before raising a $135 million funding round this week.

Still, Turakhia’s bet comes as enterprise AI has emerged as one of the most competitive areas in technology. Microsoft, Google, and Salesforce are embedding AI across their workplace software. Meanwhile every startup from the giant labs like Anthropic and OpenAI, to the productivity companies like Notion and Superhuman are racing to reshape how businesses use AI in their daily workflow.

Turakhia argued enterprise software has never been a winner-takes-all market, saying even a small share of global enterprise AI spending would represent a sizeable company.

“Even if we end up with 2% to 5% market share, that’s larger than anything I’ve built so far,” he said.

For the past few months, Neo has been in internal use across Turakhia’s companies, including Zeta. The company plans to begin rolling out the software to mid-sized businesses in the coming months, initially targeting knowledge workers across technology, consulting, and professional services firms.

Turakhia said Neo’s initial platform was built in three months, with AI extensively used in the development process, work he estimates would have taken more than a year with a much larger engineering team before generative AI.

The Bengaluru-based startup currently employs about 45 people, including 18 engineers. Turakhia told TechCrunch that it expects to grow to around 100 employees by the end of the year, with most new hires focused on AI and software engineering.

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

#Indian #tech #tycoon #bets #30M #money #build #alternative #Microsoft #Office #TechCrunchBhavin Turakhia,microsoft office,neo
How Do You Get Started With Transkriptor?

To start with Transkriptor, it does not take more than 1 minute. You can sign up with Google, Microsoft, Apple, or email. Transkriptor leans on a row of recognizable logos, from Pfizer and Tesla to Harvard and Microsoft, to build your trust before using it.

Transkriptor offers a clean, easy-to-navigate dashboard with 5 ways to create a transcript. You can record live audio, upload a file, pull a video from YouTube, join a meeting, and import audio-video files from the cloud. A left rail holds the heavier tools, including text-to-speech, AI content generation, and a calendar for scheduled meetings. The core action is never more than one click; you get transcription without any technical difficulties.

How Accurate is Transkriptor at Converting Speech to Text?

Accuracy is the most important thing, and an honest answer is better than a flawless one. On clean English audio, meaning a single speaker in a quiet room, Transkriptor landed in the high-80s to low-90s percent range in my tests, which matches what independent reviewers report. If you upload a clean 30-minute file, it will take you only a few minutes to check for grammar mistakes, mostly punctuation marks.

I started testing the tool by uploading different audio and video files, and Transkriptor supports a wide range of formats, so I never had to convert the file before uploading.

Transkriptor Review: Is It the Best Speech-to-Text App?
	
Manual transcription takes much time that most people do not have. I usually spend hours every week turning interviews and meetings into text, so I tried Transkriptor through a test to see if it could take notes and save me time. Transkriptor is an AI speech-to-text tool. It converts audio and video files into editable transcripts, and it supports 100+ languages. Over a week, I uploaded my clean and slightly messy recordings, ran them against accented audio, and also linked them to Zoom and Google Meet calls.



Here is how Transkriptor does well, where it goes wrong, and who should use it. 



How Do You Get Started With Transkriptor?



To start with Transkriptor, it does not take more than 1 minute. You can sign up with Google, Microsoft, Apple, or email. Transkriptor leans on a row of recognizable logos, from Pfizer and Tesla to Harvard and Microsoft, to build your trust before using it.



Transkriptor offers a clean, easy-to-navigate dashboard with 5 ways to create a transcript. You can record live audio, upload a file, pull a video from YouTube, join a meeting, and import audio-video files from the cloud. A left rail holds the heavier tools, including text-to-speech, AI content generation, and a calendar for scheduled meetings. The core action is never more than one click; you get transcription without any technical difficulties.



How Accurate is Transkriptor at Converting Speech to Text?



Accuracy is the most important thing, and an honest answer is better than a flawless one. On clean English audio, meaning a single speaker in a quiet room, Transkriptor landed in the high-80s to low-90s percent range in my tests, which matches what independent reviewers report. If you upload a clean 30-minute file, it will take you only a few minutes to check for grammar mistakes, mostly punctuation marks.



I started testing the tool by uploading different audio and video files, and Transkriptor supports a wide range of formats, so I never had to convert the file before uploading.







Audio with background noise and overlapping speakers leads to less accurate transcription. Also, non-native heavy accents reduced accuracy. Transkriptor supports 100+ languages and adds domain-specific vocabulary for medical, legal, and IT terms, which helped with a jargon-heavy recording, though non-English audio was less even than English.



Transkriptor’s editor did the real work. Every line of transcription carries a timestamp and speaker label. You can play back the audio while reading the transcription to ensure everything is up to the point. Additionally, AI chat and summary let you pull a quick recap of the whole conversation. You get richer insights, such as sentiment analysis and speaker talk time, but it is locked behind the Team plan.







Does Transkriptor Handle Zoom, Google Meet, and Teams Meetings?



Yes, Transkriptor handles Zoom, Google Meet, and Teams meetings with ease. You paste the link to add a recording bot to the live call. Or you can connect your Google or Outlook calendar so Transkriptor auto-joins scheduled meetings. I connected my Google Calendar in 2 clicks and set it to auto-detect the platform and record the meeting.







After each call, I got a transcript with speaker labels and an auto-generated summary with action items, which is exactly what a remote team wants from a note-taker. The bot-joins-the-call model is the same approach Otter uses, and Transkriptor matches it while supporting far more languages.



What Does Transkriptor Cost, and How Does It Compare to Otter and Sonix?



To get access to all features, you need to buy a Transkriptor subscription. It’s a limited free tier with a small daily allowance that lets you test it. Lite plan starts at .99 per month for 5 hours of transcription. Pro is .99 per month or .33 per month on annual billing (.99 a year) and unlocks 2,400 minutes per month with unlimited files. 



Team runs  per seat monthly, or  per seat on annual billing (0 a year per seat), adding 3,000 minutes per seat, shared workspaces, call analysis, and custom vocabulary. A custom-priced Business tier is available for larger orgs. Transkriptor is also ISO 27001, SOC 2, and GDPR compliant, which matters for regulated work.



Against the alternative transcription tools, Transkriptor lands in a useful middle ground. Otter is the polished meeting assistant with strong CRM sync, but it transcribes only 6 languages and caps your minutes. Sonix charges per hour and delivers the highest audio accuracy. Here is how the three line up.



ToolEntry pricingLanguagesBest atWatch out forTranskriptor.99/mo, free tier available100+Files plus live meeting recordings in one toolAccuracy dips on noisy or accented audioOtterFree, then .33/mo annual6 languagesLive meeting notes and CRM syncFew languages, strict minute capsSonix per audio hour, pay as you go50+High accuracy on clean filesNo live meeting recording



Who is Transkriptor Best For?



With Transkriptor, you get a practical mix of transcription, meeting recording, AI summaries, and multilingual support. During my testing, Transkriptor handled clean audio and video files well and integrated smoothly with Zoom, Google Meet, and Teams. It made it easy to turn speech into readable meeting notes and summaries.



While accuracy can vary with heavy background noise or challenging accents, the overall experience is reliable enough for most everyday transcription needs. The combination of 100+ language support, meeting integrations, and competitive pricing gives it a broader feature set than many alternatives.



For students, journalists, podcasters, and remote teams working across multiple languages, Transkriptor is a capable and cost-effective speech-to-text solution.

#Transkriptor #Review #SpeechtoText #AppAI

Audio with background noise and overlapping speakers leads to less accurate transcription. Also, non-native heavy accents reduced accuracy. Transkriptor supports 100+ languages and adds domain-specific vocabulary for medical, legal, and IT terms, which helped with a jargon-heavy recording, though non-English audio was less even than English.

Transkriptor’s editor did the real work. Every line of transcription carries a timestamp and speaker label. You can play back the audio while reading the transcription to ensure everything is up to the point. Additionally, AI chat and summary let you pull a quick recap of the whole conversation. You get richer insights, such as sentiment analysis and speaker talk time, but it is locked behind the Team plan.

Home page of transkriptor

Does Transkriptor Handle Zoom, Google Meet, and Teams Meetings?

Yes, Transkriptor handles Zoom, Google Meet, and Teams meetings with ease. You paste the link to add a recording bot to the live call. Or you can connect your Google or Outlook calendar so Transkriptor auto-joins scheduled meetings. I connected my Google Calendar in 2 clicks and set it to auto-detect the platform and record the meeting.

Live meeting section

After each call, I got a transcript with speaker labels and an auto-generated summary with action items, which is exactly what a remote team wants from a note-taker. The bot-joins-the-call model is the same approach Otter uses, and Transkriptor matches it while supporting far more languages.

What Does Transkriptor Cost, and How Does It Compare to Otter and Sonix?

To get access to all features, you need to buy a Transkriptor subscription. It’s a limited free tier with a small daily allowance that lets you test it. Lite plan starts at $9.99 per month for 5 hours of transcription. Pro is $19.99 per month or $8.33 per month on annual billing ($99.99 a year) and unlocks 2,400 minutes per month with unlimited files. 

Team runs $30 per seat monthly, or $20 per seat on annual billing ($240 a year per seat), adding 3,000 minutes per seat, shared workspaces, call analysis, and custom vocabulary. A custom-priced Business tier is available for larger orgs. Transkriptor is also ISO 27001, SOC 2, and GDPR compliant, which matters for regulated work.

Against the alternative transcription tools, Transkriptor lands in a useful middle ground. Otter is the polished meeting assistant with strong CRM sync, but it transcribes only 6 languages and caps your minutes. Sonix charges per hour and delivers the highest audio accuracy. Here is how the three line up.

ToolEntry pricingLanguagesBest atWatch out for
Transkriptor$9.99/mo, free tier available100+Files plus live meeting recordings in one toolAccuracy dips on noisy or accented audio
OtterFree, then $8.33/mo annual6 languagesLive meeting notes and CRM syncFew languages, strict minute caps
Sonix$10 per audio hour, pay as you go50+High accuracy on clean filesNo live meeting recording

Who is Transkriptor Best For?

With Transkriptor, you get a practical mix of transcription, meeting recording, AI summaries, and multilingual support. During my testing, Transkriptor handled clean audio and video files well and integrated smoothly with Zoom, Google Meet, and Teams. It made it easy to turn speech into readable meeting notes and summaries.

While accuracy can vary with heavy background noise or challenging accents, the overall experience is reliable enough for most everyday transcription needs. The combination of 100+ language support, meeting integrations, and competitive pricing gives it a broader feature set than many alternatives.

For students, journalists, podcasters, and remote teams working across multiple languages, Transkriptor is a capable and cost-effective speech-to-text solution.

#Transkriptor #Review #SpeechtoText #AppAI">Transkriptor Review: Is It the Best Speech-to-Text App?
	
Manual transcription takes much time that most people do not have. I usually spend hours every week turning interviews and meetings into text, so I tried Transkriptor through a test to see if it could take notes and save me time. Transkriptor is an AI speech-to-text tool. It converts audio and video files into editable transcripts, and it supports 100+ languages. Over a week, I uploaded my clean and slightly messy recordings, ran them against accented audio, and also linked them to Zoom and Google Meet calls.



Here is how Transkriptor does well, where it goes wrong, and who should use it. 



How Do You Get Started With Transkriptor?



To start with Transkriptor, it does not take more than 1 minute. You can sign up with Google, Microsoft, Apple, or email. Transkriptor leans on a row of recognizable logos, from Pfizer and Tesla to Harvard and Microsoft, to build your trust before using it.



Transkriptor offers a clean, easy-to-navigate dashboard with 5 ways to create a transcript. You can record live audio, upload a file, pull a video from YouTube, join a meeting, and import audio-video files from the cloud. A left rail holds the heavier tools, including text-to-speech, AI content generation, and a calendar for scheduled meetings. The core action is never more than one click; you get transcription without any technical difficulties.



How Accurate is Transkriptor at Converting Speech to Text?



Accuracy is the most important thing, and an honest answer is better than a flawless one. On clean English audio, meaning a single speaker in a quiet room, Transkriptor landed in the high-80s to low-90s percent range in my tests, which matches what independent reviewers report. If you upload a clean 30-minute file, it will take you only a few minutes to check for grammar mistakes, mostly punctuation marks.



I started testing the tool by uploading different audio and video files, and Transkriptor supports a wide range of formats, so I never had to convert the file before uploading.







Audio with background noise and overlapping speakers leads to less accurate transcription. Also, non-native heavy accents reduced accuracy. Transkriptor supports 100+ languages and adds domain-specific vocabulary for medical, legal, and IT terms, which helped with a jargon-heavy recording, though non-English audio was less even than English.



Transkriptor’s editor did the real work. Every line of transcription carries a timestamp and speaker label. You can play back the audio while reading the transcription to ensure everything is up to the point. Additionally, AI chat and summary let you pull a quick recap of the whole conversation. You get richer insights, such as sentiment analysis and speaker talk time, but it is locked behind the Team plan.







Does Transkriptor Handle Zoom, Google Meet, and Teams Meetings?



Yes, Transkriptor handles Zoom, Google Meet, and Teams meetings with ease. You paste the link to add a recording bot to the live call. Or you can connect your Google or Outlook calendar so Transkriptor auto-joins scheduled meetings. I connected my Google Calendar in 2 clicks and set it to auto-detect the platform and record the meeting.







After each call, I got a transcript with speaker labels and an auto-generated summary with action items, which is exactly what a remote team wants from a note-taker. The bot-joins-the-call model is the same approach Otter uses, and Transkriptor matches it while supporting far more languages.



What Does Transkriptor Cost, and How Does It Compare to Otter and Sonix?



To get access to all features, you need to buy a Transkriptor subscription. It’s a limited free tier with a small daily allowance that lets you test it. Lite plan starts at .99 per month for 5 hours of transcription. Pro is .99 per month or .33 per month on annual billing (.99 a year) and unlocks 2,400 minutes per month with unlimited files. 



Team runs  per seat monthly, or  per seat on annual billing (0 a year per seat), adding 3,000 minutes per seat, shared workspaces, call analysis, and custom vocabulary. A custom-priced Business tier is available for larger orgs. Transkriptor is also ISO 27001, SOC 2, and GDPR compliant, which matters for regulated work.



Against the alternative transcription tools, Transkriptor lands in a useful middle ground. Otter is the polished meeting assistant with strong CRM sync, but it transcribes only 6 languages and caps your minutes. Sonix charges per hour and delivers the highest audio accuracy. Here is how the three line up.



ToolEntry pricingLanguagesBest atWatch out forTranskriptor.99/mo, free tier available100+Files plus live meeting recordings in one toolAccuracy dips on noisy or accented audioOtterFree, then .33/mo annual6 languagesLive meeting notes and CRM syncFew languages, strict minute capsSonix per audio hour, pay as you go50+High accuracy on clean filesNo live meeting recording



Who is Transkriptor Best For?



With Transkriptor, you get a practical mix of transcription, meeting recording, AI summaries, and multilingual support. During my testing, Transkriptor handled clean audio and video files well and integrated smoothly with Zoom, Google Meet, and Teams. It made it easy to turn speech into readable meeting notes and summaries.



While accuracy can vary with heavy background noise or challenging accents, the overall experience is reliable enough for most everyday transcription needs. The combination of 100+ language support, meeting integrations, and competitive pricing gives it a broader feature set than many alternatives.



For students, journalists, podcasters, and remote teams working across multiple languages, Transkriptor is a capable and cost-effective speech-to-text solution.

#Transkriptor #Review #SpeechtoText #AppAI

Transkriptor, it does not take more than 1 minute. You can sign up with Google, Microsoft, Apple, or email. Transkriptor leans on a row of recognizable logos, from Pfizer and Tesla to Harvard and Microsoft, to build your trust before using it.

Transkriptor offers a clean, easy-to-navigate dashboard with 5 ways to create a transcript. You can record live audio, upload a file, pull a video from YouTube, join a meeting, and import audio-video files from the cloud. A left rail holds the heavier tools, including text-to-speech, AI content generation, and a calendar for scheduled meetings. The core action is never more than one click; you get transcription without any technical difficulties.

How Accurate is Transkriptor at Converting Speech to Text?

Accuracy is the most important thing, and an honest answer is better than a flawless one. On clean English audio, meaning a single speaker in a quiet room, Transkriptor landed in the high-80s to low-90s percent range in my tests, which matches what independent reviewers report. If you upload a clean 30-minute file, it will take you only a few minutes to check for grammar mistakes, mostly punctuation marks.

I started testing the tool by uploading different audio and video files, and Transkriptor supports a wide range of formats, so I never had to convert the file before uploading.

Transkriptor Review: Is It the Best Speech-to-Text App?
	
Manual transcription takes much time that most people do not have. I usually spend hours every week turning interviews and meetings into text, so I tried Transkriptor through a test to see if it could take notes and save me time. Transkriptor is an AI speech-to-text tool. It converts audio and video files into editable transcripts, and it supports 100+ languages. Over a week, I uploaded my clean and slightly messy recordings, ran them against accented audio, and also linked them to Zoom and Google Meet calls.



Here is how Transkriptor does well, where it goes wrong, and who should use it. 



How Do You Get Started With Transkriptor?



To start with Transkriptor, it does not take more than 1 minute. You can sign up with Google, Microsoft, Apple, or email. Transkriptor leans on a row of recognizable logos, from Pfizer and Tesla to Harvard and Microsoft, to build your trust before using it.



Transkriptor offers a clean, easy-to-navigate dashboard with 5 ways to create a transcript. You can record live audio, upload a file, pull a video from YouTube, join a meeting, and import audio-video files from the cloud. A left rail holds the heavier tools, including text-to-speech, AI content generation, and a calendar for scheduled meetings. The core action is never more than one click; you get transcription without any technical difficulties.



How Accurate is Transkriptor at Converting Speech to Text?



Accuracy is the most important thing, and an honest answer is better than a flawless one. On clean English audio, meaning a single speaker in a quiet room, Transkriptor landed in the high-80s to low-90s percent range in my tests, which matches what independent reviewers report. If you upload a clean 30-minute file, it will take you only a few minutes to check for grammar mistakes, mostly punctuation marks.



I started testing the tool by uploading different audio and video files, and Transkriptor supports a wide range of formats, so I never had to convert the file before uploading.







Audio with background noise and overlapping speakers leads to less accurate transcription. Also, non-native heavy accents reduced accuracy. Transkriptor supports 100+ languages and adds domain-specific vocabulary for medical, legal, and IT terms, which helped with a jargon-heavy recording, though non-English audio was less even than English.



Transkriptor’s editor did the real work. Every line of transcription carries a timestamp and speaker label. You can play back the audio while reading the transcription to ensure everything is up to the point. Additionally, AI chat and summary let you pull a quick recap of the whole conversation. You get richer insights, such as sentiment analysis and speaker talk time, but it is locked behind the Team plan.







Does Transkriptor Handle Zoom, Google Meet, and Teams Meetings?



Yes, Transkriptor handles Zoom, Google Meet, and Teams meetings with ease. You paste the link to add a recording bot to the live call. Or you can connect your Google or Outlook calendar so Transkriptor auto-joins scheduled meetings. I connected my Google Calendar in 2 clicks and set it to auto-detect the platform and record the meeting.







After each call, I got a transcript with speaker labels and an auto-generated summary with action items, which is exactly what a remote team wants from a note-taker. The bot-joins-the-call model is the same approach Otter uses, and Transkriptor matches it while supporting far more languages.



What Does Transkriptor Cost, and How Does It Compare to Otter and Sonix?



To get access to all features, you need to buy a Transkriptor subscription. It’s a limited free tier with a small daily allowance that lets you test it. Lite plan starts at .99 per month for 5 hours of transcription. Pro is .99 per month or .33 per month on annual billing (.99 a year) and unlocks 2,400 minutes per month with unlimited files. 



Team runs  per seat monthly, or  per seat on annual billing (0 a year per seat), adding 3,000 minutes per seat, shared workspaces, call analysis, and custom vocabulary. A custom-priced Business tier is available for larger orgs. Transkriptor is also ISO 27001, SOC 2, and GDPR compliant, which matters for regulated work.



Against the alternative transcription tools, Transkriptor lands in a useful middle ground. Otter is the polished meeting assistant with strong CRM sync, but it transcribes only 6 languages and caps your minutes. Sonix charges per hour and delivers the highest audio accuracy. Here is how the three line up.



ToolEntry pricingLanguagesBest atWatch out forTranskriptor.99/mo, free tier available100+Files plus live meeting recordings in one toolAccuracy dips on noisy or accented audioOtterFree, then .33/mo annual6 languagesLive meeting notes and CRM syncFew languages, strict minute capsSonix per audio hour, pay as you go50+High accuracy on clean filesNo live meeting recording



Who is Transkriptor Best For?



With Transkriptor, you get a practical mix of transcription, meeting recording, AI summaries, and multilingual support. During my testing, Transkriptor handled clean audio and video files well and integrated smoothly with Zoom, Google Meet, and Teams. It made it easy to turn speech into readable meeting notes and summaries.



While accuracy can vary with heavy background noise or challenging accents, the overall experience is reliable enough for most everyday transcription needs. The combination of 100+ language support, meeting integrations, and competitive pricing gives it a broader feature set than many alternatives.



For students, journalists, podcasters, and remote teams working across multiple languages, Transkriptor is a capable and cost-effective speech-to-text solution.

#Transkriptor #Review #SpeechtoText #AppAI

Audio with background noise and overlapping speakers leads to less accurate transcription. Also, non-native heavy accents reduced accuracy. Transkriptor supports 100+ languages and adds domain-specific vocabulary for medical, legal, and IT terms, which helped with a jargon-heavy recording, though non-English audio was less even than English.

Transkriptor’s editor did the real work. Every line of transcription carries a timestamp and speaker label. You can play back the audio while reading the transcription to ensure everything is up to the point. Additionally, AI chat and summary let you pull a quick recap of the whole conversation. You get richer insights, such as sentiment analysis and speaker talk time, but it is locked behind the Team plan.

Home page of transkriptor

Does Transkriptor Handle Zoom, Google Meet, and Teams Meetings?

Yes, Transkriptor handles Zoom, Google Meet, and Teams meetings with ease. You paste the link to add a recording bot to the live call. Or you can connect your Google or Outlook calendar so Transkriptor auto-joins scheduled meetings. I connected my Google Calendar in 2 clicks and set it to auto-detect the platform and record the meeting.

Live meeting section

After each call, I got a transcript with speaker labels and an auto-generated summary with action items, which is exactly what a remote team wants from a note-taker. The bot-joins-the-call model is the same approach Otter uses, and Transkriptor matches it while supporting far more languages.

What Does Transkriptor Cost, and How Does It Compare to Otter and Sonix?

To get access to all features, you need to buy a Transkriptor subscription. It’s a limited free tier with a small daily allowance that lets you test it. Lite plan starts at $9.99 per month for 5 hours of transcription. Pro is $19.99 per month or $8.33 per month on annual billing ($99.99 a year) and unlocks 2,400 minutes per month with unlimited files. 

Team runs $30 per seat monthly, or $20 per seat on annual billing ($240 a year per seat), adding 3,000 minutes per seat, shared workspaces, call analysis, and custom vocabulary. A custom-priced Business tier is available for larger orgs. Transkriptor is also ISO 27001, SOC 2, and GDPR compliant, which matters for regulated work.

Against the alternative transcription tools, Transkriptor lands in a useful middle ground. Otter is the polished meeting assistant with strong CRM sync, but it transcribes only 6 languages and caps your minutes. Sonix charges per hour and delivers the highest audio accuracy. Here is how the three line up.

ToolEntry pricingLanguagesBest atWatch out for
Transkriptor$9.99/mo, free tier available100+Files plus live meeting recordings in one toolAccuracy dips on noisy or accented audio
OtterFree, then $8.33/mo annual6 languagesLive meeting notes and CRM syncFew languages, strict minute caps
Sonix$10 per audio hour, pay as you go50+High accuracy on clean filesNo live meeting recording

Who is Transkriptor Best For?

With Transkriptor, you get a practical mix of transcription, meeting recording, AI summaries, and multilingual support. During my testing, Transkriptor handled clean audio and video files well and integrated smoothly with Zoom, Google Meet, and Teams. It made it easy to turn speech into readable meeting notes and summaries.

While accuracy can vary with heavy background noise or challenging accents, the overall experience is reliable enough for most everyday transcription needs. The combination of 100+ language support, meeting integrations, and competitive pricing gives it a broader feature set than many alternatives.

For students, journalists, podcasters, and remote teams working across multiple languages, Transkriptor is a capable and cost-effective speech-to-text solution.

#Transkriptor #Review #SpeechtoText #AppAI">Transkriptor Review: Is It the Best Speech-to-Text App?

Manual transcription takes much time that most people do not have. I usually spend hours every week turning interviews and meetings into text, so I tried Transkriptor through a test to see if it could take notes and save me time. Transkriptor is an AI speech-to-text tool. It converts audio and video files into editable transcripts, and it supports 100+ languages. Over a week, I uploaded my clean and slightly messy recordings, ran them against accented audio, and also linked them to Zoom and Google Meet calls.

Here is how Transkriptor does well, where it goes wrong, and who should use it. 

How Do You Get Started With Transkriptor?

To start with Transkriptor, it does not take more than 1 minute. You can sign up with Google, Microsoft, Apple, or email. Transkriptor leans on a row of recognizable logos, from Pfizer and Tesla to Harvard and Microsoft, to build your trust before using it.

Transkriptor offers a clean, easy-to-navigate dashboard with 5 ways to create a transcript. You can record live audio, upload a file, pull a video from YouTube, join a meeting, and import audio-video files from the cloud. A left rail holds the heavier tools, including text-to-speech, AI content generation, and a calendar for scheduled meetings. The core action is never more than one click; you get transcription without any technical difficulties.

How Accurate is Transkriptor at Converting Speech to Text?

Accuracy is the most important thing, and an honest answer is better than a flawless one. On clean English audio, meaning a single speaker in a quiet room, Transkriptor landed in the high-80s to low-90s percent range in my tests, which matches what independent reviewers report. If you upload a clean 30-minute file, it will take you only a few minutes to check for grammar mistakes, mostly punctuation marks.

I started testing the tool by uploading different audio and video files, and Transkriptor supports a wide range of formats, so I never had to convert the file before uploading.

Transkriptor Review: Is It the Best Speech-to-Text App?
	
Manual transcription takes much time that most people do not have. I usually spend hours every week turning interviews and meetings into text, so I tried Transkriptor through a test to see if it could take notes and save me time. Transkriptor is an AI speech-to-text tool. It converts audio and video files into editable transcripts, and it supports 100+ languages. Over a week, I uploaded my clean and slightly messy recordings, ran them against accented audio, and also linked them to Zoom and Google Meet calls.



Here is how Transkriptor does well, where it goes wrong, and who should use it. 



How Do You Get Started With Transkriptor?



To start with Transkriptor, it does not take more than 1 minute. You can sign up with Google, Microsoft, Apple, or email. Transkriptor leans on a row of recognizable logos, from Pfizer and Tesla to Harvard and Microsoft, to build your trust before using it.



Transkriptor offers a clean, easy-to-navigate dashboard with 5 ways to create a transcript. You can record live audio, upload a file, pull a video from YouTube, join a meeting, and import audio-video files from the cloud. A left rail holds the heavier tools, including text-to-speech, AI content generation, and a calendar for scheduled meetings. The core action is never more than one click; you get transcription without any technical difficulties.



How Accurate is Transkriptor at Converting Speech to Text?



Accuracy is the most important thing, and an honest answer is better than a flawless one. On clean English audio, meaning a single speaker in a quiet room, Transkriptor landed in the high-80s to low-90s percent range in my tests, which matches what independent reviewers report. If you upload a clean 30-minute file, it will take you only a few minutes to check for grammar mistakes, mostly punctuation marks.



I started testing the tool by uploading different audio and video files, and Transkriptor supports a wide range of formats, so I never had to convert the file before uploading.







Audio with background noise and overlapping speakers leads to less accurate transcription. Also, non-native heavy accents reduced accuracy. Transkriptor supports 100+ languages and adds domain-specific vocabulary for medical, legal, and IT terms, which helped with a jargon-heavy recording, though non-English audio was less even than English.



Transkriptor’s editor did the real work. Every line of transcription carries a timestamp and speaker label. You can play back the audio while reading the transcription to ensure everything is up to the point. Additionally, AI chat and summary let you pull a quick recap of the whole conversation. You get richer insights, such as sentiment analysis and speaker talk time, but it is locked behind the Team plan.







Does Transkriptor Handle Zoom, Google Meet, and Teams Meetings?



Yes, Transkriptor handles Zoom, Google Meet, and Teams meetings with ease. You paste the link to add a recording bot to the live call. Or you can connect your Google or Outlook calendar so Transkriptor auto-joins scheduled meetings. I connected my Google Calendar in 2 clicks and set it to auto-detect the platform and record the meeting.







After each call, I got a transcript with speaker labels and an auto-generated summary with action items, which is exactly what a remote team wants from a note-taker. The bot-joins-the-call model is the same approach Otter uses, and Transkriptor matches it while supporting far more languages.



What Does Transkriptor Cost, and How Does It Compare to Otter and Sonix?



To get access to all features, you need to buy a Transkriptor subscription. It’s a limited free tier with a small daily allowance that lets you test it. Lite plan starts at .99 per month for 5 hours of transcription. Pro is .99 per month or .33 per month on annual billing (.99 a year) and unlocks 2,400 minutes per month with unlimited files. 



Team runs  per seat monthly, or  per seat on annual billing (0 a year per seat), adding 3,000 minutes per seat, shared workspaces, call analysis, and custom vocabulary. A custom-priced Business tier is available for larger orgs. Transkriptor is also ISO 27001, SOC 2, and GDPR compliant, which matters for regulated work.



Against the alternative transcription tools, Transkriptor lands in a useful middle ground. Otter is the polished meeting assistant with strong CRM sync, but it transcribes only 6 languages and caps your minutes. Sonix charges per hour and delivers the highest audio accuracy. Here is how the three line up.



ToolEntry pricingLanguagesBest atWatch out forTranskriptor.99/mo, free tier available100+Files plus live meeting recordings in one toolAccuracy dips on noisy or accented audioOtterFree, then .33/mo annual6 languagesLive meeting notes and CRM syncFew languages, strict minute capsSonix per audio hour, pay as you go50+High accuracy on clean filesNo live meeting recording



Who is Transkriptor Best For?



With Transkriptor, you get a practical mix of transcription, meeting recording, AI summaries, and multilingual support. During my testing, Transkriptor handled clean audio and video files well and integrated smoothly with Zoom, Google Meet, and Teams. It made it easy to turn speech into readable meeting notes and summaries.



While accuracy can vary with heavy background noise or challenging accents, the overall experience is reliable enough for most everyday transcription needs. The combination of 100+ language support, meeting integrations, and competitive pricing gives it a broader feature set than many alternatives.



For students, journalists, podcasters, and remote teams working across multiple languages, Transkriptor is a capable and cost-effective speech-to-text solution.

#Transkriptor #Review #SpeechtoText #AppAI

Audio with background noise and overlapping speakers leads to less accurate transcription. Also, non-native heavy accents reduced accuracy. Transkriptor supports 100+ languages and adds domain-specific vocabulary for medical, legal, and IT terms, which helped with a jargon-heavy recording, though non-English audio was less even than English.

Transkriptor’s editor did the real work. Every line of transcription carries a timestamp and speaker label. You can play back the audio while reading the transcription to ensure everything is up to the point. Additionally, AI chat and summary let you pull a quick recap of the whole conversation. You get richer insights, such as sentiment analysis and speaker talk time, but it is locked behind the Team plan.

Home page of transkriptor

Does Transkriptor Handle Zoom, Google Meet, and Teams Meetings?

Yes, Transkriptor handles Zoom, Google Meet, and Teams meetings with ease. You paste the link to add a recording bot to the live call. Or you can connect your Google or Outlook calendar so Transkriptor auto-joins scheduled meetings. I connected my Google Calendar in 2 clicks and set it to auto-detect the platform and record the meeting.

Live meeting section

After each call, I got a transcript with speaker labels and an auto-generated summary with action items, which is exactly what a remote team wants from a note-taker. The bot-joins-the-call model is the same approach Otter uses, and Transkriptor matches it while supporting far more languages.

What Does Transkriptor Cost, and How Does It Compare to Otter and Sonix?

To get access to all features, you need to buy a Transkriptor subscription. It’s a limited free tier with a small daily allowance that lets you test it. Lite plan starts at $9.99 per month for 5 hours of transcription. Pro is $19.99 per month or $8.33 per month on annual billing ($99.99 a year) and unlocks 2,400 minutes per month with unlimited files. 

Team runs $30 per seat monthly, or $20 per seat on annual billing ($240 a year per seat), adding 3,000 minutes per seat, shared workspaces, call analysis, and custom vocabulary. A custom-priced Business tier is available for larger orgs. Transkriptor is also ISO 27001, SOC 2, and GDPR compliant, which matters for regulated work.

Against the alternative transcription tools, Transkriptor lands in a useful middle ground. Otter is the polished meeting assistant with strong CRM sync, but it transcribes only 6 languages and caps your minutes. Sonix charges per hour and delivers the highest audio accuracy. Here is how the three line up.

ToolEntry pricingLanguagesBest atWatch out for
Transkriptor$9.99/mo, free tier available100+Files plus live meeting recordings in one toolAccuracy dips on noisy or accented audio
OtterFree, then $8.33/mo annual6 languagesLive meeting notes and CRM syncFew languages, strict minute caps
Sonix$10 per audio hour, pay as you go50+High accuracy on clean filesNo live meeting recording

Who is Transkriptor Best For?

With Transkriptor, you get a practical mix of transcription, meeting recording, AI summaries, and multilingual support. During my testing, Transkriptor handled clean audio and video files well and integrated smoothly with Zoom, Google Meet, and Teams. It made it easy to turn speech into readable meeting notes and summaries.

While accuracy can vary with heavy background noise or challenging accents, the overall experience is reliable enough for most everyday transcription needs. The combination of 100+ language support, meeting integrations, and competitive pricing gives it a broader feature set than many alternatives.

For students, journalists, podcasters, and remote teams working across multiple languages, Transkriptor is a capable and cost-effective speech-to-text solution.

#Transkriptor #Review #SpeechtoText #AppAI

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