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In 2025, AI became a lightning rod for gamers and developers

In 2025, AI became a lightning rod for gamers and developers

2025 was the year generative AI made its presence felt in the video game industry. Its use has been discovered in some of the most popular games of the year, and CEOs from some of the largest game studios claim it’s being implemented everywhere in the industry including in their own development processes. Meanwhile, rank-and-file developers, especially in the indie games space, are pushing back against its encroachment, coming up with ways to signal their games are gen-AI free.

Generative AI has largely replaced NFTs as the buzzy trend publishers are chasing. Its proponents claim that the technology will be a great democratization force in video game development, as gen AI’s ability to amalgamate images, text, audio, and video could shorten development times and shrink budgets — ameliorating two major problems plaguing the industry right now. In service to that idea, numerous video game studios have announced partnerships with gen-AI companies.

Ubisoft has technology that can generate short snippets of dialogue called barks and has gen-AI powered NPCs that players can have conversations with. EA has partnered with Stability AI, Microsoft is using AI to analyze and generate gameplay. Outside of official partnerships, major game companies like Nexon, Krafton, and Square Enix are vocally embracing gen AI.

As a result, gen AI is starting to show up in games in a big way. Up until this point, gen AI in gaming had been mostly relegated to fringe cases — either prototypes or small, low-quality games that generally get lost in the tens of thousands of titles released on Steam each year. But now, gen AI is cropping up in the year’s biggest releases. ARC Raiders, one of the breakout multiplayer shooter hits of the year, used gen AI for character dialogue. Call of Duty: Black Ops 7 used gen-AI images. Even 2025’s TGA Game of the Year, Clair Obscur: Expedition 33, featured gen-AI images before they were quietly removed.

Reaction to this encroachment from both players and developers has been mixed. It seems like generally, players don’t like gen AI showing up in games. When gen-AI assets were discovered in Anno 117: Pax Romana, the game’s developer Ubisoft claimed the assets “slipped through” review and they were subsequently replaced. When gen-AI assets were found in Black Ops 7, however, Activision acknowledged the issue, but kept the images in the game. Critical response has also been lopsided. ARC Raiders was awarded low scores with reviewers specifically citing the use of gen AI as the reason. Clair Obscur, though, was nigh universally praised and its use of gen AI, however temporary, has barely been mentioned.

It seems like developers are sensitive to the public’s distaste for gen AI but are unwilling to commit to not using it. After gen-AI assets were discovered in Black Ops 7, Activision said it uses the tech to “empower” its developers, not replace them. When asked about gen AI showing up in Battlefield 6, EA VP Rebecka Coutaz called the technology seductive but affirmed it wouldn’t appear in the final product. Swen Vincke, CEO of Baldur’s Gate 3 developer Larian, said gen AI is being used for the studio’s next game Divinity but only for generating concepts and ideas. Everything in the finished game, he claimed, would be made by humans. He also hinted at why game makers insist on using the tech despite the backlash developers usually receive whenever it’s found.

“This is a tech-driven industry, so you try stuff,” he told Bloomberg reporter Jason Schreier in an interview. “You can’t afford not to try things because if somebody finds the golden egg and you’re not using it, you’re dead.”

Comments from other CEOs reinforce Vincke’s point. Junghun Lee, the CEO of ARC Raiders’ parent company Nexon, said in an interview that, “It’s important to assume that every game company is now using AI.”

The problem is, though, gen AI doesn’t yet seem to be the golden egg its supporters want people to believe it is. Last year, Keywords Studios, a game development services company, published a report on creating a 2D video game using only gen-AI tools. The company claimed that gen-AI tools can streamline some development processes but ultimately cannot replace the work of human talent. Discovering gen AI in Call of Duty and Pax Romana was possible precisely because of the low-quality of the images that were found. With Ubisoft’s interactive gen-AI NPCs, the dialogue they spout sounds unnatural and stilted. Players in the 2025 Chinese martial arts MMORPG Where Winds Meet are manipulating its AI chatbot NPCs to break the game, just like Fortnite players were able to make AI-powered Darth Vader swear.

For all the promises of gen AI, its current results do not live up to expectations. So why is it everywhere?

One reason is the competitive edge AI might but currently can’t provide that Swen Vincke alluded to in his interview with Bloomberg. Another reason is also the simplest: it’s the economy, stupid. Despite inflation, flagging consumer confidence and spending, and rising unemployment, the stock market is still booming, propped up by the billions and billions of dollars being poured into AI tech. Game makers in search of capital to keep business and profits going want in on that. Announcing AI initiatives and touting the use of AI tools — even if those tools have a relatively minor impact on the final product — can be a way to signal to AI-eager investors that a game company is worth their money.

That might explain why the majority of gen-AI’s supporters in gaming come from the C-suite of AAA studios and not smaller indie outfits who almost universally revile the tech. Indies face the same economic pressure as bigger studios but have far fewer resources to navigate those pressures. Ostensibly, indie developers are the ones who stand to benefit the most from the tech but, so far, are its biggest opponents. They are pushing back against the assertion that gen AI is everywhere, being used by everybody, with some marking their games with anti-AI logos proclaiming their games were made wholly by humans.

For some indie developers, using gen AI defeats the purpose of game making entirely. The challenge of coming up with ideas and solutions to development problems — the things gen AI is supposed to automate — is a big part of game making’s appeal to them. There are also moral and environmental implications indie developers seem especially sensitive to. Gen-AI outputs are cobbled from existing bodies of work that were often used without consent or compensation. AI data centers are notorious for consumptive energy usage and polluting their surrounding areas, which are increasingly focused in low-income and minority communities.
With its unrealized promises and so-far shoddy outputs, it’s easy to think of gen AI as gaming’s next flash in the pan the way NFTs were. But with gaming’s biggest companies increasingly reporting their use, gen AI will remain a lightning rod in game development — until the tech improves, or, like with NFTs, the bubble pops.

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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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