Crowdin's answer

Crowdin uniquely combines agile continuous localization with a highly customizable, enterprise-grade AI infrastructure. You can connect translation projects with over 700 apps and integrations. Crowdin offers exclusive capabilities like an AI Context Harvester, BYO key for any LLM connection, 40+ MT engines availability, custom workflows, Over-The-Air (OTA) deployment for mobile apps, and enterprise-grade security to protect your data.

TranscriptFetch's answer:

Most short-form video has no caption track to download. TikTok’s auto-captions are opt-in per upload, Instagram never publishes a downloadable track, and many captions on both are burned into the video frames where no parser can read them. TranscriptFetch transcribes the audio when no caption track exists, on the same endpoint, with the same response shape. Your code never branches on which method produced the text. It also covers YouTube, TikTok, Instagram, X and Facebook plus any web page as clean Markdown, so a pipeline spanning several sources is one integration rather than five.

Crowdin's answer

Enterprises choose Crowdin for its unmatched customizability and strict security compliance (ISO/IEC 27001). It completely automates complex localization pipelines using powerful developer tools (Git syncing, APIs, Over-The-Air updates), while guaranteeing accuracy by giving both AI and human linguists perfect visual context through auto-tagged screenshots and in-context editors.

TranscriptFetch's answer:

Three reasons. Coverage: one API key and one response shape across five video platforms and the open web, instead of stitching together a library per platform. Reliability: requests run through rotating infrastructure, so code that works locally keeps working from a server, which is where most open-source approaches break. Billing that matches reality: one credit per successful response, with failed, blocked and empty results never charged. That last point matters on short-form video, where a meaningful share of any batch is music with no speech in it. There is also an MCP server, so AI agents can fetch transcripts as a tool without a custom integration.

Crowdin's answer

The company was founded in 2008 by Ukrainian programmer Serhiy Dmytryshyn as a hobby project for localization of small projects. The platform was officially launched in January 2009. Since then, it has been adopted among software and game development companies for software translation. The launch of Crowdin Enterprise came in 2020. In early 2020s, Crowdin began incorporating AI and large language model (LLM) capabilities into its platform. By 2025, over 3 million registered users across 160 countries had signed up on the platform.

TranscriptFetch's answer:

It started with discovering there is no good way to get the text of a video. YouTube’s official Data API will confirm a caption track exists and then refuse to hand it over, because captions.download requires the video owner’s OAuth token. The popular open-source libraries work until you deploy them, at which point platforms start refusing datacenter IPs. And YouTube is the easy case: TikTok and Instagram publish no caption file at all. Every workaround solved one platform, worked locally, and broke in production. TranscriptFetch is the version that handles the failure cases as first-class behaviour rather than edge cases.

Crowdin's answer

• Intel
• Xiaomi
• OnePlus
• GitHub
• GitLab
• Raspberry Pi
• TYPO3
• Joomla
• Magento
• PrestaShop
• Calendly
• Wrike
• Pipedrive
• Strava
• Preply
• JetBrains
• Kickstarter
• Khan Academy
• Code.org
• Meeds
• Mojang
• Wildlife Studios
• Coffee Stain

Crowdin's answer

Crowdin’s primary audience is large global enterprises, fast-scaling tech companies, and agile development teams. Specifically software, app, and game studios. Those teams that require highly secure, continuous localization deeply integrated into their CI/CD pipelines at scale.

TranscriptFetch's answer:

Developers and technical teams building on video and web content. The common cases are RAG and retrieval pipelines that need video as text, AI agents that need to read a link mid-conversation, content teams repurposing short-form video at scale, and media monitoring and research tools. It is an API first, so the buyer is usually the person writing the integration rather than an end user. The free browser tools exist for one-off transcripts and for evaluating output quality before writing any code.

TranscriptFetch's answer:

Next.js with TypeScript and Tailwind on the front end and API layer, Clerk for auth with SHA-256 hashed API keys, Neon Postgres with Drizzle ORM, Redis for caching, and Stripe for billing. The extraction layer is a Python and FastAPI service. Speech-to-text uses Whisper-class models. The MCP server is published in the official Model Context Protocol registry with a DNS-verified namespace.