AI Act Directory's answer

Three things competitors don't combine: complete editorial independence (no pay-to-rank), explicit mapping to specific regulations (not just "AI tools"), and a static, structured-data-first site designed to be cited by both humans and AI assistants.

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.

AI Act Directory's answer

Generic AI directories like Futurepedia and TheresAnAIForThat list every AI tool without compliance focus. Gartner reports cost €4,000 and update slowly. G2 listings drown in noise and surface pay-to-rank vendors. AI Act Directory is the narrow, deep, free alternative for buyers who specifically need governance, risk, and EU AI Act compliance tooling, organized by what regulation each vendor actually covers.

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.

AI Act Directory's answer

Compliance officers, CISOs, general counsel, AI ops leads, and procurement teams at organizations subject to EU AI Act, NIST AI RMF, or ISO 42001. Mostly EU and US-based mid-market and enterprise. Secondary: AI vendors evaluating their own competitive landscape.

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.

AI Act Directory's answer

EU AI Act enforcement on high-risk AI systems lands 02 August 2026. While building automation tools for industrial PMI, I needed to evaluate compliance platforms and found the discovery process broken: paid placements everywhere, no neutral source, fragmented data across G2, Capterra, Forrester, and individual analyst blogs. So I built the directory I wanted to use.

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.

AI Act Directory's answer

SvelteKit, Cloudflare Pages, static prerendering, Schema.org structured data, JSON dataset version-controlled in Git.

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.