Tork.network's answer

• Currently in early access with enterprise pilots underway

Tork.network's answer

Tork is purpose-built as middleware — it sits between AI agents and the tools they call, not as a standalone audit tool. Every request gets PII detection (~1ms, 50+ patterns, 13 countries), policy enforcement, and a cryptographic compliance receipt. No other platform covers 79+ framework adapters across 6 languages with 3 lines of code to integrate.

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.

Tork.network's answer

Most AI governance tools focus on model evaluation or bias detection after the fact. Tork operates in real-time at the middleware layer — intercepting, scanning, and enforcing policies on every agent interaction as it happens. It's framework-agnostic (LangChain, CrewAI, AutoGen, MCP, OpenAI Agents), open source (MIT), and has a generous free tier.

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.

Tork.network's answer

Engineering teams deploying AI agents in production, compliance officers needing audit-ready evidence for HIPAA/GDPR/SOC 2, and security engineers managing PII exposure across autonomous AI workflows.

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.

Tork.network's answer

Next.js, TypeScript, Supabase, Redis (Upstash), Vercel Edge Runtime. SDKs in Python, JavaScript, Go, Ruby, Rust, Java, PHP, .NET, Swift, Elixir, and Kotlin.

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.

Tork.network's answer

We saw teams rushing AI agents into production with no governance layer. Agents were calling APIs, accessing databases, and handling user data with zero audit trail. Tork was built to be the seatbelt — not slowing agents down, but making sure every interaction is safe, compliant, and traceable.

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.