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.

useSherlock.ai's answer:

Sherlock Calls is the only tool that correlates voice AI call events across multiple providers (Twilio, ElevenLabs, Vapi, Retell AI, etc.) into a single incident case file posted in Slack. Most observability tools show dashboards. Sherlock answers specific questions like why did this call fail? The output is a Slack thread with a timestamped cross-provider timeline, root cause with evidence, troubleshooting options, and first checks in triage order, not another screen to monitor.

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.

useSherlock.ai's answer:

Generic APM tools like Datadog and New Relic were not built for voice AI stacks. They don't understand the relationship between Twilio telephony events and ElevenLabs TTS behavior, or how webhook delivery timing affects call outcomes. Sherlock is purpose-built for cross-provider voice call correlation. Setup is OAuth-only. 60 seconds, no code changes, no agent installation.

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.

useSherlock.ai's answer:

Engineering teams running voice AI in production: telephony engineers, on-call SREs, voice AI operators, and technical founders whose product relies on AI phone agents built on Twilio, Genesys, ElevenLabs, Vapi, or Retell AI, among others.

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.

useSherlock.ai's answer:

Built by Borja, Jorge and Jose after years working in the voice AI and telephony space. Every call failure investigation followed the same pattern: open the Twilio console, open the ElevenLabs dashboard, pull webhook logs, reconcile timestamps manually, guess at the root cause. Two to three hours per incident. They kept asking why no tool just answered the question. When they looked and found nothing purpose-built for voice AI stacks, they built it themselves.

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.

useSherlock.ai's answer:

Next.js, TypeScript, Supabase (PostgreSQL), All major LLMs, Slack API, Stripe, Vercel, Resend, Supabase

useSherlock.ai's answer:

We are our own first clients and we are seeking other like-minded individuals and teams facing the same problems that could try our product and provide us with some feedback.