Underpriced AI's answer

My wife flips thrift store and estate sale finds, and I watched her spend 10+ minutes researching every item on her phone before deciding to buy. I'm a software engineer, so I built a tool to do it in seconds — snap a photo and know what it's worth. What started as a side project to help her source faster evolved into a full reseller workflow: scan, price, list, and track.

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.

Underpriced AI's answer

It's the only tool that combines AI image recognition with real sold price data from 6+ marketplaces in a single scan. Most competitors either identify items OR look up prices — not both. You get identification, fair market value, comparable sales, and a ready-to-post listing from one photo.

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.

Underpriced AI's answer

Pricing is based on actual sold data across eBay, Poshmark, Mercari, Facebook, Etsy, and Depop — not just eBay asking prices. There's no monthly commitment required; you can buy a 5-scan pack for $4 and try it. It also goes from scan to eBay listing in two clicks, which no other pricing tool does.

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.

Underpriced AI's answer

Thrift store flippers, estate sale buyers, antique dealers, online resellers, and anyone who buys and sells secondhand goods. Also useful for people valuing donated items for tax purposes or settling estates.

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.

Underpriced AI's answer

Next.js, TypeScript, Claude AI (Anthropic) for image analysis, Perplexity AI for real-time market research, eBay Browse API for live listings, PostgreSQL, AWS, and Vercel.

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.

Underpriced AI's answer

• Independent resellers and thrift flippers
• Antique and vintage dealers
• Estate sale professionals