Stop Watching Videos. Start Talking To Them.
Turn YouTube videos into searchable knowledge for ChatGPT, Claude and MCP-compatible AI assistants.
No credit card. No extension. Works with videos you already have open.
sample library — no signup required
42:18Building a Production MCP Server: Stripe, Next.js, and Claude
Applied AI Engineering
A walkthrough of shipping a Model Context Protocol server in front of an existing Next.js app, using Stripe billing as the worked example, plus the deployment and auth decisions that actually bite in production.
- Insights
- 14
- Tools
- 11
- Prompts
- 4
Ask this video
Pick a question. You will get an answer grounded in the transcript, with the exact timestamps it came from.
Eight structured outputs from one URL
Not a wall of text. Each of these is a queryable record with a timestamp, which is what makes the library useful to an AI assistant rather than only to a human skimming.
Transcript
Time-anchored, searchable, exportable
Chapters
The real structure of the talk
AI summary
A TL;DR plus a substantive abstract
Key insights
The non-obvious claims, with timestamps
Action items
What to actually go do
Tools mentioned
Every product, library, and service
APIs mentioned
Endpoints, SDKs, and protocols
Prompts used
Reproduced as faithfully as the transcript allows
How it works
Paste a YouTube URL
One field. No browser extension, no screen recording, no upload. If a video has captions disabled, paste the transcript instead — everything downstream works identically.
We structure the knowledge
The transcript is chunked and time-anchored, then Claude extracts the summary, chapters, insights, action items, and every tool, API, prompt, and code snippet mentioned.
Search it, or let an AI search it
Hybrid vector and keyword retrieval powers search and grounded chat in the dashboard — and the same index is exposed over MCP to Claude, ChatGPT, and your IDE.
the same library, from inside Claude
> Which of my saved talks cover MCP auth?
search_videos({ query: "MCP auth" })
→ 3 videos
> Where does the Stripe one explain bearer keys?
find_timestamp({ query: "bearer keys", video: "..." })
→ 26:40 "clients support bearer far more consistently than OAuth today"Built for people who research in public
The common thread: the information you need already exists on YouTube, and watching is the slowest possible way to get at it.
AI founders
Every useful MCP and agent detail is buried in a 90-minute stream.
Ask what was said about tool schemas and get the timestamp, not a summary of the whole video.
SaaS founders
Competitor launch talks and conference sessions never get watched twice.
Build a collection per competitor and query it like a research database.
Developers
The code from a tutorial is on screen and nowhere else.
Snippets, APIs, and prompts extracted as first-class records your agent can retrieve.
Agencies
Client research lives in five different people’s notes apps.
One shared library, scoped collections, and an MCP endpoint your whole stack can read.
Technical marketers
Positioning claims and objections are scattered across dozens of talks.
Search every transcript at once and cite the exact quote in a doc.
Consultants
Billing hours to re-watch footage you have already seen once.
Ask the library instead. Answers arrive with citations you can paste into a deliverable.
Researchers
Video is the least citable medium in a literature review.
Every claim links to a timestamped source, so a video citation behaves like a page number.
Knowledge platform vs. summarizer vs. notes app
Summarizers compress a video once. Notes apps store what you typed. Neither produces something an AI assistant can query.
| Capability | VideoIntelHQ | AI summarizer | Notes app |
|---|---|---|---|
| Full time-anchored transcript | Yes | Yes | Yes |
| AI summary and chapters | Yes | Yes | No |
| Tools, APIs, and prompts extracted as records | Yes | No | No |
| Semantic search across every video | Yes | No | No |
| Grounded chat with timestamp citations | Yes | Partial | No |
| Collections scoped for search and chat | Yes | No | Yes |
| Readable by Claude, ChatGPT, and your IDE over MCP | Yes | No | No |
| Structured JSON built for agents, not humans | Yes | No | No |
Pricing preview
Planned launch pricing while the platform is in early access. Start on Starter — it is enough to decide whether the workflow fits how you actually work.
Starter
$0to try
Enough to prove the workflow on real videos.
- 5 videos
- Transcript, summary, chapters
- Keyword search
- AI chat with citations
Pro
$29per month
For one person with a real research habit.
- 150 videos per month
- Semantic search + hybrid retrieval
- Collections
- MCP server access
- Full knowledge extraction
Team
$99per month
Shared libraries for agencies and product teams.
- Unlimited videos
- Shared collections
- Multiple MCP keys
- Priority processing
- Export and API access
Early-access pricing is not final and may change before general availability. See current plans.
Frequently asked questions
What is MCP, and why does it matter here?
The Model Context Protocol is an open standard for connecting AI assistants to external tools and data. VideoIntelHQ ships a standards-compliant MCP server, so your video library becomes something Claude, ChatGPT, and MCP-capable IDEs can search directly — nine tools covering search, summaries, timestamps, prompts, APIs, and code. You are not copying answers out of our dashboard; the assistant queries the library itself.
How is this different from an AI video summarizer?
A summarizer gives you a paragraph and ends the conversation. We build a queryable index: time-anchored transcript chunks, extracted entities, hybrid semantic and keyword retrieval, and an MCP endpoint. The summary is one output among several, not the product.
What happens if a video has no captions?
Paste the transcript yourself. Lines beginning with a [mm:ss] marker are anchored exactly; text without timestamps is distributed across the runtime so citations stay approximately useful. Everything downstream — chunking, embeddings, extraction, search, MCP — works identically.
Are the timestamps and citations reliable?
Citations come from retrieved transcript chunks, and each carries the offset of the passage it was taken from, so every link opens YouTube at the moment the claim was made. The chat model is instructed to answer only from retrieved passages and to say so when they do not contain the answer, rather than filling the gap from training data.
Which AI clients can connect?
Anything that speaks MCP over Streamable HTTP: Claude Desktop, MCP-capable IDE extensions, and custom agents built on any SDK. You create a bearer API key in the dashboard, drop the endpoint and key into your client config, and the tools appear.
How long does a video take to process?
A typical 30-to-60-minute talk finishes in a couple of minutes: transcript retrieval, chunking, embeddings, then one extraction pass. Very long videos take proportionally longer, and processing is idempotent, so a retry never duplicates or corrupts anything.
The next video you were going to watch twice
Paste it once. Keep the knowledge, cite the timestamps, and let your AI assistant search it from now on.