Transcripts and summaries are useful for reading — but impossible to automate. The Extract API lets you define exactly what you need and get clean, structured JSON back from any video.
Video Data Extraction API: Turn Any Video Into Structured JSON
The Problem: Video Content Doesn't Scale
Every day, thousands of hours of video are published on YouTube, TikTok, Instagram, and X. Buried inside are competitor mentions, product reviews, pricing signals, customer pain points, expert insights, and buying intent — data that teams across your organization need.
But video data extraction today is broken. Sales teams manually watch webinars to find lead signals. Market researchers hire interns to catalog competitor mentions across hundreds of product reviews. Content teams scrub through hours of footage to pull a handful of quotes. And when teams try to automate with ChatGPT, they get inconsistent, free-form text that changes shape with every call — unusable for databases, CRMs, or pipelines.
VidNavigator's video data extraction API solves this. You define a JSON or YAML schema describing exactly the data points you need — companies, pricing, sentiment, action items, anything — and the API returns clean, validated, structured JSON that matches your schema every single time. No prompt engineering. No parsing code. No inconsistency.
Key Takeaways
- Define a custom schema (JSON or YAML) to extract exactly the data you need from any video — no prompt engineering required
- 2-phase AI pipeline: prompt compilation (cached) → structured extraction (Pydantic-enforced) — guaranteed consistent output
- Works with online videos (
/v1/extract/video) and uploaded files (/v1/extract/file) — YouTube, TikTok, Instagram, X, and 6+ more platforms - Prompt caching (2-hour TTL) means repeat extractions are instant — define a schema once, extract from hundreds of videos
- At 100 extractions per credit, processing 1,000 videos costs less than a single hour of manual research
Who Is This For?
Extract company names, decision-makers, pricing offers, pain points, and buying signals from sales calls, webinars, and competitor product demos — then push directly to your CRM.
Turn hundreds of competitor videos into structured datasets: positioning, feature claims, pricing strategies, target audience, and objections addressed. Build a competitive database that updates itself.
Identify hooks, viral quotes, sponsored mentions, content formats, and audience engagement patterns across creator videos and branded content. Scale content research without watching a single video.
Produce vector-ready summaries, typed entities, factual claims, and topic labels — structured for direct ingestion into RAG pipelines, knowledge bases, scoring systems, and AI agent workflows.
Monitor brand mentions, sentiment, promotional codes, creator recommendations, and purchase intent signals across product reviews, unboxings, and influencer content — across every platform, in any language.
Why Not Just Use ChatGPT?
You could paste a transcript into ChatGPT and ask for structured data. It works for one video. But here's what breaks at scale:
- Inconsistent output shape — ChatGPT returns slightly different JSON keys, structures, and formatting every time. You can't reliably pipe it into a database or API.
- No schema enforcement — the Extract API uses Pydantic to enforce your exact schema. Every response is guaranteed to match your field names, types, and nesting.
- No transcript pipeline — you have to manually get the transcript, paste it, and copy the result. The Extract API handles transcript retrieval, caching, and extraction in one call.
- No prompt caching — every ChatGPT call re-generates the prompt. VidNavigator caches the optimized extraction prompt, so repeat schemas are faster and cheaper.
- No batch automation — the Extract API is a REST endpoint. Loop over 1,000 video URLs, feed results into your pipeline. No copy-paste needed.
How It Works: The 2-Phase Pipeline
Phase 1 — Prompt Compilation (one-time, cached)
The API takes your schema and optional what_to_extract instruction and generates an optimized pair of AI prompts (system prompt + user prompt template). This compiled “extraction plan” is cached with a 2-hour TTL using a SHA-256 fingerprint of your schema + instructions. The next time you send the exact same schema within the cache window, the compilation step is skipped entirely.
Phase 2 — Structured Extraction
The cached prompt template is filled with the video's transcript text, then sent to the AI model with strict structured output enforcement (Pydantic-based). The result is validated JSON that exactly matches your custom schema extraction — no hallucinated fields, no missing keys.
Quickstart — extract/video
Use Case Templates
1. Lead Generation
Built for sales and BD teams. Extract companies, decision-makers, pricing signals, pain points, buying intent, and calls-to-action from sales calls, webinars, or product demos.
2. Market Research
Competitive intelligence for product and strategy teams. Map competitor mentions, feature claims, pricing strategies, target audiences, and objections addressed in industry talks and reviews.
3. Content & Creator Analysis
Designed for marketing and content teams. Capture hooks, key quotes, content format, sponsored product mentions, and audience engagement cues from creator videos and branded content.
4. AI Pipeline / RAG Ingestion
For AI builders and data engineers. Produce vector-ready summaries, named entities, factual claims, topic labels, language codes, and sentiment — structured for direct ingestion into RAG pipelines and knowledge bases.
5. Brand & E-Commerce Monitoring
Track brand mentions, promotional codes, creator recommendations, audience demographics cues, and purchase intent signals across product reviews, unboxings, and influencer content.
Extract from Uploaded Files
The /v1/extract/file endpoint works identically to /v1/extract/video but takes a file_id instead of video_url. The file must be uploaded and transcribed first via the file upload endpoints.
If an uploaded video or audio file doesn't have a transcript yet, call /v1/transcribe first to generate one via speech-to-text. The transcript is cached, so subsequent extractions on the same file are instant.
No Transcript? Call /transcribe First
The /v1/extract/video endpoint requires the video to already have a transcript. If the video doesn't have native captions — common with Instagram Reels, TikTok videos, and some Facebook posts — call /v1/transcribe first to generate a transcript via speech-to-text. The generated transcript is cached in VidNavigator's system, so you only pay for transcription once. After transcribing, call /v1/extract/video with the same URL.
Schema Rules
- •Max 10 root fields
- •Max 3 nesting levels (level 3 must be primitive only)
- •Max 10 subfields per Object
- •Supported types:
String,Number,Boolean,Integer,Array,Object,Enum - •Every field requires both
typeanddescription
Example Response
Prompt Caching — Why It Matters
Every extraction schema you send is fingerprinted using SHA-256 over the canonical JSON of your what_to_extract instruction and schema definition. The resulting hash is used to look up a previously compiled prompt plan in the database.
- The first call with a new schema has ~2–3s of compilation overhead
- All subsequent calls with the same schema skip compilation entirely
- Plans are cached for 2 hours — 2-hour TTL — plans are automatically recompiled when they expire
- Shared across all your videos within the cache window — define once, extract from many
- Changing the schema or instructions creates a new extraction plan
This means your AI video data extraction pipeline gets faster the more you use it. Once a schema is compiled, every subsequent video processed with that schema benefits from instant prompt reuse.
Best Practices
- Write specific field descriptions — the better your descriptions, the more accurate the extraction. Instead of “topic”, write “Primary topic discussed in the video, in 5–10 words”.
- Use Enum types for classification fields instead of free-text String. Enums constrain the AI output to your predefined values, eliminating inconsistency.
- Start with a simple schema and add fields iteratively. Test with 2–3 fields first, verify accuracy, then expand. Complex schemas are harder to debug.
- Use
what_to_extractto guide the AI's focus. This optional instruction steers the model toward specific parts of the transcript, improving relevance and reducing noise. - Write descriptions in your target language. The output is returned in the same language as your schema descriptions. Write field descriptions in French to get French results, in Spanish for Spanish, etc. — 99+ languages supported.
Comparison: Extract API vs. Other Endpoints
| Feature | Extract API | Raw Transcript | Analyze API |
|---|---|---|---|
| Custom output schema | ✅ | ❌ | ❌ |
| JSON / YAML input | ✅ | ❌ | ❌ |
| Prompt caching | ✅ | N/A | ❌ |
| Structured output | ✅ Pydantic-enforced | Raw text | Free-form |
| Works with files | ✅ /extract/file | ❌ | ✅ /analyze/file |
| Works with videos | ✅ /extract/video | ✅ /transcript | ✅ /analyze/video |
Real-World Example: Competitive Intelligence Pipeline
Imagine you're a product team tracking how competitors position themselves. Here's a pipeline you can build in an afternoon:
- Collect URLs — gather 200 YouTube video URLs from competitor channels, industry conferences, and product review creators.
- Define your schema once — use the Market Research template: competitors mentioned, feature claims, pricing strategy, positioning, objections addressed.
- Loop and extract — call
/v1/extract/videofor each URL. The first call compiles the prompt; the remaining 199 reuse the cached plan instantly. - Store results — push the structured JSON into a Postgres database, Google Sheet, or your data warehouse.
- Analyze — query your database: “Which competitors were mentioned most? What features are they claiming? Where is pricing being discussed?”
Total cost: 200 videos = 200 video analyses = 2 credits. Total time: minutes, not weeks. And the schema is reusable — run it again next month on new videos with zero setup.
Pricing: Built for Scale
Each extraction counts as 1 video analysis. With VidNavigator, 1 credit = 100 video analyses.
This includes both the prompt compilation (if needed) and the structured extraction. Compare that to the cost of a research analyst manually watching and cataloging video content — or the engineering time to build and maintain a custom GPT wrapper with parsing, retries, and schema validation.
See the pricing page for full plan details and volume options.