Searchable video library and analysis console

For

Product and data teams running large video libraries that need search, summarization and analysis

Solves

Video content is hard to search, summarize and analyze at scale, and the work is spread across several rented tools.

Delivers

Reviewed search results, summaries and analysis records linked to time-stamped evidence

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$12,500 for the MVP, $42,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce time to find and analyze relevant video moments while keeping the library and its embeddings under the owner's control.

1. Ingest video, audio, transcripts and metadata.
2. Search inside video with plain-language queries.
3. Generate summaries and textual analyses from video content.
4. Build multimodal embeddings from video, audio, text and images.
5. Support large video libraries with scalable indexing.
6. Provide a free tier and an interactive playground.
7. Combine modalities into one embedding space for retrieval.
8. Retrieve fast-moving actions and long-form content.
9. Search natively across many languages.
10. Retrieve speech and non-speech audio.
11. Reduce storage footprint with an efficient embedding design.
12. Chunk videos into synchronized audio and visual segments with time stamps.
13. Connect to external AI models for flexible workflows.
14. Expose an API for existing AI workflows.
15. Produce content improvement insights from analyzed video.
16. Plan connectors for common video platforms.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned reviewed search results, summaries and analysis records linked to time-stamped evidence with source references and unresolved questions.

Everything these tools do, in one app

• Natural language video search Allows users to search inside video content using plain language queries.Found in TwelveLabs
• Video summarization and analysis Generates detailed summaries and textual analyses from video content.Found in TwelveLabs
• Multimodal embeddings Creates rich embeddings from video, audio, text, and images for tasks like classification and recommendation.Found in TwelveLabs, TwelveLabs Marengo 3.0
• Large-scale video library support Handles extensive video collections efficiently for scalable understanding.Found in TwelveLabs
• Free tier and playground Provides a free tier and an interactive playground to explore capabilities without commitment.Found in TwelveLabs
• Unified multimodal embeddings Combines video, audio, text, and images into a single embedding space for precise search and retrieval.Found in TwelveLabs Marengo 3.0
• Action-level retrieval Improves retrieval of fast-moving actions and long-form content.Found in TwelveLabs Marengo 3.0
• Multilingual search Enables native search across 36 languages for international datasets.Found in TwelveLabs Marengo 3.0
• Audio retrieval Covers both speech and non-speech audio retrieval for comprehensive analysis.Found in TwelveLabs Marengo 3.0
• Storage-efficient design Reduces storage footprint by 3-6× compared to comparable models, lowering infrastructure costs.Found in TwelveLabs Marengo 3.0
• Time-stamped chunking Transforms videos into synchronized audio and visual segments for accurate timing.Found in VMTP
• Integration with 100+ AI models Supports connectivity to over 100 AI models through Openrouter for flexible AI workflows.Found in VMTP
• API-based architecture Allows easy incorporation into existing AI workflows via API.Found in VMTP
• Content improvement insights Analyzes video content to provide insights for content improvement.Found in VMTP
• Video platform connectors Plans to support connectors for popular video platforms like YouTube and Vimeo.Found in VMTP

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed video, audio, transcripts and metadata

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed video

4. 3

   Audio

5. 4

   Transcripts and metadata

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed search results, summaries and analysis records linked to time-stamped evidence

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed embedding model and licensed media set; final editorial and rights checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Library and ingestion, Search and analysis workspace, Review and export. Use a thumbnail and transcript gallery for the video library, a large central player with a synchronized transcript and timeline, and a right-hand panel for filters, embeddings status, summaries and comments. Let users compare candidate moments side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant timecode. Make the task-specific outcome reviewed search results, summaries and analysis records linked to time-stamped evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Owner-authorized video libraries, transcripts and permitted research sources. Cloud asset storage, design-file import/export and publishing destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: ingest video, audio, transcripts and metadata; search inside video with plain-language queries. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will product and data teams running large video libraries that need search, summarization and analysis use it to solve "video content is hard to search, summarize and analyze at scale, and the work is spread across several rented tools"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted search results per analyst hour and correction rate after review.
4. Measure, then decide. Track accepted search results per analyst hour and correction rate after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One fixed embedding model and licensed media set; final editorial and rights checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: ingest video, audio, transcripts and metadata; search inside video with plain-language queries. Support the third module with operator review: generate summaries and textual analyses from video content. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed search results, summaries and analysis records linked to time-stamped evidence. Retain the explicit scope boundary: One fixed embedding model and licensed media set; final editorial and rights checks remain human.

What the build depends on. Asset upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist creative QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed embedding model and licensed media set; final editorial and rights checks remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined media package. Offer a monthly production allowance after repeat demand. Quote complex video, 3D or specialist design separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed search results, summaries and analysis records linked to time-stamped evidence. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce time to find and analyze relevant video moments while keeping the library and its embeddings under the owner's control. Demonstrate a concrete reviewed search results, summaries and analysis records linked to time-stamped evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product and data teams running large video libraries that need search, summarization and analysis professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed search results, summaries and analysis records linked to time-stamped evidence from a small authorized input set, with a transparent calculation of accepted search results per analyst hour and correction rate after review and no promised savings.

The first 30 days

1. Week 1: interview five product and data teams running large video libraries that need search, summarization and analysis and inspect a recent example of video content is hard to search, summarize and analyze at scale, and the work is spread across several rented tools.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted search results per analyst hour and correction rate after review, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted search results per analyst hour and correction rate after review. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted search results per analyst hour and correction rate after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed search results, summaries and analysis records linked to time-stamped evidence. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved search patterns, embedding configurations and review examples, together with reliable delivery for a narrow media niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product and data teams running large video libraries that need search, summarization and analysis. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

TwelveLabs, TwelveLabs Marengo 3.0, VMTP, freelancers, creative agencies and generic generation tools. Compare this product with the buyer's present method on accepted search results per analyst hour and correction rate after review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, video or image processing, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed search results, summaries and analysis records linked to time-stamped evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Owners approve substantive changes and publication scope. One fixed embedding model and licensed media set; final editorial and rights checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.