Searchable research library and data stewardship console

For

Research teams and knowledge workers who save material from many sources and need to retrieve, summarize and question it later

Solves

Saved articles, papers, videos, notes and datasets sit in disconnected tools, so teams cannot search across them, trace where a claim came from, or reuse prior work reliably.

Delivers

A searchable, source-linked library with reviewed summaries and answers

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Reduce time spent re-finding and re-reading saved material while keeping source provenance and review state attached.

1. Save links, articles, videos, PDFs, notes and images in one library.
2. Summarize long articles, videos, podcasts and documents into key points.
3. Answer questions using only the user's saved material.
4. Search by plain-language description instead of exact keywords.
5. Search across connected apps, drives and accounts from one query.
6. Import from Gmail, Slack, Notion, Obsidian, bookmarks and similar services.
7. Transcribe video and audio and extract timestamps and frame descriptions.
8. Link related concepts and items into a browsable knowledge graph.
9. Arrange saved items on a visual board or wall.
10. Group items with tags and project collections.
11. Schedule spaced reviews of saved material.
12. Surface relevant saved items proactively when they may be useful.
13. Send scheduled digest summaries of past saves.
14. Present summaries as overviews, tables, mindmaps or timelines.
15. Turn saved content into briefs, clips, watchlists or plans.
16. Sync saved content across phones, tablets, computers and the web.
17. Save web pages through a browser extension button.
18. Let authorized external AI agents query the library through a documented interface.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned, source-linked library view with references and unresolved questions.

Everything these tools do, in one app

• Save diverse content Lets users store links, articles, videos, PDFs, notes, images, and other items in one place.Found in Recall, SaveDay, Findr: remember everything and 7 more
• Summarize saved content Condenses long articles, videos, podcasts, or documents into short key points.Found in Recall, SaveDay, Corgi AI and 2 more
• Chat with saved content Lets users ask questions and get answers drawn from their own saved material.Found in Recall, SaveDay, Corgi AI and 4 more
• Natural language search Finds saved items by describing them in plain words instead of exact keywords or titles.Found in SaveDay, Findr: remember everything, Briefy and 4 more
• Unified search across sources Searches across multiple connected apps, drives, or accounts from one query.Found in Findr: remember everything, Deepmark, Finden
• Import from other apps Pulls in content from external services such as Gmail, Slack, Linear, Notion, Obsidian, or browser bookmarks.Found in Findr: remember everything, Cubox AI 3.0, Remem AI and 1 more
• Video and audio processing Reads videos and audio by transcribing speech, extracting timestamps, or describing frames.Found in Recall, Corgi AI, Deepmark
• Knowledge graph linking Automatically connects related concepts or memories so related items appear together.Found in Recall, Remem AI
• Visual board organization Shows saved content on a visual canvas or wall that users can arrange and browse.Found in Findr: remember everything, Second Brain
• Tags and projects Groups saved items with tags or project folders for easier retrieval.Found in Second Brain
• Spaced repetition Schedules reviews of saved material to help users remember it long term.Found in Recall
• Proactive reminders Surfaces relevant saved information on its own when it may be useful.Found in Findr: remember everything
• Scheduled digests Sends regular summary emails to help users revisit past saves.Found in Cubox AI 3.0
• Multiple summary views Presents summaries as overviews, tables, mindmaps, or timelines to suit different preferences.Found in Briefy
• Action-ready outputs Turns saved content into briefs, clips, watchlists, or plans users can act on.Found in Corgi AI
• Cross-device sync Keeps saved content available across phones, tablets, computers, and the web.Found in Cubox AI 3.0, Briefy
• Browser extension saving Adds a browser button to quickly save web pages into the tool.Found in Second Brain
• AI agent access Lets external AI agents query the user's saved library directly.Found in Deepmark

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted links, articles, videos, PDFs, notes, images and connected-app items

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted links

4. 3

   Articles

5. 4

   Videos

6. 5

   PDFs

7. 6

   Notes

8. 7

   Images and connected-app items

9. 8

   Then follow this sequence: 1

10. Out
    Finish with

    A searchable, source-linked library with reviewed summaries and answers

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate summaries, answers and links for the 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 input set and permitted source list; final accuracy, citation and interpretation checks remain with the researcher. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Library intake and sources, Search and ask workspace, Item and collection review. Use a thumbnail and list gallery for saved items, a large central reading and question canvas, and a right-hand panel for tags, collections, provenance and comments. Let users compare summaries against the source text side by side. Display draft, needs review, verified and archived states. Provide a shared collection link with comments anchored to the relevant item or passage. Make the task-specific outcome a searchable, source-linked library with reviewed summaries and answers visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, shared collection comments, approval states, usage allowances, import 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

Author-owned documents, permitted research sources and connected apps such as Gmail, Slack, Notion, Obsidian and browser bookmarks. Cloud storage, 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: save links, articles, videos, PDFs, notes and images in one library; summarize long articles, videos, podcasts and documents into key points. 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 research teams and knowledge workers who save material from many sources and need to retrieve, summarize and question it later use it to solve "saved articles, papers, videos, notes and datasets sit in disconnected tools, so teams cannot search across them, trace where a claim came from, or reuse prior work reliably"?
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: Time to retrieve a verified source and accepted answers per review hour.
4. Measure, then decide. Track time to retrieve a verified source and accepted answers per review hour; 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 input set and permitted source list; final accuracy, citation and interpretation checks remain with the researcher. Implement one approved import format, a bounded representative case set and the first two task modules: save links, articles, videos, PDFs, notes and images in one library; summarize long articles, videos, podcasts and documents into key points. Support the remaining modules with operator review: answer questions using only the user's saved material; search by plain-language description; search across connected apps, drives and accounts. 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 the searchable, source-linked library with reviewed summaries and answers. Retain the explicit scope boundary: One fixed input set and permitted source list; final accuracy, citation and interpretation checks remain with the researcher.

What the build depends on. Asset upload and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity retrieval requires specialist research QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed input set and permitted source list; final accuracy, citation and interpretation checks remain with the researcher.

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 library package. Offer a monthly production allowance after repeat demand. Quote complex video, audio or specialist source work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded searchable, source-linked library with reviewed summaries and answers. 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 spent re-finding and re-reading saved material while keeping source provenance and review state attached. Demonstrate a concrete searchable, source-linked library with reviewed summaries and answers using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Research teams and knowledge workers professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample searchable, source-linked library with reviewed summaries and answers from a small authorized input set, with a transparent calculation of time to retrieve a verified source and accepted answers per review hour and no promised savings.

The first 30 days

1. Week 1: interview five research teams and knowledge workers who save material from many sources and inspect a recent example of saved articles, papers, videos, notes and datasets sitting in disconnected tools.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure time to retrieve a verified source and accepted answers per review hour, 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: Time to retrieve a verified source and accepted answers per review hour. 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

Time to retrieve a verified source and accepted answers per review hour; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a searchable, source-linked library with reviewed summaries and answers. 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 source types, import mappings and review examples, together with reliable delivery for a narrow research niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for research teams and knowledge workers who save material from many sources. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Recall, SaveDay, Findr: remember everything, Corgi AI, Briefy, Second Brain, Cubox AI 3.0, Remem AI, Deepmark and Finden are what buyers use today, each covering part of the job. Compare this product with the buyer's present method on time to retrieve a verified source and accepted answers per review hour. Offer one owned, source-linked library instead of renting several subscriptions, so the buyer keeps the data, the workflow and the brand. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Transcription and processing attempts, storage, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of the searchable, source-linked library with reviewed summaries and answers. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Researchers approve substantive interpretations and publication scope. One fixed input set and permitted source list; final accuracy, citation and interpretation checks remain with the researcher. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.