Private multi-source memory library and search console

For

Individuals and small teams who capture information from many sources and need to find it later by meaning

Solves

Saved notes, files, messages and web pages are scattered across tools and cannot be searched by what they mean, so useful information is lost or re-found by hand.

Delivers

A searchable, source-linked memory library

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Reduce time spent re-finding saved information while keeping the library under the owner's control.

1. Capture notes, photos, voice memos, documents, screenshots, web pages, bookmarks and messages into one library.
2. Search saved items by describing what you remember instead of exact keywords.
3. Keep data on the owner's device or personal cloud with local-first storage.
4. Generate summaries of notes, meetings and daily entries.
5. Group and arrange items automatically without manual folders or tags.
6. Enrich saved items with permitted public context such as company details or social profiles.
7. Search the library without an internet connection.
8. Turn saved items into tasks in a reminders app.
9. Import and search saved social posts and threads alongside other items.
10. Connect permitted tools such as messaging apps, social platforms, note apps and browser extensions.
11. Run the software on the owner's own infrastructure for full control.
12. Provide inspectable and modifiable source code.
13. Draft or refine text based on the saved library.
14. Explore connected concepts on a knowledge canvas.
15. Clip readable content from web pages automatically.
16. Generate meeting recaps and highlights.
17. Jot notes instantly with a keystroke or tray icon.
18. Run language models on the owner's machine for private processing.
19. Process uploaded files with OCR, chunking and embedding for search.
20. Save corrections so future answers reflect updated information.
21. Share a collective knowledge base with a team.
22. Cite source documents in answers for traceability.
23. Ask questions in a chat interface to find specific items.
24. Categorize items with tags for easier retrieval.
25. Store conversational context across different AI chat clients.
26. Detect and merge similar items to reduce clutter.
27. Compare the reviewed result with the recorded baseline and value assumptions.
28. Capture corrections and named-owner approval before consequential use.
29. Export a versioned, source-linked memory library with references and unresolved questions.

Everything these tools do, in one app

• Multi-source capture Save notes, photos, voice memos, documents, screenshots, web pages, bookmarks, and messages into one place.Found in Dump Memory, Supermemory, remio 2.0 and 4 more
• Semantic search Find saved items by describing what you remember instead of matching exact keywords.Found in Dump Memory, Supermemory, remio 2.0 and 4 more
• Local-first storage Keep your data on your own device or personal cloud so the company cannot read it.Found in Dump Memory, remio 2.0, Lore and 1 more
• AI summarization Generate summaries of your notes, meetings, or daily entries.Found in remio 2.0, PowerNote
• Automatic organization Arrange and group notes without manual folders or tags.Found in Dump Memory, PowerNote
• Web enrichment Automatically add context like company details or social profiles to saved items.Found in Dump Memory
• Offline search Search your saved items without an internet connection.Found in Dump Memory, Lore
• Task creation Turn saved memories into tasks in a reminders app.Found in Dump Memory
• Bookmark sync Import and search saved social media posts and threads alongside other items.Found in Dump Memory
• App integrations Connect with tools like Telegram, Twitter, Notion, and browser extensions.Found in Supermemory, remio 2.0
• Self-hosting Run the software on your own infrastructure for full control.Found in Supermemory, Second Brain for AI
• Open source Inspect and modify the code, often for free.Found in Supermemory, Lore, Second Brain for AI
• Writing assistant Get help drafting or refining text based on your saved knowledge.Found in Supermemory
• Knowledge canvas Visually connect and explore concepts from your saved information.Found in Supermemory
• Auto web clipping Automatically capture readable content from web pages you visit.Found in remio 2.0
• Meeting recap Generate summaries and highlights from meeting content.Found in remio 2.0
• Quick capture Jot down notes or ideas instantly with a keystroke or from a tray icon.Found in Lore
• Local LLM Run language models on your own machine for private processing.Found in Lore
• Document ingestion Process uploaded files with OCR, chunking, and embedding for search.Found in Manex
• Correction memory Save corrections so future answers reflect updated information.Found in Manex
• Team workspaces Share a collective knowledge base with your team.Found in Manex
• Grounded answers Get responses that cite the source documents for traceability.Found in Manex
• Chat-based search Ask questions in a chat interface to find specific notes or topics.Found in PowerNote
• Tagging Categorize notes with tags for easier retrieval.Found in PowerNote
• Persistent AI memory Store conversational context across different AI chat clients.Found in Second Brain for AI
• Duplicate detection Identify and merge similar notes to reduce clutter.Found in Second Brain for AI

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted notes, photos, voice memos, documents, screenshots, web pages, bookmarks and messages

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted notes

4. 3

   Photos

5. 4

   Voice memos

6. 5

   Documents

7. 6

   Screenshots

8. 7

   Web pages

9. 8

   Bookmarks and messages

10. 9

    Then follow this sequence: 1

11. Out
    Finish with

    A searchable, source-linked memory library

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 approved capture format and a bounded representative case set; final accuracy and permission checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Capture inbox, Search and answer view, Library and stewardship console. Use a thumbnail or list gallery for items, a large central reading and answer pane, and a right-hand panel for sources, tags, collections and review state. Let users compare candidate answers side by side. Display captured, processed, needs review and approved states. Provide a shareable read-only link for a collection with citations anchored to the relevant item. Make the task-specific outcome a searchable, source-linked memory library visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, item 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 notes, files, messages and permitted research sources. Cloud or local 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: capture notes, photos, voice memos, documents, screenshots, web pages, bookmarks and messages into one library; search saved items by describing what you remember instead of exact keywords. 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 individuals and small teams who capture information from many sources and need to find it later by meaning use it to solve "saved notes, files, messages and web pages are scattered across tools and cannot be searched by what they mean, so useful information is lost or re-found by hand"?
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 known item and share of searches answered with a correct cited source.
4. Measure, then decide. Track time to retrieve a known item and share of searches answered with a correct cited source; 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 approved capture format and a bounded representative case set; final accuracy and permission checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: capture notes, photos, voice memos, documents, screenshots, web pages, bookmarks and messages into one library; search saved items by describing what you remember instead of exact keywords. Support the third module with operator review: keep data on the owner's device or personal cloud with local-first storage. 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 memory library. Retain the explicit scope boundary: One approved capture format and a bounded representative case set; final accuracy and permission checks remain human.

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 knowledge-management QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved capture format and a bounded representative case set; final accuracy and permission 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 capture package. Offer a monthly production allowance after repeat demand. Quote complex multi-source or team deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded searchable, source-linked memory library. 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 saved information while keeping the library under the owner's control. Demonstrate a concrete searchable, source-linked memory library using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Individuals and small teams who capture information from many sources and need to find it later by meaning 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 memory library from a small authorized input set, with a transparent calculation of time to retrieve a known item and share of searches answered with a correct cited source and no promised savings.

The first 30 days

1. Week 1: interview five individuals and small teams who capture information from many sources and need to find it later by meaning and inspect a recent example of saved notes, files, messages and web pages scattered across tools and cannot be searched by what they mean.
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 time to retrieve a known item and share of searches answered with a correct cited source, 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 known item and share of searches answered with a correct cited source. 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 known item and share of searches answered with a correct cited source; 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 memory library. 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 capture formats, retrieval examples and review corrections, together with reliable delivery for a narrow knowledge-management niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for individuals and small teams who capture information from many sources and need to find it later by meaning. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Dump Memory, Supermemory, remio 2.0, Lore, Manex, PowerNote and Second Brain for AI. Compare this product with the buyer's present method on time to retrieve a known item and share of searches answered with a correct cited source. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Capture attempts, storage, processing, 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 memory library. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve owner voice, source attribution, quotation accuracy and usage permissions. Owners approve substantive changes and publication scope. One approved capture format and a bounded representative case set; final accuracy and permission checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.