Cross-tool AI conversation memory library

For

Developers, consultants and small teams who use several AI chat tools and lose context between them

Solves

Context and preferences from past AI chats stay trapped in each tool, so users repeat themselves and lose useful decisions.

Delivers

Searchable, permissioned memory entries

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

Keep useful context from AI chats available across different AI tools so users stop repeating themselves.

1. Capture conversations from supported AI chat tools automatically.
2. Store captured data locally with encryption by default.
3. Index entries for search across notes, chats and imported files.
4. Inject selected context into a new chat with one action.
5. Trigger in-chat retrieval with a shortcut such as @memory.
6. Manage entries in a central dashboard with edit and delete.
7. Apply scoped retrieval, decay and versioning metadata.
8. Save reusable snippets for brand voice, client notes or project rules.
9. Balance recent turns, summaries and vectorized long-term context.
10. Explain or draft on any page using stored knowledge.
11. Archive social posts into the same searchable store.
12. Index by project and episodic summary rather than whole conversations.
13. Listen continuously and split conversations by activity and speaker.
14. Connect agents through MCP to pull historical conversations.
15. Run on phone, tablet and watch without extra hardware.
16. Transcribe audio then discard it, keeping only text.
17. Export conversations to Markdown, HTML, JSON or plain text.
18. Save conversations to Notion with one click.
19. Preserve Canvas, Artifacts, diagrams and citations in exports.
20. Import and export data including Google Takeout and ChatGPT logs.
21. Run embeddings and indexing in the browser for local recall.
22. Tag and categorize chats for organization.
23. Edit notes in WYSIWYG form with formulas and toggled sections.
24. Play back saved conversations as audio.
25. Keep saved content available offline.
26. Back up and restore local storage.

Everything these tools do, in one app

• Cross-platform memory sync Keeps your context and preferences available across multiple AI chat platforms so you don't repeat yourself.Found in OpenMemory Chrome Extension, CogniMemo Extension, AI Context Flow and 3 more
• Automatic conversation capture Passively records your AI chats in the background without changing your workflow.Found in Memdex, Personal AI Memory, Draft
• Local encrypted storage Stores your conversation data on your own device with encryption instead of on external servers.Found in Memdex, Personal AI Memory, Draft
• Searchable memory store Lets you search your saved notes, past chats, and other content to find relevant context.Found in CogniMemo Extension, Personal AI Memory, ChatGPT Saved Chats and 1 more
• One-click context injection Inserts saved context into a new AI chat with a single action.Found in AI Context Flow, Memdex
• In-chat retrieval trigger Lets you search your saved memory from inside a chat using a shortcut like @cogni.Found in CogniMemo Extension
• Memory management dashboard Provides a central place to view, edit, and manage your stored memories.Found in OpenMemory Chrome Extension
• Memory hygiene controls Offers scoped retrieval, decay and versioning metadata, and edit/delete/audit options for entries.Found in CogniMemo Extension
• Reusable context snippets Stores project context like brand voice or client notes once and reapplies it across chats.Found in AI Context Flow
• Multi-tier memory architecture Balances short-term recent turns, mid-term summaries, and longer-term vectorized context for retrieval.Found in AI Context Flow
• Floating on-page assistant Explains or writes using your stored knowledge on any website.Found in CogniMemo Extension
• Social post archiving Syncs and archives social posts like X/Twitter into your searchable memory store.Found in CogniMemo Extension
• End-to-end encryption Protects your data with enterprise-grade encryption.Found in MemSync
• Project-oriented indexing Uses project indexing and episodic summaries to surface the most relevant sections rather than entire conversations.Found in Memdex
• Continuous background listening Listens passively for up to 24 hours with one tap and separates conversations by activity.Found in Hemory
• Speaker labels Auto-splits your day into moments and applies speaker labels to each conversation.Found in Hemory
• MCP connectivity for agents Connects to AI agents like Claude, Codex, Cursor, or OpenClaw so they can pull historical conversations as context.Found in Hemory
• Multi-device support Runs on iPhone, Android, and Apple Watch without extra hardware.Found in Hemory
• Audio transcription and discard Transcribes original audio then discards it, keeping only text memory in the cloud.Found in Hemory
• Chat export to files Exports conversations in Markdown, HTML, JSON, or Plain Text.Found in YourAIScroll
• Notion integration Saves AI conversations directly to Notion with one click.Found in YourAIScroll
• Preserves rich content types Keeps Canvas, Artifacts, Diagrams, and Web Search Citations intact in exports.Found in YourAIScroll
• Import/export conversation data Supports importing and exporting conversation data including Google Takeout and ChatGPT logs.Found in Personal AI Memory
• In-browser embeddings and indexing Enables local search and recall without cloud APIs by running embeddings in the browser.Found in Personal AI Memory
• Chat tagging and categorization Lets you categorize or tag chats for better organization.Found in ChatGPT Saved Chats
• Editable WYSIWYG notes Preserves formatting including formulas and toggled sections in editable notes.Found in Draft
• Text-to-speech playback Plays back saved conversations as audio.Found in Draft
• Offline access Keeps saved content available offline for review.Found in Draft
• Backup and restore Provides local storage with backup and restore options.Found in Draft

What goes in, what comes out

The workflow

1. In
   Start with

   Authorized conversation exports, project notes and preference lists

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect authorized conversation exports

4. 3

   Project notes and preference lists

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Searchable, permissioned memory entries

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs 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. Final decisions about what context to inject and what to share remain with the user. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Memory library and search, Entry detail and edit, Context injection and settings. Use a searchable list with filters for source tool, project, tag and date, a large central entry view with version history, and a right-hand panel for scope, permissions and linked conversations. Let users compare entry versions side by side. Display captured, reviewed, scoped and archived states. Provide a client or teammate preview link with comments anchored to the relevant entry. Make the task-specific outcome searchable, permissioned memory entries visible beside their evidence, review state and value baseline.

Accounts and administration

Project ownership, entry versions, teammate comments, approval states, usage allowances, retention limits, export 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

Authorized conversation exports, project notes and preference lists. 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: capture conversations from supported AI chat tools automatically; store captured data locally with encryption by default. 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

   2 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 developers, consultants and small teams who use several AI chat tools and lose context between them use it to solve "context and preferences from past AI chats stay trapped in each tool, so users repeat themselves and lose useful decisions"?
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: Repeated-context minutes saved per task and accepted context injections per week.
4. Measure, then decide. Track repeated-context minutes saved per task and accepted context injections per week; 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 supported chat tool and one browser; local encrypted storage by default; final decisions about what context to inject and what to share remain with the user. Implement one approved input format, a bounded representative case set and the first two task modules: capture conversations from supported AI chat tools automatically; store captured data locally with encryption by default. Support the third module with operator review: index entries for search across notes, chats and imported files. 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 searchable, permissioned memory entries. Retain the explicit scope boundary: One supported chat tool and one browser; local encrypted storage by default; final decisions about what context to inject and what to share remain with the user.

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 supported chat tool and one browser; local encrypted storage by default; final decisions about what context to inject and what to share remain with the user.

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 memory package. Offer a monthly production allowance after repeat demand. Quote complex multi-tool or agent integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded searchable, permissioned memory entries. 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

Keep useful context from AI chats available across different AI tools so users stop repeating themselves. Demonstrate a concrete searchable, permissioned memory entries using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Developers, consultants and small teams who use several AI chat tools and lose context between them professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample searchable, permissioned memory entries from a small authorized input set, with a transparent calculation of repeated-context minutes saved per task and accepted context injections per week and no promised savings.

The first 30 days

1. Week 1: interview five developers, consultants and small teams who use several AI chat tools and lose context between them and inspect a recent example of context and preferences from past AI chats stay trapped in each tool, so users repeat themselves and lose useful decisions.
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 repeated-context minutes saved per task and accepted context injections per week, 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: Repeated-context minutes saved per task and accepted context injections per week. 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

Repeated-context minutes saved per task and accepted context injections per week; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs searchable, permissioned memory entries. 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 memory schemas, project constraints and review examples, together with reliable delivery for a narrow technical niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for developers, consultants and small teams who use several AI chat tools and lose context between them. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

OpenMemory Chrome Extension, CogniMemo Extension, AI Context Flow, MemSync, Memdex, Hemory, YourAIScroll, Personal AI Memory, ChatGPT Saved Chats and Draft. Compare this product with the buyer's present method on repeated-context minutes saved per task and accepted context injections per week. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Capture attempts, transcription or embedding 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 searchable, permissioned memory entries. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve user voice, source attribution, quotation accuracy and usage permissions. Users approve substantive changes and sharing scope. One supported chat tool and one browser; local encrypted storage by default; final decisions about what context to inject and what to share remain with the user. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.