Local work memory and recall console

For

Knowledge workers and small technical teams who need a searchable record of their own work activity

Solves

Work activity is scattered across screens, tabs, chats, email and meetings, so people cannot find what they did earlier or answer questions about it.

Delivers

A searchable, user-approved work history with reminders and agent-accessible context

Built in

about 4 weeks of creation time, MVP in 4 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

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

Reduce time spent reconstructing past work while keeping captured data on the user's own device.

1. Capture work activity across screens, tabs, chats, email and meetings without manual logging.
2. Store captured data locally on the user's device.
3. Transcribe meeting speech on-device.
4. Index past screens, transcripts and activity for search.
5. Answer natural language questions over the user's history.
6. Expose captured context to AI agents through MCP or a command-line interface.
7. Generate automatic daily summaries.
8. Apply configurable retention rules such as 7, 30 or 90 days or indefinite.
9. Encrypt stored data with keys held in the device keychain.
10. Restrict AI requests to providers that do not retain data.
11. Hold anything addressed to another person for explicit user approval before sending.
12. Work as a model-agnostic memory layer.
13. Schedule reminders with flexible intervals and timely notifications.
14. Send automated follow-up alerts for missed tasks and deadlines.
15. Synchronize reminders across the user's devices.
16. Connect to calendars and communication apps.
17. Process notes, voice memos, photos, emails and documents using transcription, OCR and parsing.
18. Let the user CC the assistant on email threads so it participates with full context.
19. Make calls and handle scheduling or rescheduling with human oversight.
20. Capture content quickly from mobile features such as the lock-screen camera.

Everything these tools do, in one app

• Automatic activity capture Records your work activity without manual logging, covering screens, tabs, chats, email, and meetings.Found in LUCI Desktop, Hansel, ShogunAI
• Local-first storage Keeps captured data on your own device rather than in a vendor cloud account.Found in LUCI Desktop, Hansel, ShogunAI
• Searchable work history Makes past screens, transcripts, and activity searchable so you can find what you did earlier.Found in LUCI Desktop, Hansel, ShogunAI
• AI Q&A over history Lets you ask natural language questions about your past work and get answers.Found in Hansel
• Agent-accessible context Exposes your captured context to AI agents through MCP or a command-line interface so they can query it.Found in LUCI Desktop, ShogunAI
• On-device transcription Converts meeting speech to searchable text without sending audio to cloud servers.Found in LUCI Desktop
• Automatic daily summaries Generates summaries of your captured activity each day.Found in LUCI Desktop
• Configurable retention rules Lets you auto-delete history after set periods like 7, 30, or 90 days, or keep it indefinitely.Found in LUCI Desktop
• Encrypted local data Encrypts stored data with keys kept in your device's keychain so it can't be read without them.Found in Hansel
• Zero-data-retention AI Sends AI requests only to providers that do not retain your data.Found in Hansel
• Approval-gated sending Stops anything addressed to another person and waits for your explicit approval before sending.Found in ShogunAI
• Model-agnostic memory Works as a memory layer regardless of which AI model you use.Found in ShogunAI
• Reminder scheduling Schedules reminders with flexible time intervals and sends timely notifications.Found in Remind AI
• Automated follow-up notifications Sends follow-up alerts to reduce missed tasks and deadlines.Found in Remind AI
• Multi-device synchronization Keeps reminders consistent across your different devices.Found in Remind AI
• Calendar and app integration Connects with popular calendars and communication apps.Found in Remind AI
• Multi-format input processing Analyzes notes, voice memos, photos, emails, and documents using transcription, OCR, and parsing.Found in Memno
• Email thread participation Lets you CC the assistant on email threads so it can participate with full context.Found in Memno
• Scheduling and calls Makes phone calls and handles scheduling or rescheduling tasks with human oversight.Found in Memno
• Mobile quick capture Captures content quickly from mobile features like the lock screen camera and other apps.Found in Memno

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted local activity capture, on-device transcription and user notes

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted local activity capture

4. 3

   On-device transcription and user notes

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   A searchable, user-approved work history with reminders and agent-accessible context

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. Capture scope, retention and agent access remain user-controlled; sending to other people and scheduling calls remain user-approved. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Capture and permissions, Searchable history, Review and reminders. Use a timeline and filter bar for captured activity, a large central search and answer panel, and a right-hand panel for retention rules, encryption status and agent access. Let users compare a question with its cited source items. Display captured, reviewed and approved states. Provide an export and deletion view with a full audit trail. Make the task-specific outcome a searchable, user-approved work history with reminders and agent-accessible context visible beside its evidence, review state and value baseline.

Accounts and administration

Device ownership, capture scope, retention periods, encryption key status, agent access tokens, approval states, export and deletion history and a rights record for captured material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

User-owned devices, authorized calendars and permitted communication apps. Local storage, design-file import/export and agent interfaces. 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

   4 days

   One buyer segment, one recurring use case; first modules: capture work activity across screens, tabs, chats, email and meetings without manual logging; store captured data locally on the user's device. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

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

4. 4

   Full product

   10 days

   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 knowledge workers and small technical teams who need a searchable record of their own work activity use it to solve "work activity is scattered across screens, tabs, chats, email and meetings, so people cannot find what they did earlier or answer questions about it"?
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 past work item and user-confirmed recall accuracy.
4. Measure, then decide. Track time to retrieve a past work item and user-confirmed recall accuracy; 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 operating system and one user device; capture scope, retention and agent access remain user-controlled. Implement one approved input format, a bounded representative case set and the first two task modules: capture work activity across screens, tabs, chats, email and meetings without manual logging; store captured data locally on the user's device. Support the third module with user review: index past screens, transcripts and activity for search. Include source references, corrections, basic device 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 a searchable, user-approved work history with reminders and agent-accessible context. Retain the explicit scope boundary: One operating system and one user device; capture scope, retention and agent access remain user-controlled.

What the build depends on. Device capture permissions, asynchronous indexing jobs, editable version history, user access and tested export formats. High-fidelity recall requires user review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One operating system and one user device; capture scope, retention and agent access remain user-controlled.

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 device and capture scope. Offer a monthly production allowance after repeat demand. Quote complex multi-device or team deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded searchable, user-approved work history with reminders and agent-accessible context. 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 reconstructing past work while keeping captured data on the user's own device. Demonstrate a concrete searchable, user-approved work history with reminders and agent-accessible context using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

A reviewed sample searchable, user-approved work history with reminders and agent-accessible context from a small authorized input set, with a transparent calculation of time to retrieve a past work item and user-confirmed recall accuracy and no promised savings.

The first 30 days

1. Week 1: interview five knowledge workers and small technical teams who need a searchable record of their own work activity and inspect a recent example of work activity scattered across screens, tabs, chats, email and meetings, so people cannot find what they did earlier or answer questions about it.
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 past work item and user-confirmed recall accuracy, 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 past work item and user-confirmed recall accuracy. 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 past work item and user-confirmed recall accuracy; 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, user-approved work history with reminders and agent-accessible context. 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 rules, retention configurations and review examples, together with reliable delivery for a narrow technical niche. Build a permissioned library of representative task cases, user corrections and verified operating constraints for knowledge workers and small technical teams who need a searchable record of their own work activity. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Remind AI, LUCI Desktop, Hansel, ShogunAI and Memno, plus manual note-taking and generic search tools. Compare this product with the buyer's present method on time to retrieve a past work item and user-confirmed recall accuracy. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Capture processing, on-device transcription, storage, reviewer hours, user revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of a searchable, user-approved work history with reminders and agent-accessible context. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve user privacy, source attribution, quotation accuracy and usage permissions. Users approve substantive changes and external sending scope. One operating system and one user device; capture scope, retention and agent access remain user-controlled. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.