Private knowledge capture and search workbench

For

Solicitors, barristers and in-house legal teams handling confidential client documents

Solves

Client and matter knowledge sits in scattered files, inboxes and web pages, and public AI tools cannot be used on privileged material.

Delivers

Cited, matter-isolated search results and source-checked drafts

Built in

about 5 weeks of creation time, MVP in 6 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 finding and checking matter material while keeping privileged documents inside the firm's own environment.

1. Run AI processing on the firm's own device or private infrastructure.
2. Keep documents private by processing offline and avoiding training on client material.
3. Capture files, URLs, images, notes and ideas into matter collections.
4. Search by meaning across captured material.
5. Auto-categorise and enrich captured items for later retrieval.
6. Show the exact source location for every retrieved passage.
7. Surface relevant saved material automatically while drafting.
8. Flag claims in a draft that the library does not support.
9. Link meeting topics to existing saved material without a separate bot.
10. Isolate each client or matter in its own collection.
11. Draft, summarise and complete text from the firm's own material and style.
12. Produce a consolidated context document for AI assistants.
13. Scrape and analyse content from multiple web pages starting from one URL.
14. Export documents for assistants that accept uploads, such as ChatGPT and Claude.
15. Benchmark several language models on the firm's own evaluation set and report results.
16. Export search and evaluation results for reporting.
17. Suggest queries as the user types.
18. Handle queries and content in multiple languages.
19. Connect to common applications, browsers and productivity tools.
20. Capture and retrieve knowledge through a chat app interface.

Everything these tools do, in one app

• Local or private deployment Runs the AI processing on your own device or private infrastructure instead of the public cloud.Found in ThirdAI PocketLLM, Docu Dig, Remio
• Privacy-focused data handling Keeps your data private by processing it offline or avoiding long-term storage and training on it.Found in ThirdAI PocketLLM, Docu Dig, Remio and 1 more
• Capture documents and content Lets you add files, URLs, images, notes, and ideas into a personal or team knowledge base.Found in Remio, Keepi, THEO
• Context-aware search Understands the meaning of your query to return precise, relevant results from your documents.Found in Docu Dig, Search Copilot, Remio and 1 more
• Automatic organization Enriches and categorizes captured information so it is easy to find later.Found in Remio, Keepi
• Source citations and references Shows the exact location in the original document where retrieved information came from.Found in Docu Dig, Liminary
• Proactive context recall Automatically surfaces relevant saved material as you work without you having to search for it.Found in Liminary
• Fact-checking against sources Compares claims in your work against your saved library and flags gaps or unsupported statements.Found in Liminary
• Live meeting recall Links topics discussed in meetings to your existing saved material without a separate note-taking bot.Found in Liminary
• Collections for isolation Groups sources into separate collections so different projects or clients stay isolated.Found in Liminary
• AI writing assistance Helps draft, summarize, and complete text based on your own knowledge and writing style.Found in Remio
• Structured knowledge base output Produces a consolidated document of instructions and context that AI assistants can use.Found in THEO
• URL scraping and analysis Automatically pulls and analyzes content from multiple web pages starting from one URL.Found in THEO
• AI assistant compatibility Works with popular AI assistants that accept document uploads, such as ChatGPT and Claude.Found in THEO
• Language model benchmarking Tests and compares multiple language models across performance metrics and produces reports.Found in RLAMA
• Result export Lets you export evaluation or search results for further analysis and reporting.Found in RLAMA
• Real-time query suggestions Offers suggestions as you type to refine your search and improve results.Found in Search Copilot
• Multi-language support Handles queries and content in multiple languages for broader accessibility.Found in Search Copilot
• App and browser integration Connects with common applications, browsers, and productivity tools to fit existing workflows.Found in Search Copilot, Docu Dig, Liminary
• Messaging app interface Lets you capture and retrieve knowledge through a familiar chat app like WhatsApp.Found in Keepi

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted client and matter files, URLs, images, notes and firm style examples

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted files

4. 3

   URLs

5. 4

   Images and notes

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Cited, matter-isolated search results and source-checked drafts

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. One private deployment per firm and one approved document set; final legal advice, privilege calls and filing decisions remain with the qualified lawyer. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Matter library and capture, Search and answer with citations, Draft and source-check, Stewardship console. Use a collection list for matters, a central search and reading pane, and a right-hand panel for citations, related saved material and review state. Let users compare a draft against its cited sources side by side. Display captured, indexed, reviewed and approved states. Provide a client-safe export link with citations anchored to the source passage. Make the task-specific outcome cited, matter-isolated search results and source-checked drafts visible beside its evidence, review state and value baseline.

Accounts and administration

Matter ownership, collection boundaries, document versions, reviewer comments, approval states, retention rules, model benchmark records, 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

Firm document management systems, email, browsers and permitted web sources. Cloud or on-premise storage, document import/export and assistant upload formats. Start with file exchange and validate destination specifications before promising direct filing or court-system integration. 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: run AI processing on the firm's own device or private infrastructure; keep documents private by processing offline and avoiding training on client material; capture files, URLs, images, notes and ideas into matter collections; search by meaning across captured material. 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 solicitors, barristers and in-house legal teams handling confidential client documents use it to solve "client and matter knowledge sits in scattered files, inboxes and web pages, and public AI tools cannot be used on privileged material"?
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: Search time per matter query and unsupported statements caught before filing.
4. Measure, then decide. Track search time per matter query and unsupported statements caught before filing; 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 private deployment for one firm and one approved document set; final legal advice, privilege calls and filing decisions remain with the qualified lawyer. Implement one approved input format, a bounded representative matter set and the first four task modules: run AI processing on the firm's own device or private infrastructure; keep documents private by processing offline and avoiding training on client material; capture files, URLs, images, notes and ideas into matter collections; search by meaning across captured material. Support the remaining modules with operator review: show the exact source location for every retrieved passage; flag claims in a draft that the library does not support. 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 matter volume only after new evaluation cases pass. Build reusable firm configurations and recurring value reports around cited, matter-isolated search results and source-checked drafts. Retain the explicit scope boundary: One private deployment per firm and one approved document set; final legal advice, privilege calls and filing decisions remain with the qualified lawyer.

What the build depends on. Document upload and preview, asynchronous indexing jobs, editable version history, reviewer access and tested export formats. High-fidelity legal use requires qualified legal review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One private deployment per firm and one approved document set; final legal advice, privilege calls and filing decisions remain with the qualified lawyer.

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 matter package. Offer a monthly production allowance after repeat demand. Quote complex multi-office or specialist integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded cited, matter-isolated search results and source-checked drafts. 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 finding and checking matter material while keeping privileged documents inside the firm's own environment. Demonstrate a concrete cited, matter-isolated search results and source-checked drafts using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Solicitors, barristers and in-house legal teams handling confidential client documents professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample cited, matter-isolated search results and source-checked drafts from a small authorized input set, with a transparent calculation of search time per matter query and unsupported statements caught before filing and no promised savings.

The first 30 days

1. Week 1: interview five solicitors, barristers and in-house legal teams handling confidential client documents and inspect a recent example of client and matter knowledge sitting in scattered files, inboxes and web pages, and public AI tools being unusable on privileged material.
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 search time per matter query and unsupported statements caught before filing, 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: Search time per matter query and unsupported statements caught before filing. 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

Search time per matter query and unsupported statements caught before filing; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs cited, matter-isolated search results and source-checked drafts. 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 matter collections, firm style examples and reviewer corrections, together with reliable private deployment for a narrow legal niche. Build a permissioned library of representative matter cases, reviewer corrections and verified operating constraints for solicitors, barristers and in-house legal teams handling confidential client documents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

ThirdAI PocketLLM, Docu Dig, RLAMA, Remio, Search Copilot, Liminary, Keepi and THEO, plus manual folder search and public AI assistants. Compare this product with the buyer's present method on search time per matter query and unsupported statements caught before filing. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model inference, private hosting, 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 cited, matter-isolated search results and source-checked drafts. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve privilege, source attribution, quotation accuracy and usage permissions. Qualified lawyers approve substantive advice and filing scope. One private deployment per firm and one approved document set; final legal advice, privilege calls and filing decisions remain with the qualified lawyer. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.