
Legal file naming restoration desk
Recover findability without altering original evidence.
- For
- Small law practices
- Solves
- Inherited files cannot be reliably identified.
- Delivers
- Reviewer-approved file index
- Built in
- about 5 weeks of creation time, MVP in 6 days
- Investment
- $12,000 for the MVP, $41,000 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
For small law practices, turn authorized documents and firm naming rules into reviewer-approved file index.
- Extract document metadata.
- Suggest traceable names.
- Preserve original paths.
- Link proposed outputs to original source records.
- Capture reviewer corrections and approval.
- Export a versioned reviewer-approved file index.
What goes in, what comes out
- Authorized documents
- Firm naming rules
AI drafts, people review. Searchable structured library and data stewardship console.
- Reviewer-approved file index
How it works
The workflow
- InStart with
Authorized documents and firm naming rules
- 1
The buyer creates a project
- 2
Supplies authorized documents and firm naming rules
- 3
Confirms scope and access
- OutFinish with
Reviewer-approved file index
AI does the heavy lifting, people stay in charge
AI assists these bounded tasks: extract document metadata; suggest traceable names; preserve original paths. Use only authorized documents and firm naming rules and preserve uncertainty in reviewer-approved file index. Keep model suggestions separate from verified facts. Link factual outputs to authorized input evidence and show missing information explicitly. Use deterministic checks for counts, dates, identifiers and arithmetic where applicable. A designated reviewer validates consequential outputs and signs off the delivered result.
What your team sees
Key screens: Brief and sources, Legal file naming restoration desk, Review and delivery. Use a searchable table or visual gallery with filters for the domain’s important attributes. Open each item into a detail drawer containing source records, ownership and history. Put proposed merges and field changes in a separate review queue. Provide a preview before any bulk export. Open with brief and sources; move into legal file naming restoration desk for the detailed task; finish in review and delivery for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.
Accounts and administration
Record ownership, access permissions, change proposals, original-value retention, version history, review dates, bulk import/export and duplicate resolution. Include organization-scoped access, named project owners, review queues, usage limits, export history and retention settings. Never reuse private customer material for other accounts without permission.
Integrations and data access
Authorized matter files, firm templates and approved legal knowledge collections. Source systems, catalog exports and cloud file storage. Start with reversible CSV or file imports and validate identifiers before any direct writes. Begin with uploads and exports of authorized documents and firm naming rules. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.
How we build it
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
Scoping call
Day 1Thirty 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
MVP
6 daysOne buyer segment, one recurring use case; first modules: extract document metadata; suggest traceable names. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
7 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
3 weeksSelf-serve onboarding, billing, monitoring and the wider integration set.
- 5
Run and improve
MonthlyWe 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.
- Pick the riskiest assumption. Here: will small law practices use it to solve "inherited files cannot be reliably identified"?
- Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
- Run a paid pilot. Agree the acceptance criteria, input limits and reviewer responsibilities before starting.
- Measure, then decide. Track unidentifiable matter files; reviewer correction minutes; buyer acceptance and repeat purchase. Then expand, change course or stop, with evidence instead of opinions.
MVP scope for this solution. Costed pilot: One organization, one defined input format and one representative pilot batch using authorized documents and firm naming rules. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: extract document metadata; suggest traceable names. Support the third task through an assisted review queue: preserve original paths. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of reviewer-approved file index. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.
After the MVP. After paying customers repeatedly accept reviewer-approved file index, automate link proposed outputs to original source records; capture reviewer corrections and approval; export a versioned reviewer-approved file index. Add one tested read integration, reusable customer configuration and scheduled repeat delivery. Increase supported formats or teams only when evaluation cases and reviewer capacity cover the new scope. One organization, one defined input format and one representative pilot batch using authorized documents and firm naming rules. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype.
What the build depends on. Stable identifiers, an agreed data schema, reversible imports, mapping review and source ownership. Data quality work can exceed model development effort. Obtain representative authorized inputs, an agreed review rubric and a buyer-side owner. Specific scope: One organization, one defined input format and one representative pilot batch using authorized documents and firm naming rules. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype.
Investment
A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.
- Phase 1
MVP
One buyer segment, one recurring use case; first modules: extract document metadata; suggest traceable names. Manual review in the loop.
- Phase 2
Paid pilot
Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- Phase 3
Full product
Self-serve onboarding, billing, monitoring and the wider integration set.
Indicative total, MVP to full product$41,000about 5 weeks of creation time · start with the MVP from $12,000
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.
| Stage | Hosting and infrastructure | AI usage | Total per month |
|---|---|---|---|
| MVP and paid pilotabout 3 customers | $50–$100 | $50–$100 | $100–$200 |
| Full productabout 50 customers | $190–$380 | $350–$700 | $540–$1,080 |
Run it or resell it
For your own team
Small law practices run it inside the business: authorized documents and firm naming rules in, reviewer-approved file index out, reviewed by your people.
As part of your offer
Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.
Your brand, or this one
Run it under your own brand, or start from this concept style.
- primary
#276591 - accent
#c99954 - surface
#e4ecf1 - ink
#22201e
- Headings
- Space Grotesk
- Text
- Inter
- Voice
- Precise, measured, defensible
Selling it to your own clients: the go-to-market playbook
Pricing to test
Test USD 500-2,500 for one collection cleanup and launch, followed by USD 100-500 monthly for maintenance within agreed record limits. Larger migrations and complex rights management are separately scoped. Prices are hypotheses. For this buyer, package the first sale around prepare a sample reviewer-approved file index from a small authorized set of authorized documents and firm naming rules and the defined reviewer-approved file index. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.
Message to test
Recover findability without altering original evidence. Demonstrate the result with prepare a sample reviewer-approved file index from a small authorized set of authorized documents and firm naming rules for small law practices. Use a concrete before-and-after example without promising unmeasured savings.
Where to find buyers
Legal operations networks and professional practice managers
Lead magnet
Prepare a sample reviewer-approved file index from a small authorized set of authorized documents and firm naming rules
The first 30 days
- Week 1: interview five prospective buyers from small law practices and inspect how they handle inherited files cannot be reliably identified.
- Week 2: prepare prepare a sample reviewer-approved file index from a small authorized set of authorized documents and firm naming rules using authorized or synthetic material.
- Week 3: share the demonstration through legal operations networks and professional practice managers and seek one bounded paid pilot.
- Week 4: measure unidentifiable matter files; reviewer correction minutes; buyer acceptance and repeat purchase, review delivery effort and ask for a repeat purchase. This is a validation schedule, not a promise that the full product can be built in thirty days.
Paid pilot
Agree the acceptance criteria, input limits and reviewer responsibilities before starting. Run prepare a sample reviewer-approved file index from a small authorized set of authorized documents and firm naming rules and deliver reviewer-approved file index. Compare unidentifiable matter files; reviewer correction minutes; buyer acceptance and repeat purchase with the buyer's current process on comparable cases; include corrections, missed issues and reviewer time. Seek payment and repeat use. Stop or revise the scope if data access, accuracy or unit economics fail.
Success metrics
Unidentifiable matter files; reviewer correction minutes; buyer acceptance and repeat purchase
Retention and expansion
Build repeat use around reviewer-approved file index. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on unidentifiable matter files; reviewer correction minutes; buyer acceptance and repeat purchase. Offer a recurring volume allowance after repeat demand; expand to adjacent tasks only when the buyer asks and delivery quality remains acceptable.
Why clients would pick it
A useful niche taxonomy, customer-approved mappings and accumulated correction history that improve retrieval and reduce repeated cleanup. For this concept, accumulate permissioned examples and reviewer corrections around recover findability without altering original evidence. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.
Alternatives and positioning
Spreadsheets, shared folders, existing asset or information management systems and manual data cleanup. Position this concept around recover findability without altering original evidence. Compare it against the customer's current process on the same representative task. This is proposed differentiation; no exhaustive competitor study or uniqueness claim has been established.
Main delivery costs
Import cleanup, extraction, storage, indexing, steward review, duplicate investigation and recurring source updates. Initial validation additionally budgets for representative sample preparation, interviews with small law practices, and qualified domain review of reviewer-approved file index. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.
Safeguards
Preserve matter confidentiality, access boundaries and original evidence. Qualified professionals review legal interpretations and final client documents. One organization, one defined input format and one representative pilot batch using authorized documents and firm naming rules. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.