Legal document intake and stewardship console

For

Law firms, in-house legal teams and compliance functions handling large volumes of client and matter documents

Solves

Incoming documents arrive unsorted, inconsistently named and unsearchable, so staff spend hours filing, renaming and re-reading files before any legal work starts.

Delivers

Reviewed, searchable and correctly named document library linked to matter files

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$12,500 for the MVP, $42,500 for the full product

Run it

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

Cut manual filing and retrieval time while keeping every document under the firm's own control.

1. Ingest documents from watched folders and cloud storage.
2. Classify and sort files into matters and folders.
3. Rename documents from their content and matter metadata.
4. Summarize each document and flag key clauses and dates.
5. Extract parties, dates, amounts and obligations into structured fields.
6. Index documents, images and scanned pages for multi-format search.
7. Support keyword and semantic search across the library.
8. Answer questions about a document through a chat interface with source citations.
9. Manage PDFs in a centralized store with version history.
10. Export clean markdown for note-taking and knowledge tools.
11. Run intake and indexing in the background without blocking user work.
12. Share documents and folders under role-based permissions.
13. Compare the reviewed result with the recorded baseline and value assumptions.
14. Capture corrections and named-owner approval before consequential use.
15. Export a versioned reviewed, searchable and correctly named document library linked to matter files with source references and unresolved questions.

Everything these tools do, in one app

• Automatic File Organization Automatically sorts and categorizes files without manual effort.Found in MagicDocs, Magic Documents, AutoSortPro and 1 more
• Smart Renaming Renames documents based on their content for easier identification.Found in MagicDocs, AutoSortPro
• Document Summarization Creates concise summaries to highlight essential information within documents.Found in MagicDocs, Magic Documents
• Data Extraction Pulls out key data points from various document types to reduce manual errors.Found in MagicDocs, Magic Documents, Documind
• Secure Sharing and Collaboration Allows users to share organized documents and collaborate with teams while maintaining privacy and security.Found in MagicDocs, Magic Documents, Documind
• Searchable Knowledge Base Creates a searchable database to easily find and rediscover files.Found in AutoSortPro
• Markdown Export Generates clean markdown files compatible with note-taking apps like Obsidian, Notion, and Roam.Found in AutoSortPro
• Background Processing Processes files quickly in the background without interrupting user activity.Found in AutoSortPro, Dynbox.app
• Cloud Storage Integration Integrates with popular cloud storage platforms for seamless file management.Found in Dynbox.app
• Semantic Search Enables searching files by meaning rather than just keywords.Found in Dynbox.app
• Multi-format Support Supports indexing and handling of various file types including documents, images, and videos.Found in MagicDocs, AutoSortPro, Dynbox.app
• PDF Management Upload, store, and organize multiple PDFs in a centralized location.Found in Documind
• Chat Interface Interact with documents through a chat interface to ask questions and get quick answers.Found in Documind
• Folder Organization Organize documents into folders for easier management.Found in Documind
• Enterprise-Level Security Implements enterprise-grade protection measures to safeguard sensitive information.Found in Magic Documents

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted document collections, matter metadata, retention rules and access policies

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted document collections

4. 3

   Matter metadata

5. 4

   Retention rules and access policies

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, searchable and correctly named document library linked to matter files

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 document taxonomy and one retention schedule per pilot; privilege calls, legal advice and final filing decisions remain with qualified staff. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Intake and classification queue, Searchable library and document viewer, Stewardship and access console. Use a folder and matter tree for navigation, a large central document viewer, and a right-hand panel for extracted fields, summaries, versions and comments. Let users compare a renamed file against its original. Display draft, in review and approved states. Provide a permissioned client or co-counsel link with comments anchored to the relevant document. Make the task-specific outcome a reviewed, searchable and correctly named document library linked to matter files visible beside its evidence, review state and value baseline.

Accounts and administration

Matter ownership, document versions, client comments, approval states, retention schedules, access roles, 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

Firm-owned document management systems, permitted client document collections and authorized matter metadata. Cloud storage, PDF tooling, note-taking and knowledge-base export destinations. Start with file exchange and validate destination specifications before promising direct filing. 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: ingest documents from watched folders and cloud storage; classify and sort files into matters and folders. 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 law firms, in-house legal teams and compliance functions handling large volumes of client and matter documents use it to solve "incoming documents arrive unsorted, inconsistently named and unsearchable, so staff spend hours filing, renaming and re-reading files before any legal work starts"?
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: Documents filed per reviewer hour and retrieval time per requested document.
4. Measure, then decide. Track documents filed per reviewer hour and retrieval time per requested document; 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 document taxonomy and one retention schedule per pilot; privilege calls, legal advice and final filing decisions remain with qualified staff. Implement one approved input format, a bounded representative case set and the first two task modules: ingest documents from watched folders and cloud storage; classify and sort files into matters and folders. Support the third module with operator review: rename documents from their content and matter metadata. 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 reviewed, searchable and correctly named document library linked to matter files. Retain the explicit scope boundary: One document taxonomy and one retention schedule per pilot; privilege calls, legal advice and final filing decisions remain with qualified staff.

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

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 document collection. Offer a monthly processing allowance after repeat demand. Quote complex migrations or specialist review separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, searchable and correctly named document library linked to matter files. 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

Cut manual filing and retrieval time while keeping every document under the firm's own control. Demonstrate a concrete reviewed, searchable and correctly named document library linked to matter files using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Law firms, in-house legal teams and compliance functions professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, searchable and correctly named document library linked to matter files from a small authorized input set, with a transparent calculation of documents filed per reviewer hour and retrieval time per requested document and no promised savings.

The first 30 days

1. Week 1: interview five law firms, in-house legal teams and compliance functions handling large volumes of client and matter documents and inspect a recent example of incoming documents arriving unsorted, inconsistently named and unsearchable, so staff spend hours filing, renaming and re-reading files before any legal work starts.
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 documents filed per reviewer hour and retrieval time per requested document, 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: Documents filed per reviewer hour and retrieval time per requested document. 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

Documents filed per reviewer hour and retrieval time per requested document; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a reviewed, searchable and correctly named document library linked to matter files. 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 taxonomies, retention rules and review examples, together with reliable delivery for a narrow legal niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for law firms, in-house legal teams and compliance functions handling large volumes of client and matter documents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

MagicDocs, Magic Documents, AutoSortPro, Dynbox.app and Documind, plus manual filing and generic cloud storage. Compare this product with the buyer's present method on documents filed per reviewer hour and retrieval time per requested document. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Ingestion and indexing compute, 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 the reviewed, searchable and correctly named document library linked to matter files. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve privilege, source attribution, confidentiality and usage permissions. Qualified staff approve substantive classifications, privilege calls and filing scope. One document taxonomy and one retention schedule per pilot; privilege calls, legal advice and final filing decisions remain with qualified staff. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.