Source-linked document question and extraction console

For

Legal teams and administrators who answer questions and extract information from PDF documents

Solves

Document answers are scattered across several rented tools, and extracted facts lack source links, review states and a defensible record.

Delivers

Reviewer-approved answers and extracted fields linked to source passages

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Reduce time spent locating and verifying document answers while keeping a source-linked record.

1. Accept PDF, Word, Excel, CSV and text uploads.
2. Index documents for search and retrieval.
3. Answer natural-language questions from document content.
4. Provide a conversational chat interface.
5. Summarize long documents.
6. Query multiple documents at once.
7. Attach source citations with page and section references.
8. Support multiple document languages.
9. Process sensitive files under access controls.
10. Run local or on-device processing where required.
11. Convert documents to spoken audio.
12. Adjust audio playback speed.
13. Download audio files for offline use.
14. Redact confidential and personal information.
15. Organize documents in a library.
16. Configure model, context size and answer style.
17. Manage chat sessions.
18. Compare the reviewed result with the recorded baseline and value assumptions.
19. Capture corrections and named-owner approval before consequential use.
20. Export a versioned reviewer-approved answer and extracted field set with source references and unresolved questions.

Everything these tools do, in one app

• AI question answering Allows users to ask questions and receive instant answers extracted from document content.Found in ParrotPDF, Ask Your PDF, BrainyPdf and 6 more
• Conversational chat interface Provides a chat-based interface for natural language interaction with documents.Found in Ask Your PDF, Talk to PDF, aiPDF - talk to books, docs and podcasts and 3 more
• Document summarization Generates concise summaries of lengthy documents to provide quick overviews.Found in Ask Your PDF, Talk to PDF, chatd
• Multi-document support Enables uploading and querying multiple documents simultaneously.Found in ParrotPDF, ChattyDocs
• Source citations Includes references to the original document sections or page numbers in answers.Found in BrainyPdf, ChattyDocs, Hyperlink by Nexa AI
• Multi-format support Accepts various file types beyond PDFs, such as Word, Excel, CSV, and text files.Found in BrainyPdf, Chat With Data, ChattyDocs and 1 more
• Multi-language support Supports documents and conversations in multiple languages.Found in BrainyPdf
• Secure processing Protects sensitive document information during processing.Found in ParrotPDF, BrainyPdf
• Local processing Runs entirely on the user's device to keep data private and offline.Found in chatd, Hyperlink by Nexa AI
• Text-to-speech conversion Converts PDF documents into spoken audio with natural-sounding voices.Found in TalkingPdf.io
• Customizable playback speed Allows users to adjust the speed of audio playback for personalized listening.Found in TalkingPdf.io
• Audio download Enables downloading audio files for offline use.Found in TalkingPdf.io
• Secure redaction Redacts confidential and personal information from documents.Found in Talk to PDF
• Document organization Provides a library feature to manage and organize documents.Found in Ask Your PDF
• Multi-platform accessibility Accessible via mobile app, browser extension, and plugins for other platforms.Found in Ask Your PDF
• Dataset customization Allows configuring AI model, context size, creativity, and AI personality.Found in ChattyDocs
• Session management Manages chat sessions for continued interactions.Found in ChattyDocs
• On-device indexing Indexes local files on the device for quick search and retrieval.Found in Hyperlink by Nexa AI

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted PDFs, office files and text; user questions; extraction schemas; redaction rules; language and access settings

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted PDFs

4. 3

   Office files and text

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewer-approved answers and extracted fields linked to source passages

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate answers and extracted fields 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 legal interpretation and redaction decisions remain with qualified reviewers. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Document intake and library, Source-linked question console, Extraction and review queue, Administrator console. Use a document list with processing states, a central chat and answer panel, and a right-hand panel showing cited passages, page numbers and confidence. Let users compare answers side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant passage. Make the task-specific outcome reviewer-approved answers and extracted fields linked to source passages visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, document versions, client comments, approval states, usage allowances, revision limits, 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

Client-owned document stores, authorized case files and permitted research sources. Cloud document storage, office-file import/export and case management 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: accept PDF, Word, Excel, CSV and text uploads; index documents for search and retrieval; answer natural-language questions from document content. 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 legal teams and administrators who answer questions and extract information from PDF documents use it to solve "document answers are scattered across several rented tools, and extracted facts lack source links, review states and a defensible record"?
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: Accepted answers per reviewer hour and corrections after approval.
4. Measure, then decide. Track accepted answers per reviewer hour and corrections after approval; 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 language and one approved file set; final legal interpretation and redaction decisions remain with qualified reviewers. Implement one approved input format, a bounded representative case set and the first three task modules: accept PDF, Word, Excel, CSV and text uploads; index documents for search and retrieval; answer natural-language questions from document content. Support citation and redaction with operator review. 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 reviewer-approved answers and extracted fields linked to source passages. Retain the explicit scope boundary: One document language and one approved file set; final legal interpretation and redaction decisions remain with qualified reviewers.

What the build depends on. Document upload and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity legal work requires specialist review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One document language and one approved file set; final legal interpretation and redaction decisions remain with qualified reviewers.

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 package. Offer a monthly processing allowance after repeat demand. Quote complex multi-language or redaction-heavy work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved answers and extracted fields linked to source passages. 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 locating and verifying document answers while keeping a source-linked record. Demonstrate a concrete reviewer-approved answers and extracted fields linked to source passages using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Legal teams and administrators who answer questions and extract information from PDF documents professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved answers and extracted fields linked to source passages from a small authorized input set, with a transparent calculation of accepted answers per reviewer hour and corrections after approval and no promised savings.

The first 30 days

1. Week 1: interview five legal teams and administrators who answer questions and extract information from PDF documents and inspect a recent example of document answers scattered across several rented tools and extracted facts lacking source links, review states and a defensible record.
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 accepted answers per reviewer hour and corrections after approval, 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: Accepted answers per reviewer hour and corrections after approval. 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

Accepted answers per reviewer hour and corrections after approval; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved answers and extracted fields linked to source passages. 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 extraction schemas, redaction 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 legal teams and administrators who answer questions and extract information from PDF documents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

ParrotPDF, TalkingPdf.io, Ask Your PDF, BrainyPdf, Talk to PDF, aiPDF - talk to books, docs and podcasts, Chat With Data, ChattyDocs, chatd and Hyperlink by Nexa AI. Compare this product with the buyer's present method on accepted answers per reviewer hour and corrections after approval. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, document processing, storage, reviewer hours, client revision rounds and licensed source documents. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved answers and extracted fields linked to source passages. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Qualified reviewers approve substantive answers, redactions and filing scope. One document language and one approved file set; final legal interpretation and redaction decisions remain with qualified reviewers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.