Source-linked document reading and extraction workbench

For

Researchers, analysts and administrators who read and extract information from long PDFs and mixed document sets

Solves

Document answers, tables and citations are scattered across several rented tools, and extracted data cannot be traced back to the exact source passage.

Delivers

Source-linked answers, summaries and extracted records with citations and structured exports

Built in

about 5 weeks of creation time, MVP in 5 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Reduce time spent locating, reading and extracting document content while keeping every answer linked to its source.

1. Upload PDFs, docs, spreadsheets and scanned files by drag and drop.
2. Run OCR on scanned pages to produce searchable text.
3. Ask questions and receive answers grounded in the document.
4. Generate concise summaries of long documents.
5. Extract named fields, details and insights.
6. Extract structured tables from pages.
7. Select specific columns or fields for extraction.
8. Convert documents into structured JSON.
9. Export results as CSV, JSON or plain text.
10. Attach source references and citations to every answer.
11. Process batches of documents in one run.
12. Decode complex text, formulas and tables in research papers.
13. Find relevant papers without specifying keywords.
14. Run dialogue-based tutoring sessions with personalized feedback.
15. Track learner progress across subjects.
16. Store large files long term under encryption and access controls.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned source-linked record with source references and unresolved questions.

Everything these tools do, in one app

• Interactive AI Chat Allows users to ask questions and receive instant answers based on the document content.Found in aiPDF, AI Drive, Papermark AI and 1 more
• Document Summarization Automatically generates concise summaries of lengthy documents.Found in aiPDF, AI Drive, Papermark AI
• Information Extraction Extracts specific data, details, or insights from documents.Found in aiPDF, PDF Parser, AI Drive and 2 more
• Multi-Format Support Handles various document types beyond PDFs, such as docs and Excel files.Found in aiPDF, AI Drive
• References and Citations Provides source references and citations to back up responses.Found in aiPDF
• PDF to JSON Conversion Converts PDF documents into structured JSON data for easy integration.Found in PDF Parser
• Drag and Drop Interface Simplifies uploading and processing of PDF files with an intuitive interface.Found in PDF Parser, PDF Dino
• Selective Column Extraction Allows users to choose specific columns or fields to extract from documents.Found in PDF Parser
• Batch Processing Processes multiple documents at once for efficiency.Found in PDF Parser
• Interactive Tutoring Engages users in dialogue-based learning sessions that adapt to responses.Found in DeepTutor
• Personalized Feedback Provides tailored feedback to help learners improve weak areas.Found in DeepTutor
• Multi-Subject Support Covers multiple academic subjects like math, science, and language arts.Found in DeepTutor
• Progress Tracking Monitors learning progress over time.Found in DeepTutor
• LMS Integration Integrates with common learning management systems.Found in DeepTutor
• Large File Handling Supports uploading and storing large PDF files up to 2GB.Found in AI Drive
• Long-Term Storage Allows document storage without time restrictions.Found in AI Drive
• Multi-AI Model Integration Accesses multiple leading AI models for document analysis.Found in AI Drive
• OCR Technology Converts images and scanned documents into searchable text.Found in AI Drive
• Secure Storage Provides encryption and secure management of sensitive documents.Found in AI Drive
• Customizable Memory Allows users to customize the AI's memory for document analysis.Found in AI Drive
• Table Extraction Extracts structured tables from PDF documents.Found in PDF Dino
• Multiple Output Formats Supports output in formats like CSV, JSON, and plain text.Found in PDF Dino
• Customizable Output Allows users to tailor the output to fit specific workflows.Found in PDF Dino
• Complex Content Decoding Simplifies confusing text, mathematical expressions, and tables in research papers.Found in SciSpace by Typeset
• Keyword-Free Search Enables finding relevant papers without specifying keywords.Found in SciSpace by Typeset
• All-in-One Research Platform Offers additional tools like Chat with PDF and AI Writer for academic tasks.Found in SciSpace by Typeset
• Pitch Deck to Memo Conversion Converts pitch decks into structured investment memos.Found in Papermark AI
• Content Improvement Tools Includes grammar checks and structural suggestions for documents.Found in Papermark AI
• Open-Source Platform Allows self-hosting, customization, and community contributions.Found in Papermark AI

What goes in, what comes out

The workflow

1. In
   Start with

   Uploaded PDFs, docs, spreadsheets and scanned files; licensed source material; extraction schemas and column selections; review criteria

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect uploaded PDFs

4. 3

   Docs

5. 4

   Spreadsheets and scanned files

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Source-linked answers, summaries and extracted records with citations and structured exports

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 approved document set and licensed source material; final interpretation and professional judgments remain human. 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 reading and chat, Extraction and export console, Administrator console. Use a thumbnail or list gallery for documents, a large central reading pane with a chat panel, and a right-hand panel for citations, extracted fields and review state. Let users compare answers side by side with the source passage. Display draft, changes requested and approved states. Provide a shared review link with comments anchored to the relevant page and passage. Make the task-specific outcome source-linked answers, summaries and extracted records visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, document versions, shared review comments, approval states, usage allowances, extraction schemas, 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

Author-owned documents, authorized interviews and permitted research sources. Cloud document storage, LMS platforms, reference managers and export destinations. 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

   5 days

   One buyer segment, one recurring use case; first modules: upload PDFs, docs, spreadsheets and scanned files by drag and drop; run OCR on scanned pages to produce searchable text; ask questions and receive answers grounded in the document; generate concise summaries of long documents; extract named fields, details and insights. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

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

4. 4

   Full product

   2 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 researchers, analysts and administrators who read and extract information from long PDFs and mixed document sets use it to solve "document answers, tables and citations are scattered across several rented tools, and extracted data cannot be traced back to the exact source passage"?
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 extracted records per reviewer hour and corrections after export.
4. Measure, then decide. Track accepted extracted records per reviewer hour and corrections after export; 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 approved document set and licensed source material; final interpretation and professional judgments remain human. Implement one approved input format, a bounded representative case set and the first task modules: upload PDFs, docs, spreadsheets and scanned files by drag and drop; run OCR on scanned pages to produce searchable text; ask questions and receive answers grounded in the document; generate concise summaries of long documents; extract named fields, details and insights. Support the remaining modules with operator review: extract structured tables from pages; select specific columns or fields for extraction; convert documents into structured JSON; export results as CSV, JSON or plain text; attach source references and citations to every answer. 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 source-linked answers, summaries and extracted records. Retain the explicit scope boundary: One approved document set and licensed source material; final interpretation and professional judgments remain human.

What the build depends on. Document upload and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity extraction requires specialist review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved document set and licensed source material; final interpretation and professional judgments remain human.

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 batch, OCR or specialist extraction separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked answers, summaries and extracted records. 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, reading and extracting document content while keeping every answer linked to its source. Demonstrate a concrete source-linked answers, summaries and extracted records using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Researchers, analysts and administrators professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample source-linked answers, summaries and extracted records from a small authorized input set, with a transparent calculation of accepted extracted records per reviewer hour and corrections after export and no promised savings.

The first 30 days

1. Week 1: interview five researchers, analysts and administrators who read and extract information from long PDFs and mixed document sets and inspect a recent example of document answers, tables and citations scattered across several rented tools and extracted data that cannot be traced back to the exact source passage.
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 accepted extracted records per reviewer hour and corrections after export, 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 extracted records per reviewer hour and corrections after export. 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 extracted records per reviewer hour and corrections after export; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs source-linked answers, summaries and extracted records. 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, document layouts and review examples, together with reliable delivery for a narrow research and education niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for researchers, analysts and administrators who read and extract information from long PDFs and mixed document sets. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

aiPDF, PDF Parser, DeepTutor, AI Drive, PDF Dino, SciSpace by Typeset and Papermark AI. Compare this product with the buyer's present method on accepted extracted records per reviewer hour and corrections after export. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, OCR processing, 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 source-linked answers, summaries and extracted records. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Named owners approve substantive changes and export scope. One approved document set and licensed source material; final interpretation and professional judgments remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.