Source-linked document analysis and Q&A console

For

Research teams and analysts working through long documents and article sets

Solves

Long documents and article sets take too long to read, summarize and question, and answers are hard to trace back to the source.

Delivers

Source-linked summaries, answers and extracted findings

Built in

about 4 weeks of creation time, MVP in 5 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 reading and review time while keeping every answer traceable to its source.

1. Generate concise summaries from long texts automatically.
2. Answer questions using only the supplied document content.
3. Adjust summary length on request.
4. Accept PDF, Word and web article inputs.
5. Handle content in multiple languages.
6. Highlight key sentences inside the original text.
7. Search keywords across documents to find relevant sections.
8. Tag and categorize documents for retrieval.
9. Share summarized content through controlled links.
10. Link each insight back to its source quote and location.
11. Process multiple documents together to extract common themes.
12. Adapt answers to user-defined goals and queries.
13. Refine queries and exclude selected speakers or sections.
14. Explain complex concepts in plain language.
15. Provide academic tools for paraphrasing, conclusions and stated limitations.
16. Scan permitted sources for related papers and integrate findings into reports.
17. Connect with reference management tools for citation.
18. Capture reviewer corrections and named-owner approval before consequential use.
19. Export a versioned source-linked analysis with references and unresolved questions.

Everything these tools do, in one app

• Automatic summarization Generates concise summaries from long texts automatically.Found in Text Summarizer AI, Sharly AI, iBrief and 4 more
• Question answering Allows users to ask questions and get answers based on the document content.Found in EpsteinGPT, Sharly AI, PaperChat
• Adjustable summary length Lets users control how long or short the generated summary is.Found in Text Summarizer AI, Summarify, Abstractify
• Multi-format support Accepts various document types such as PDF, Word, and web articles.Found in Text Summarizer AI, Sharly AI, Summarify and 1 more
• Multi-language support Handles content in multiple languages.Found in Text Summarizer AI, Summarify
• Key sentence highlighting Highlights important sentences within the original text for easy reference.Found in Summarify
• Keyword search Enables searching for specific keywords within documents to find relevant sections.Found in PaperChat
• Document categorization Organizes documents with tags or categories for better retrieval.Found in Sharly AI
• Sharing options Provides easy ways to share summarized content with others.Found in iBrief
• Traceable insights Links insights back to source quotes and timestamps for verification.Found in Breyta.ai
• Multi-file processing Analyzes multiple documents simultaneously to extract common themes.Found in Breyta.ai
• Context-aware responses Adapts answers based on user-defined goals and queries.Found in Breyta.ai
• Interactive query refinement Allows users to refine queries and exclude certain speakers during analysis.Found in Breyta.ai
• Plain language explanations Breaks down complex concepts into simple, easy-to-understand language.Found in ExplainThis.AI
• Academic tools Provides specialized tools like paraphrasing, identifying conclusions, and highlighting limitations.Found in Abstractify
• Deep research capabilities Scans the web to gather relevant academic papers and integrates data into reports.Found in Abstractify
• Reference management integration Connects with popular reference management tools for easier citation.Found in PaperChat
• Community discussion Offers a space for users to discuss prompts and usage tips.Found in EpsteinGPT

What goes in, what comes out

The workflow

1. In
   Start with

   Uploaded documents, article collections and user questions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect uploaded documents

4. 3

   Article collections and user questions

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Source-linked summaries, answers and extracted findings

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 fixed document schema and permitted source set; final interpretation and citation checks remain with the reviewer. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Document intake and library, Analysis workspace, Review and export. Use a thumbnail gallery for documents, a large central reading and question canvas, and a right-hand panel for sources, tags and comments. Let users compare summaries and answers side by side. Display draft, changes requested and approved states. Provide a shareable review link with comments anchored to the relevant passage. Make the task-specific outcome source-linked summaries, answers and extracted findings visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, document versions, reviewer comments, approval states, usage allowances, query 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

Author-owned document collections, permitted research sources and reference management tools. Cloud document storage, file import/export and publishing 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: generate concise summaries from long texts automatically; answer questions using only the supplied document content. 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 research teams and analysts working through long documents and article sets use it to solve "long documents and article sets take too long to read, summarize and question, and answers are hard to trace back to the source"?
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 summaries per reviewer hour and corrections after review.
4. Measure, then decide. Track accepted summaries per reviewer hour and corrections after review; 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 fixed document schema and permitted source set; final interpretation and citation checks remain with the reviewer. Implement one approved input format, a bounded representative case set and the first two task modules: generate concise summaries from long texts automatically; answer questions using only the supplied document content. Support the remaining modules with operator review: link each insight back to its source quote and location. 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 summaries, answers and extracted findings. Retain the explicit scope boundary: One fixed document schema and permitted source set; final interpretation and citation checks remain with the reviewer.

What the build depends on. Document upload and preview, asynchronous analysis jobs, editable version history, reviewer access and tested export formats. High-fidelity research requires specialist review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed document schema and permitted source set; final interpretation and citation checks remain with the reviewer.

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 production allowance after repeat demand. Quote complex multi-source or specialist research separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked summaries, answers and extracted findings. 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 reading and review time while keeping every answer traceable to its source. Demonstrate a concrete source-linked summaries, answers and extracted findings using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Research teams and analysts working through long documents and article sets 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 summaries, answers and extracted findings from a small authorized input set, with a transparent calculation of accepted summaries per reviewer hour and corrections after review and no promised savings.

The first 30 days

1. Week 1: interview five research teams and analysts working through long documents and article sets and inspect a recent example of long documents and article sets take too long to read, summarize and question, and answers are hard to trace back to the source.
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 summaries per reviewer hour and corrections after review, 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 summaries per reviewer hour and corrections after review. 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 summaries per reviewer hour and corrections after review; 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 summaries, answers and extracted findings. 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 document schemas, review examples and citation rules, together with reliable delivery for a narrow research niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for research teams and analysts working through long documents and article sets. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

EpsteinGPT, Zero, Text Summarizer AI, Sharly AI, iBrief, Summarify, Breyta.ai, ExplainThis.AI, Abstractify and PaperChat, plus manual reading and generic chat tools. Compare this product with the buyer's present method on accepted summaries per reviewer hour and corrections after review. 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 material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of source-linked summaries, answers and extracted findings. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Reviewers approve substantive interpretations and publication scope. One fixed document schema and permitted source set; final interpretation and citation checks remain with the reviewer. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.