Evidence-backed research and reporting workspace

For

Research teams, analysts and knowledge workers who gather sources and produce evidence-backed reports

Solves

Topic research, source gathering and report writing are split across several rented tools, so citations, data and drafts are scattered and hard to verify.

Delivers

Reviewer-approved evidence-backed report with linked citations

Built in

about 5 weeks of creation time, MVP in 6 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 the time from question to a cited, reviewable report while keeping sources and drafts in one owned workspace.

1. Accept plain-language research questions.
2. Gather information automatically from permitted sources.
3. Upload and organize document collections.
4. Search both web and local documents.
5. Connect to live permitted data sets.
6. Run full-text search with contextual filters.
7. Summarize long documents.
8. Summarize across multiple documents.
9. Analyze data and interpret trends.
10. Extract and organize data from documents.
11. Generate structured reports with linked citations.
12. Run recursive deep research across subtopics.
13. Set research depth, breadth and domain parameters.
14. Adjust curation interactively to highlight relevant data.
15. Monitor for new matching documents and alert users.
16. Share findings and comments within teams.
17. Export reports to PDF and common formats.
18. Handle multi-step tasks with tool use.
19. Toggle between quick answers and deep reasoning.
20. Support long research sessions with a large context window.
21. Compare the reviewed result with the recorded baseline and value assumptions.
22. Capture corrections and named-owner approval before consequential use.
23. Export a versioned reviewer-approved evidence-backed report with source references and unresolved questions.

Everything these tools do, in one app

• Natural language query Allows users to ask questions in plain language to find relevant information quickly.Found in Researcher & Analyst in M365 Copilot
• Automated information gathering Automatically collects information on a given topic from various sources.Found in GPT Researcher, STORM
• Document upload and management Lets users upload and organize collections of research documents.Found in Iris.ai
• Web and local data sources Supports research from both online web sources and local documents.Found in GPT Researcher
• Live proxy data sets Connects to live data sets from publishers, patent authorities, or internal repositories.Found in Iris.ai
• Full-text search and filtering Enables full-text searches and contextual filtering to retrieve relevant documents quickly.Found in Iris.ai
• Automatic summarization Generates summaries of lengthy documents or data to highlight key points.Found in Researcher & Analyst in M365 Copilot, Iris.ai
• Multi-document summarization Produces summaries across multiple documents to simplify review.Found in Iris.ai
• Data analysis and trend interpretation Analyzes data to help interpret trends and patterns within datasets.Found in Researcher & Analyst in M365 Copilot
• Data extraction and organization Systematically extracts and organizes data from documents for analysis.Found in Iris.ai
• Report generation Compiles findings into structured reports or summaries.Found in GPT Researcher, STORM
• Deep research workflow Explores topics recursively, diving into subtopics while maintaining a holistic view.Found in GPT Researcher
• Customizable research parameters Allows users to tailor research depth, breadth, and domain-specific agents.Found in GPT Researcher
• Interactive curation Adjusts the curation process based on user needs to highlight relevant data.Found in STORM
• Real-time monitoring and alerts Monitors for new documents meeting specific criteria and alerts users.Found in Iris.ai
• Collaboration and sharing Enables sharing of insights and findings within teams.Found in Researcher & Analyst in M365 Copilot
• Export options Allows research outputs to be exported, such as PDF documents.Found in GPT Researcher
• Multi-step task handling Improves handling of complex, multi-step tasks and tool usage for stronger agent abilities.Found in DeepSeek-V3.1
• Hybrid inference modes Offers toggle between quick responses and deeper, multi-step reasoning.Found in DeepSeek-V3.1
• Large context window Supports extensive chats and reasoning tasks with a 128K token context length.Found in DeepSeek-V3.1
• Open-source weights Provides open-source model weights for community contributions and customization.Found in DeepSeek-V3.1
• Strict function calling API Supports beta API for strict function calling to integrate with external tools.Found in DeepSeek-V3.1

What goes in, what comes out

The workflow

1. In
   Start with

   Plain-language research questions, uploaded documents, permitted web sources and live data sets

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect the plain-language question

4. 3

   Uploaded documents

5. 4

   Permitted web sources and live data sets

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved evidence-backed report with linked citations

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. Final source verification, interpretation and professional judgment remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Research brief and sources, Editable report workspace, Review and delivery. Use a project list with a large central report canvas and a right-hand panel for sources, extracted data, alerts and comments. Let users compare draft versions side by side and switch between quick and deep research modes. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant claim or source. Make the task-specific outcome reviewer-approved evidence-backed report with linked citations visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, team comments, approval states, usage allowances, research limits, export 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, permitted web sources and licensed data sets. Cloud document storage, reference managers, data repositories and report 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

   6 days

   One buyer segment, one recurring use case; first modules: accept plain-language research questions; gather information automatically from permitted sources; upload and organize document collections; search both web and local documents; summarize long documents; summarize across multiple documents; generate structured reports with linked citations. 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 research teams, analysts and knowledge workers who gather sources and produce evidence-backed reports use it to solve "topic research, source gathering and report writing are split across several rented tools, so citations, data and drafts are scattered and hard to verify"?
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 reports per research hour and corrections after review.
4. Measure, then decide. Track accepted reports per research 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 research domain and one approved source set; final source verification and interpretation remain human. Implement one approved input format, a bounded representative case set and the first modules: accept plain-language research questions; gather information automatically from permitted sources; upload and organize document collections; search both web and local documents; summarize long documents; summarize across multiple documents; generate structured reports with linked citations. Support the remaining modules 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 the reviewer-approved evidence-backed report with linked citations. Retain the explicit scope boundary: One research domain and one approved source set; final source verification and interpretation remain human.

What the build depends on. Document upload and preview, asynchronous research jobs, editable version history, reviewer access and tested export formats. High-fidelity research requires specialist source verification. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One research domain and one approved source set; final source verification and interpretation 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 research package. Offer a monthly research allowance after repeat demand. Quote complex multi-domain or regulated research separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved evidence-backed report with linked citations. 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 the time from question to a cited, reviewable report while keeping sources and drafts in one owned workspace. Demonstrate a concrete reviewer-approved evidence-backed report with linked citations using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Research teams, analysts and knowledge workers 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 evidence-backed report with linked citations from a small authorized input set, with a transparent calculation of accepted reports per research hour and corrections after review and no promised savings.

The first 30 days

1. Week 1: interview five research teams, analysts and knowledge workers who gather sources and produce evidence-backed reports and inspect a recent example of topic research, source gathering and report writing split across several rented tools.
2. Week 2: prepare a consented or synthetic demonstration of the stated task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted reports per research 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 reports per research 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 reports per research 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 a reviewer-approved evidence-backed report with linked citations. 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 source sets, extraction rules and review examples, 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, analysts and knowledge workers who gather sources and produce evidence-backed reports. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Researcher & Analyst in M365 Copilot, GPT Researcher, STORM, Iris.ai and DeepSeek-V3.1. Compare this product with the buyer's present method on accepted reports per research 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 inference, source access, 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 reviewer-approved evidence-backed report with linked citations. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Named reviewers approve substantive claims and publication scope. One research domain and one approved source set; final source verification and interpretation remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.