Source-linked research and document assistant console

For

Research teams, analysts and administrators who manage documents and produce evidence-backed content

Solves

Research, document management and content production sit in separate rented tools, so sources, drafts and approvals are hard to trace and data is spread across vendors.

Delivers

Reviewed, source-linked drafts, summaries and reports

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

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

Reduce tool switching and re-checking while keeping every claim linked to a source.

1. Accept natural-language queries and commands.
2. Generate documents, articles and emails.
3. Find and summarize research information.
4. Organize and retrieve files and documents.
5. Provide a mobile app for phones and tablets.
6. Connect third-party applications and services.
7. Support real-time multi-user collaboration.
8. Analyze data and produce visualizations and reports.
9. Automate repetitive tasks and send reminders.
10. Provide customizable dashboards for project insights.
11. Handle multiple requests and conversations at once.
12. Enable basic functions offline.
13. Adjust response length, style and preferences.
14. Apply privacy and security controls to interactions.
15. Give instant answers from organizational data.
16. Support multiple file formats and languages.
17. Analyze sentiment in text data.
18. Generate personas from research data.
19. Sync work across devices via the cloud.
20. Highlight potential gaps in documentation.
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 reviewed, source-linked drafts, summaries and reports with source references and unresolved questions.

Everything these tools do, in one app

• Natural Language Interaction Allows users to interact with the AI using everyday language for queries and commands.Found in AI Assistant, Sol-A., Claude for Desktop and 3 more
• Content Generation Automatically creates various types of content such as documents, articles, and emails.Found in AI Assistant, Sol-A., Findr 2.0 and 2 more
• Research Assistance Helps users find and summarize information for research purposes.Found in AI Assistant, Deskrib.Ai, Copilotly
• Document Management Organizes and provides access to files and documents efficiently.Found in AI Assistant, Workspace by Portal Labs, Research Studio and 1 more
• Mobile App Provides a mobile application for access on smartphones and tablets.Found in AI Assistant, MobileGPT 2.0
• Third-Party Integrations Connects with other popular applications and services to enhance workflow.Found in Sol-A., Workspace by Portal Labs, Findr 2.0 and 2 more
• Real-Time Collaboration Enables multiple users to work together on projects simultaneously.Found in Sol-A., Workspace by Portal Labs
• Data Analysis Analyzes data and provides visualizations and reports.Found in Sol-A., Research Studio
• Task Automation Automates repetitive tasks and sends smart reminders to keep projects on track.Found in Workspace by Portal Labs
• Customizable Dashboards Provides customizable dashboards for real-time project insights.Found in Workspace by Portal Labs
• Multi-Tasking Support Allows users to handle multiple requests or conversations simultaneously.Found in Claude for Desktop, MobileGPT 2.0
• Offline Mode Enables basic functions without an internet connection.Found in Claude for Desktop, MobileGPT 2.0
• Customizable Settings Allows users to adjust response length, style, and other preferences.Found in Claude for Desktop, MobileGPT 2.0
• Privacy and Security Ensures user data privacy and security during interactions.Found in Claude for Desktop
• Instant Answers Provides quick answers to questions based on organizational data.Found in Findr 2.0, Slite - Ask
• Multi-Format Support Supports various file formats and languages for data upload and analysis.Found in Research Studio, Slite - Ask
• Sentiment Analysis Analyzes the sentiment of text data.Found in Research Studio
• Persona Generation Generates detailed personas from research data.Found in Research Studio
• Cloud Sync Syncs work across devices via the cloud.Found in Research Studio
• Knowledge Gap Identification Highlights potential gaps in documentation.Found in Slite - Ask

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted documents, datasets, notes and organizational files

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted documents

4. 3

   Datasets

5. 4

   Notes and organizational files

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, source-linked drafts, summaries and reports

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 research judgments, document approvals and publication scope remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source intake and permissions, Assistant workspace, Admin console and delivery. Use a thumbnail gallery for projects and documents, a large central editing canvas, and a right-hand panel for sources, constraints and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant source. Make the task-specific outcome reviewed, source-linked drafts, summaries and reports visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, 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

Customer-owned documents, authorized datasets and permitted research sources. 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: accept natural-language queries and commands; find and summarize research information; generate documents, articles and emails. 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, analysts and administrators who manage documents and produce evidence-backed content use it to solve "research, document management and content production sit in separate rented tools, so sources, drafts and approvals are hard to trace and data is spread across vendors"?
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 outputs per reviewer hour and corrections after approval.
4. Measure, then decide. Track accepted outputs 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 approved document set and one language; final research judgments, document approvals and publication scope remain human. Implement one approved input format, a bounded representative case set and the first three task modules: accept natural-language queries and commands; find and summarize research information; generate documents, articles and emails. 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 reviewed, source-linked drafts, summaries and reports. Retain the explicit scope boundary: One approved document set and one language; final research judgments, document approvals and publication scope remain human.

What the build depends on. Document upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity research requires specialist domain QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved document set and one language; final research judgments, document approvals and publication scope 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 set. Offer a monthly production allowance after repeat demand. Quote complex data analysis or specialist research separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, source-linked drafts, summaries and reports. 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 tool switching and re-checking while keeping every claim linked to a source. Demonstrate a concrete reviewed, source-linked drafts, summaries and reports using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Research teams, analysts and administrators who manage documents and produce evidence-backed content professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, source-linked drafts, summaries and reports from a small authorized input set, with a transparent calculation of accepted outputs per reviewer hour and corrections after approval and no promised savings.

The first 30 days

1. Week 1: interview five research teams, analysts and administrators who manage documents and produce evidence-backed content and inspect a recent example of research, document management and content production sit in separate rented tools, so sources, drafts and approvals are hard to trace and data is spread across vendors.
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 outputs 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 outputs 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 outputs 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 reviewed, source-linked drafts, summaries and reports. 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 mappings, review examples and organizational terminology, 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 administrators who manage documents and produce evidence-backed content. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

AI Assistant, Sol-A., Workspace by Portal Labs, Claude for Desktop, Findr 2.0, Research Studio, Deskrib.Ai, Slite - Ask, MobileGPT 2.0 and Copilotly. Compare this product with the buyer's present method on accepted outputs 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

Generation attempts, data 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 reviewed, source-linked drafts, summaries and reports. Track cost per accepted output, including correction work, unsuccessful cases and support.

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