Source-linked research answer workbench

For

Research teams and analysts who need cited answers from live web and internal sources

Solves

Answers are scattered across search tools, citations are hard to verify, and research notes live in separate apps.

Delivers

Reviewed, source-linked answers with citations and notes

Built in

about 4 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 finding and verifying cited answers while keeping sources and notes in one owned workspace.

1. Accept natural-language questions.
2. Run live web searches in real time.
3. Attach source citations to each claim.
4. Break complex questions into reasoning steps.
5. Present answers with images, diagrams and cards.
6. Run autonomous follow-up searches.
7. Perform calculations and create visualizations.
8. Personalize results to user context.
9. Keep search history private.
10. Provide specialized tools for coding, writing and image tasks.
11. Handle and organize large datasets.
12. Connect to data science frameworks.
13. Extract key information from uploaded data.
14. Expose API endpoints for integration.
15. Access multiple AI models in one place.
16. Support back-and-forth dialogue.
17. Accept text, images and documents as input.
18. Organize findings into folders.
19. Export research as reports or posts.
20. Collect relevance ratings on answers.
21. Post questions to relevant communities.
22. Provide a browser extension.
23. Offer language and difficulty options.
24. Compare the reviewed result with the recorded baseline and value assumptions.
25. Capture corrections and named-owner approval before consequential use.
26. Export a versioned reviewed, source-linked answers with citations and notes with source references and unresolved questions.

Everything these tools do, in one app

• Natural language querying Allows users to ask questions in plain language and receive relevant answers.Found in ThinkAny, Phind, You.com and 7 more
• Live web search Retrieves current information from the internet in real time.Found in Phind, You.com, Metaphor and 3 more
• Source citations Includes references to the sources used in the answers for verification.Found in You.com, Rixx, Claude Web Search and 1 more
• Multi-step reasoning Breaks down complex questions into steps to provide thorough answers.Found in Phind
• Visual result presentation Displays answers with images, diagrams, or cards to aid understanding.Found in Phind, Rixx
• Autonomous follow-up searches Automatically performs additional searches to deepen or complete answers.Found in Phind
• Integrated calculation and visualization Allows performing calculations and creating visualizations within the tool.Found in Phind
• Personalized search results Tailors search results to individual user preferences or context.Found in You.com, Metaphor
• Privacy-focused search Ensures that personal data and search history remain private.Found in You.com
• Integrated AI tools Provides specialized tools for tasks like coding, writing, and image generation.Found in You.com
• Scalable data management Handles and organizes large datasets efficiently.Found in Activeloop AI Knowledge Agent
• Integration with data science tools Connects with popular data science frameworks and environments.Found in Activeloop AI Knowledge Agent
• Automated knowledge extraction Automatically extracts key information from data to speed up analysis.Found in Activeloop AI Knowledge Agent
• API integration Offers API endpoints for developers to integrate search capabilities into applications.Found in Metaphor
• Multi-AI model access Allows users to interact with multiple AI models within one platform.Found in Poe
• Conversational interaction Supports back-and-forth dialogue for follow-up questions and deeper discussions.Found in Poe
• Multimodal input Accepts text, images, and documents as part of the query context.Found in Rixx
• Organizational folders Helps users collect and structure research findings and notes.Found in Rixx
• Export and publishing Turns research into blogs, reports, or other shareable formats.Found in ThinkAny, Rixx
• Community feedback Allows users to rate the relevance of answers to improve results.Found in GigaBrain
• Question posting to communities Enables users to post their questions to relevant online communities for broader input.Found in GigaBrain
• Browser extension Integrates the tool into the web browser for easy access.Found in GigaBrain
• Language and difficulty options Allows users to select their preferred language and difficulty level for answers.Found in Teach Anything

What goes in, what comes out

The workflow

1. In
   Start with

   Natural-language questions, live web results, uploaded documents and internal datasets

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect natural-language questions

4. 3

   Live web results

5. 4

   Uploaded documents and internal datasets

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, source-linked answers with citations and notes

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three 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 source policy and licensed content set; final fact and citation checks remain editorial. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Query and source setup, Answer and citation review, Library and export. Use a question list, a central answer canvas with inline citations, and a right-hand panel for sources, reasoning steps and comments. Let users compare answer versions and follow-up threads. Display draft, changes requested and approved states. Provide a shared workspace link with comments anchored to the relevant claim. Make the task-specific outcome reviewed, source-linked answers with citations and notes visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source 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

Author-owned documents, authorized web sources and permitted internal datasets. Cloud storage, data science environments 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 questions; run live web searches in real time. 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 who need cited answers from live web and internal sources use it to solve "answers are scattered across search tools, citations are hard to verify, and research notes live in separate apps"?
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: Verified answers per research hour and citation accuracy after review.
4. Measure, then decide. Track verified answers per research hour and citation accuracy 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 source policy and licensed content set; final fact and citation checks remain editorial. Implement one approved input format, a bounded representative case set and the first two task modules: accept natural-language questions; run live web searches in real time. Support the third module with operator review: attach source citations to each claim. 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 answers with citations and notes. Retain the explicit scope boundary: One fixed source policy and licensed content set; final fact and citation checks remain editorial.

What the build depends on. Source upload and preview, asynchronous search jobs, editable version history, reviewer access and tested export formats. High-fidelity research requires specialist fact and citation QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed source policy and licensed content set; final fact and citation checks remain editorial.

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 production allowance after repeat demand. Quote complex data science or specialist integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, source-linked answers with citations and notes. 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 finding and verifying cited answers while keeping sources and notes in one owned workspace. Demonstrate a concrete reviewed, source-linked answers with citations and notes using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Research teams and analysts who need cited answers from live web and internal sources 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 answers with citations and notes from a small authorized input set, with a transparent calculation of verified answers per research hour and citation accuracy after review and no promised savings.

The first 30 days

1. Week 1: interview five research teams and analysts who need cited answers from live web and internal sources and inspect a recent example of answers are scattered across search tools, citations are hard to verify, and research notes live in separate apps.
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 verified answers per research hour and citation accuracy 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: Verified answers per research hour and citation accuracy 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

Verified answers per research hour and citation accuracy 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 reviewed, source-linked answers with citations and notes. 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 policies, citation examples and review corrections, 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 who need cited answers from live web and internal sources. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

ThinkAny, Phind, You.com, Activeloop AI Knowledge Agent, Metaphor, Poe, Rixx, Claude Web Search, GigaBrain and Teach Anything. Compare this product with the buyer's present method on verified answers per research hour and citation accuracy after review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Search API calls, model usage, storage, reviewer hours, client revision rounds and licensed source content. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed, source-linked answers with citations and notes. 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 fixed source policy and licensed content set; final fact and citation checks remain editorial. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.