Source-linked research answer workbench

For

Research teams, analysts and knowledge workers who need current, source-linked answers

Solves

Answers are scattered across search tabs, chat tools and saved notes, with no traceable source or shared review.

Delivers

Reviewer-approved source-linked answers with citations

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 time spent gathering and checking answers while keeping every claim linked to its source.

1. Accept natural language questions.
2. Run real-time web search across permitted sources.
3. Generate concise AI summaries with source links.
4. Support follow-up questions that refine the answer.
5. Provide a conversational chat interface.
6. Accept image uploads as part of a query.
7. Filter spam and low-quality results.
8. Present results in a clean visual layout.
9. Open a distraction-free reader for source articles.
10. Keep searches private and ad-free.
11. Consolidate web, document and note data into one searchable space.
12. Apply retrieval augmented generation over permitted sources.
13. Assist drafting, brainstorming and key-point extraction.
14. Support team spaces with real-time sharing.
15. Save web content through a browser extension.
16. Run scheduled recurring research and deliver summaries by email or app.
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 reviewer-approved source-linked answer with citations and unresolved questions.

Everything these tools do, in one app

• Natural language queries Lets you ask questions in everyday language and get relevant answers.Found in Perplexity for Mac, Perplexity AI, Andi and 6 more
• Real-time web search Pulls current information from the web to answer your questions.Found in Perplexity for Mac, Andi, Perplexity Labs and 2 more
• AI-generated summaries Provides concise summaries of search results or answers.Found in Perplexity for Mac, Andi, Perplexity Labs and 2 more
• Follow-up questions Allows you to ask additional questions to refine or clarify responses.Found in Perplexity for Mac, Perplexity Labs
• Conversational interface Interacts with you in a chat-like manner for a natural experience.Found in Andi, Perplexity Labs, DeepSeek for iOS
• Source links Includes links to original sources for further exploration.Found in Google Search AI Mode, Peruser AI
• Image upload Allows you to upload images as part of your search query.Found in Perplexity AI
• Privacy and ad-free Offers private, anonymous searching without ads or tracking.Found in Andi
• Distraction-free reader Provides a clean reading view for articles without distractions.Found in Andi
• Spam and bad content filtering Filters out spam and low-quality content from search results.Found in Andi
• Visual search results Presents search results in a visually appealing layout.Found in Andi
• Content creation assistance Helps you generate factual, high-quality content for writing and research.Found in Andi
• Unified data organization Consolidates data from multiple sources into a single searchable space.Found in IKI AI
• Retrieval augmented generation Uses advanced language models to enhance research and provide deeper insights.Found in IKI AI
• AI co-pilot and editor Assists with drafting text, brainstorming, and extracting key information.Found in IKI AI
• Team collaboration Enables team spaces and real-time sharing for collective intelligence.Found in IKI AI
• Browser extension Allows fast saving of web content directly from your browser.Found in IKI AI
• Automated scheduled research Runs recurring research based on your prompts and schedule.Found in Perplexity Tasks
• Email and app delivery Sends research summaries directly to your email or app.Found in Perplexity Tasks

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted web sources, uploaded documents, team notes and query history

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted web sources

4. 3

   Uploaded documents and team notes

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewer-approved source-linked answers with citations

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate answers 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 factual and citation checks remain with qualified reviewers. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Query and source intake, Editable answer preview, Team review and delivery. Use a thumbnail gallery for saved queries, a large central answer canvas, and a right-hand panel for sources, citations and comments. Let users compare answer versions side by side. Display draft, changes requested and approved states. Provide a shared team link with comments anchored to the relevant claim. Make the task-specific outcome reviewer-approved source-linked answers with citations visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, team 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

Customer-owned documents, permitted web sources and team note systems. Cloud storage, browser extension endpoints and email or app delivery. 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 real-time web search across permitted sources; generate concise AI summaries with source links. 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 knowledge workers who need current, source-linked answers use it to solve "answers are scattered across search tabs, chat tools and saved notes, with no traceable source or shared review"?
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 answers per research hour and corrections after review.
4. Measure, then decide. Track accepted answers 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 approved source set and one team workspace; final factual and citation checks remain with qualified reviewers. Implement one approved input format, a bounded representative case set and the first three task modules: accept natural language questions; run real-time web search across permitted sources; generate concise AI summaries with source links. Support the remaining modules with operator review: follow-up questions, conversational interface, image upload, spam filtering, visual layout, reader view, private search, data consolidation, retrieval augmented generation, drafting assistance, team spaces, browser extension and scheduled delivery. 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 reviewer-approved source-linked answers with citations. Retain the explicit scope boundary: One approved source set and one team workspace; final factual and citation checks remain with qualified reviewers.

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 factual QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved source set and one team workspace; final factual and citation checks remain with qualified reviewers.

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-source or specialist research separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved source-linked answer with 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 time spent gathering and checking answers while keeping every claim linked to its source. Demonstrate a concrete reviewer-approved source-linked answer with 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 source-linked answer with citations from a small authorized input set, with a transparent calculation of accepted answers 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 need current, source-linked answers and inspect a recent example of answers scattered across search tabs, chat tools and saved notes, with no traceable source or shared review.
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 answers 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 answers 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 answers 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 reviewer-approved source-linked answers with 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 sources, citation 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 need current, source-linked answers. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Perplexity for Mac, Perplexity AI, Andi, Perplexity Labs, IKI AI, Google Search AI Mode, DeepSeek for iOS, Peruser AI and Perplexity Tasks. Compare this product with the buyer's present method on accepted answers 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

Search and model calls, document processing, storage, reviewer hours, client revision rounds and licensed source access. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved source-linked answers with citations. Track cost per accepted answer, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Reviewers approve substantive claims and publication scope. One approved source set and one team workspace; final factual and citation checks remain with qualified reviewers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.