Community opinion research and summary console

For

Marketing and product teams that need authentic community opinions to answer questions or inform decisions

Solves

Relevant community discussions are scattered across Reddit and YouTube, and reading and verifying them takes hours.

Delivers

Source-linked summary with citations and reviewer approval

Built in

about 4 weeks of creation time, MVP in 4 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 spent finding and reading community discussions while keeping every claim linked to its source.

1. Search community platforms for relevant discussions.
2. Summarize discussions, comments and opinions concisely.
3. Attach citations and direct links to original sources.
4. Surface authentic user experiences and recommendations.
5. Provide a browser extension for in-page research.
6. Analyze prolific and helpful contributors.
7. Support conversational questions and answers.
8. Aggregate highly rated comments from threads.
9. Show recent and frequently queried topics.
10. Support shared search rooms for teams.
11. Integrate AI agents to draft and validate results.
12. Attribute contributions to users and AI.
13. Let team members upvote and validate trusted answers.
14. Filter results by selected YouTube channels.
15. Jump to the exact video moment where a topic is discussed.
16. Suggest context-aware questions from the current page.
17. Rank and highlight the most helpful comments.
18. Generate draft content from approved inputs.
19. Suggest edits to improve draft text.
20. Schedule and publish approved content.
21. Support team feedback on drafts.
22. Report content performance in a dashboard.
23. Compare the reviewed result with the recorded baseline and value assumptions.
24. Capture corrections and named-owner approval before consequential use.
25. Export a versioned source-linked summary with citations and unresolved questions.

Everything these tools do, in one app

• Community discussion search Searches across community platforms like Reddit and YouTube to find relevant discussions and answers.Found in Gigabrain, Reddit Answers, Reddit Scout and 2 more
• AI-powered summaries Generates concise summaries of discussions, comments, or opinions to save reading time.Found in Reddit Answers, Reddit Scout, Pigeon
• Source citations and links Provides citations or direct links to original sources for verification and deeper exploration.Found in Gigabrain, Reddit Answers
• Real user experiences Surfaces authentic human perspectives and recommendations from community discussions.Found in Reddit Answers, Pigeon
• Chrome extension Offers a browser extension for seamless integration into browsing workflows.Found in Gigabrain, Reddit Scout, Pigeon
• Contributor analysis Analyzes prolific and helpful contributors to highlight trustworthy information.Found in Gigabrain
• Conversational interface Allows users to ask questions and receive responses in a conversational manner.Found in Reddit Answers
• Automated aggregation Automatically scans and gathers highly rated comments and recommendations from Reddit threads.Found in Reddit Scout
• Real-time search topics Displays recent and frequently queried product topics for up-to-date information.Found in Reddit Scout
• Collaborative search rooms Enables multiple users to search together simultaneously in shared spaces.Found in LLMHub
• AI agent integration Integrates AI agents to assist in generating and validating search results.Found in LLMHub
• Contribution attribution Clearly attributes contributions to both users and AI for transparency.Found in LLMHub
• Upvote validation Allows team members to upvote and validate trusted answers.Found in LLMHub
• Ad-free experience Provides an uninterrupted search experience without advertisements.Found in LLMHub
• Channel filtering Filters search results by selecting multiple YouTube channels for targeted findings.Found in Explore AI
• Time-stamp navigation Jumps directly to the specific moment in a video where information is discussed.Found in Explore AI
• Context-aware questions Analyzes the current webpage and suggests relevant questions to guide research.Found in Pigeon
• Comment ranking Ranks and highlights the most helpful comments to reduce noise.Found in Pigeon
• Content generation Automatically generates content based on user inputs.Found in Felo
• Editing suggestions Provides intelligent editing suggestions to improve text quality.Found in Felo
• Task automation Automates content scheduling and publishing tasks.Found in Felo
• Collaboration tools Enables team workflows and feedback for content creation.Found in Felo
• Analytics dashboard Provides insights on content performance.Found in Felo

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted community discussions, video transcripts and channel selections

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted community discussions

4. 3

   Video transcripts and channel selections

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Source-linked summary with citations and reviewer approval

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. Public community content only; no private or restricted data. Final claims and publication decisions 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 summary preview, Client proof and delivery. Use a thumbnail gallery for research projects, a large central editing canvas, and a right-hand panel for sources, citations 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 claim. Make the task-specific outcome source-linked summary with citations and reviewer approval 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

Authorized community platform access, permitted video transcripts and approved research sources. Cloud storage, content scheduling destinations and analytics sources. 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

   4 days

   One buyer segment, one recurring use case; first modules: search community platforms for relevant discussions; summarize discussions, comments and opinions concisely. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   10 days

   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 marketing and product teams that need authentic community opinions to answer questions or inform decisions use it to solve "relevant community discussions are scattered across Reddit and YouTube, and reading and verifying them takes hours"?
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: Research hours per accepted answer and citation accuracy after review.
4. Measure, then decide. Track research hours per accepted answer 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: Public community content only; no private or restricted data. Final claims and publication decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: search community platforms for relevant discussions; summarize discussions, comments and opinions concisely. Support the third module with operator review: attach citations and direct links to original sources. 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 source-linked summary with citations and reviewer approval. Retain the explicit scope boundary: Public community content only; no private or restricted data. Final claims and publication decisions remain human.

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 review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: Public community content only; no private or restricted data. Final claims and publication decisions 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-platform or high-volume research separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked summary with citations and reviewer approval. 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 spent finding and reading community discussions while keeping every claim linked to its source. Demonstrate a concrete source-linked summary with citations and reviewer approval using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Marketing and product research 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 summary with citations and reviewer approval from a small authorized input set, with a transparent calculation of research hours per accepted answer and citation accuracy after review and no promised savings.

The first 30 days

1. Week 1: interview five marketing and product teams that need authentic community opinions and inspect a recent example of relevant community discussions scattered across Reddit and YouTube, and reading and verifying them takes hours.
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 research hours per accepted answer 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: Research hours per accepted answer 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

Research hours per accepted answer 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 a source-linked summary with citations and reviewer approval. 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, 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 marketing and product teams that need authentic community opinions. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Gigabrain, Reddit Answers, Reddit Scout, LLMHub, Explore AI, Pigeon and Felo, plus manual browsing and generic search tools. Compare this product with the buyer's present method on research hours per accepted answer 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 and summarization attempts, transcript 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 the source-linked summary with citations and reviewer approval. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Reviewers approve substantive claims and publication scope. Public community content only; no private or restricted data. Final claims and publication decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.