Product feedback intelligence that ranks the roadmap without guessing.
Unify feedback into evidence-linked themes, quantify affected accounts and revenue context, and route validated opportunities to product owners.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Feedback is fragmented across tickets, calls, CRM, surveys, reviews, and community; loud anecdotes overpower broad signals and duplicate requests.
Product managers, researchers, support, sales and CS teams
Trigger to act: Roadmap debates lack evidence, enterprise requests are hard to quantify, feedback volume is growing, or leadership wants faster closed-loop learning.
The result you can model before you sign.
Illustrative only: 15 product managers × 6 research/synthesis hours removed monthly × $85 loaded hourly cost = $7,650 monthly capacity. Better roadmap decisions require outcome tracking, not just theme volume.
The outcome, plainly: Unify feedback into evidence-linked themes, quantify affected accounts and revenue context, and route validated opportunities to product owners.
Inputs in. A cited, review-ready result out. Your expert decides.
An evidence-grounded reporting agent. Every material fact is grounded in an allowed source and returned with its identifier, with no invented data.
Claude Sonnet 4.6 or GPT-5.6 Terra for complex grounded work, selected by task-level evaluation; Gemini 2.5 Flash, GPT-5.4 mini, or Claude Haiku 4.5 for high-volume routing and drafting. PostgreSQL + pgvector or a managed vector store; policy rules and an evaluator service.
- Feedback text
- Account / segment
- ARR or usage context
- Product area
- Date
- Source
AI-Native, not autonomous. Judgment stays with your people.
The machine does the work; the human’s role narrows to the one thing that matters, judgment. That constraint is what makes it safe to deploy.
Product/research teams validate theme and interpretation; customer quotes preserve source and permissions; no roadmap priority is automated.
A scorecard, not a demo. We baseline what breaks in production.
Every deployment ships with an evaluation suite. These are the numbers we baseline before launch and monitor after.
The category is crowded. Most of it isn’t built for your workflow.
Fixed-scope, tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in from day one. A price you own, not a subscription you rent.
It plugs into the stack you already run.
No rip-and-replace. Access is scoped to the minimum data necessary, isolated per tenant, and fully logged.
Transparent by design. The build price buys the workflow and the proof.
A fixed implementation fee plus a monthly bill that scales with volume and governance. No hidden seats.
- ✓ One process / scope
- ✓ Live workflow on your data
- ✓ Baseline evaluation suite
- ✓ Measured vs. current process
- ✓ Full scope & integration
- ✓ Human-review UI & audit trail
- ✓ Write-back to your systems
- ✓ Production evals & monitoring
- ✓ Multi-facility rollout
- ✓ Advanced security & compliance
- ✓ Custom control & escalation
- ✓ Dedicated evaluation program
500 to 5,000 report runs per month, plus source queries, model use and export storage.
Planning assumptions, not vendor quotations. Your help desk and other platform licenses are separate and owned by you. Figures confirmed during scoping.
“His vast knowledge of technologies and a natural problem-solving mindset consistently lead us through complex challenges with clarity and confidence.”
Questions serious buyers ask.
Does the AI act on its own?
No. Product/research teams validate theme and interpretation; customer quotes preserve source and permissions; no roadmap priority is automated. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: no fabricated quote; no automatic roadmap priority; no treating ARR concentration as universal user need; no exposure of restricted account data.
How do you stop it inventing facts?
Every material claim is grounded in an allowed source record and returned with its source identifier. The system separates observed facts, model inference, and missing information, and routes to a human whenever confidence is low, evidence conflicts, or an adverse outcome is possible.
What does it cost to run each month?
A usage bill of roughly $400 to $3,600 per month (500 to 5,000 report runs per month plus source queries, model use and export storage), plus a $2,000 per month managed retainer for evaluation, monitoring and maintenance. Your existing platform licenses are separate and already yours. Exact figures are confirmed during scoping.
Do we need a ChatGPT or Claude subscription?
No consumer ChatGPT or Claude subscription is required for the production workflow. You need an approved API/cloud billing account. Workspace seats are optional for internal prototyping and administrator access.
How is this different from Productboard?
Tools like Productboard, Dovetail, and Enterpret are broad platforms you adapt to. This is a fixed-scope implementation tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in, and a transparent price instead of a seat subscription.
How long until it is live, and how do we prove it works?
We baseline theme precision first, then measure against that baseline. You see the scorecard before expanding scope: the evaluation suite ships with the system, not as an afterthought.
Bring your real numbers. Leave with a fixed-scope plan.
A 30-minute engineering-led working session, no slideware. You leave with a sized opportunity estimate, a fixed-scope pilot plan, and the integration & human-review path mapped.
VP of Growth at ViitorCloud · senior delivery owner confirmed before paid work