Review and voice-of-customer intelligence that ends the guesswork.
Unify reviews, tickets, surveys, returns, and social feedback into evidence-linked themes, quantified drivers, and owner-ready actions.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Feedback is abundant but fragmented; sentiment dashboards lack root cause, emerging defects hide in long-tail text, and teams debate anecdotes.
Product, merchandising, quality, support and marketing teams
Trigger to act: Product returns or negative reviews are rising, roadmap prioritization lacks evidence, or leadership needs a cross-channel customer truth layer.
The result you can model before you sign.
Illustrative only: identify one issue affecting 2,000 monthly units with $8 avoidable contribution loss and reduce it 10% = $1,600 monthly value. Treat this as a testable hypothesis, not a forecast.
The outcome, plainly: Unify reviews, tickets, surveys, returns, and social feedback into evidence-linked themes, quantified drivers, and owner-ready actions.
Inputs in. A cited, review-ready result out. Your expert decides.
A Evidence-grounded reporting agent. Every material fact is grounded in an allowed source and returned with its identifier, no invented data.
Claude Sonnet 4.6 or GPT-5.6 Terra for complex grounded work; select 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 managed vector store; Policy rules and evaluator service.
- Review text and ratings
- support tickets
- survey comments
- return reasons
- product/SKU
- cohort and date
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.
Analysts validate taxonomy and material findings; legal/PR reviews sensitive claims; representative quotes retain provenance and privacy controls.
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
500–5,000 report runs/month plus source queries, model use and export storage.
Planning assumptions, not vendor quotations. Your Review platforms 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. Analysts validate taxonomy and material findings; legal/PR reviews sensitive claims; representative quotes retain provenance and privacy controls. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: No deletion or suppression of legitimate criticism; no invented customer quote; no unreviewed public response; no sensitive-trait inference..
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 $400–3,600/month (500–5,000 report runs/month plus source queries, model use and export storage), plus a $2,000/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. The client needs an approved API/cloud billing account. Workspace seats are optional for internal prototyping and administrator access.
How is this different from Yotpo?
Tools like Yotpo, Bazaarvoice, Sprinklr 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’s live, and how do we prove it works?
This is a validate next. 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