Customer support that boosts CSAT, not just deflection.
Resolve approved order, product, policy, and account questions with live commerce actions while measuring recontact, not just deflection.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Agents repeatedly answer status, sizing, policy, and return questions while fragmented order context and weak escalation create poor customer experiences.
Customers, support agents, order operations and returns teams
Trigger to act: Ticket volume is outgrowing headcount, response times hurt conversion/retention, seasonal peaks create backlog, or the brand is replacing a brittle chatbot.
The result you can model before you sign.
Illustrative only: 20,000 tickets/month × 35% safely resolved × $5.50 avoided human handling cost = $38,500 monthly gross capacity before platform costs. Validate recontact and CSAT guardrails.
The outcome, plainly: Resolve approved order, product, policy, and account questions with live commerce actions while measuring recontact, not just deflection.
Inputs in. A cited, review-ready result out. Your expert decides.
A Permission-aware RAG copilot. 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, 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.
- Customer message
- Order/account context
- Product catalog
- Policies
- Shipping/returns status
- Prior conversation
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.
Humans handle refunds above thresholds, fraud, legal/safety complaints, angry or vulnerable customers, policy exceptions, and uncertain answers.
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
10,000 to 35,000 grounded questions per month plus indexing and vector search.
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. Humans handle refunds above thresholds, fraud, legal/safety complaints, angry or vulnerable customers, policy exceptions, and uncertain answers. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: No unbounded refunds or credits; no invented policy; no access without authentication; no success claim based only on deflection; no replacement of required human escalation.
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 $500 to $3,800 per month (10,000 to 35,000 grounded questions per month plus indexing and vector search), 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. The client needs an approved API/cloud billing account. Workspace seats are optional for internal prototyping and administrator access.
How is this different from Gorgias AI?
Tools like Gorgias AI, Zendesk AI, and Intercom Fin 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?
This is a launch now. We baseline “Resolved-without-human” 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