Semantic search and a shopping assistant that end the dead-end query.
Understand natural-language shopping intent, retrieve eligible products, explain fit, and guide shoppers to the right PDP or cart without fabricating product facts.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Keyword search fails on conversational needs, attributes are incomplete, zero-result queries hide demand, and shoppers bounce when they cannot translate a need into filters.
Shoppers, merchandisers, site-search analysts, customer service
Trigger to act: Search exit or zero-result rates are high, catalog complexity is growing, agentic-shopping traffic is emerging, or the brand needs differentiated discovery.
The result you can model before you sign.
Illustrative only: $3 million monthly search-attributed GMV × 0.25% incremental conversion × 45% contribution margin = $3,375 monthly contribution. Run randomized experiments before scaling.
The outcome, plainly: Understand natural-language shopping intent, retrieve eligible products, explain fit, and guide shoppers to the right PDP or cart without fabricating product facts.
Inputs in. A cited, review-ready result out. Your expert decides.
A Semantic search + shopping copilot. Every material fact is grounded in an allowed source and returned with its identifier, with no invented data.
Hybrid lexical + vector retrieval with a reranker; Gemini 2.5 Flash or GPT-5.4 mini for conversational answers; a stronger model is invoked only for complex comparison or policy questions. OpenSearch/Elasticsearch or Algolia; embedding service; catalog policy filter.
- Query and session context
- Product attributes
- Inventory
- Price
- Compatibility
- Reviews
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.
Merchandisers control ranking policies and exclusions; safety-sensitive or regulated recommendations escalate or use approved deterministic rules.
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
100,000 to 1,000,000 catalog/search events per month plus embeddings, reranking and model answers.
Planning assumptions, not vendor quotations. Your commerce platform 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. Merchandisers control ranking policies and exclusions; safety-sensitive or regulated recommendations escalate or use approved deterministic rules. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: no fabricated compatibility or availability; no undisclosed paid ranking; no autonomous regulated-product advice; no promise that conversational search always improves conversion.
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 $850 to $6,800 per month (100,000 to 1,000,000 catalog/search events per month plus embeddings, reranking and model answers), plus a $2,200 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 Constructor?
Tools like Constructor, Algolia, and Bloomreach 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 “Search conversion” 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