Generative AI for Commerce — measurable outcomes, grounded in your data
Product descriptions, collections, assistants and guided workflows. Practical delivery with RAG grounding, governance and KPI tracking.
Where GenAI delivers value
A focused set of high‑ROI commerce scenarios we implement and measure.
Product content generation
Localized PDP copy, bullets and meta from specs and brand tone, with templates and approvals.
Conversational search
Natural‑language discovery and answers grounded in your catalog.
Assistant with sources
Customer or sales assistant with citations and permission controls.
Merchandising & collections
Auto‑curate collections by theme, availability and margin.
Support knowledge
Deflect tickets with policy‑aware answers from manuals and SOPs.
Key capabilities
Grounded with RAG, governed with policies, measured against KPIs.
- ✔ Model choice: OpenAI, Gemini or local — per use case.
- ✔ Grounding via retrieval (RAG) with citations and policy rules.
- ✔ Evaluations: accuracy, faithfulness, latency and KPI impact.
- ✔ Privacy & governance: redaction, role scopes, audit logs.
- ✔ Ops: monitoring, budgets, rate limiting and graceful fallbacks.
- ✔ Experimentation: prompt/version A/B with rollbacks.
- ✔ Outcomes: conversion lift, AOV, deflection and time‑to‑publish.
Reference architecture
Connect sources, ingest and chunk, create embeddings, retrieve context and generate outputs with guardrails. Measure KPI lift and iterate.
From idea to impact
Four phases from discovery to scale, each tied to measurable KPIs.
- 1
Discover
Audit data and processes; define KPI and candidate use cases.
- 2
Design
Pick models, grounding and guardrails; set evaluation plan.
- 3
Pilot
Ship a 4–6 week experiment and measure KPI movement.
- 4
Scale
Harden, automate and expand to new SKUs or markets.
Let’s design a GenAI pilot with real KPIs
We scope a 4–6 week pilot with a single KPI (conversion, AOV, deflection or time‑to‑publish), then scale what works.
Plan your GenAI pilot