For retail and B2B commerce teams
Retail B2B & Wholesale

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.

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RAG Grounding Policy‑Safe Multilingual A/B Experiments Evaluation KPIs RAG Grounding Policy‑Safe Multilingual A/B Experiments Evaluation KPIs
Use cases

Where GenAI delivers value

A focused set of high‑ROI commerce scenarios we implement and measure.

PDP

Product content generation

Localized PDP copy, bullets and meta from specs and brand tone, with templates and approvals.

Search

Conversational search

Natural‑language discovery and answers grounded in your catalog.

Chat

Assistant with sources

Customer or sales assistant with citations and permission controls.

Merch

Merchandising & collections

Auto‑curate collections by theme, availability and margin.

Support

Support knowledge

Deflect tickets with policy‑aware answers from manuals and SOPs.

Capabilities

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.
Architecture

Reference architecture

Connect sources, ingest and chunk, create embeddings, retrieve context and generate outputs with guardrails. Measure KPI lift and iterate.

  1. Sources Catalog, PIM, docs
  2. Ingest Clean + chunk
  3. Embed Vectors
  4. Store Vector DB
  5. Retrieve Relevant context
  6. Generate LLM + guardrails
  7. Measure KPIs + evals
Delivery plan

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.

Next step

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