SmartDuke builds rag systems for e-commerce and retail — systems that ground AI answers in your own data, with citations and freshness guarantees. Conversational commerce isn't hype anymore — agents are driving GMV in stores that have invested in production-grade AI infrastructure. Our approach: Retrieval evals before any LLM call, citation-rate monitored as a first-class metric, and explicit refusal when confidence falls below threshold.
The problems
we keep solving.
Conversational commerce isn't hype anymore — agents are driving GMV in stores that have invested in production-grade AI infrastructure.
Product discovery breaks at scale
Beyond a few thousand SKUs, traditional search and filters lose to a conversation that understands intent.
Customer support volume scales with order volume
Returns, sizing, fit, availability — most CX questions are repetitive and well-suited to AI handling with human escalation.
Personalization is broken without context
Email blasts and 'recommended for you' miss the mark when the system doesn't know what the customer is actually trying to accomplish.
Three things we'd build
first.
Concrete starting points for rag systems in e-commerce and retail. We pick the one with the highest leverage and the cleanest measurement story.
- Idea 01
Product-finder agent that asks clarifying questions and recommends across catalog with reasoning
- Idea 02
Review-summary copilot that synthesizes customer feedback into structured pros/cons per SKU
- Idea 03
CX agent that handles 80% of inbound and intelligently escalates the rest with full conversation context
Production-grade,
from day one.
Hybrid retrieval (BM25 + semantic + reranker), source-priority ranking, freshness gating, and answer-capsule generation with sentence-level citations.
Evals before launch.
Every loop, tool call, and structured output is graded with a frozen test set and an explicit rubric. Failed evals block the deploy.
Telemetry from day one.
Traces, latency budgets, token costs, and error rates wired up before the first user touches the system.
Guardrails as architecture.
Input validation, output verification, escape hatches, and human handoff paths designed in — not bolted on after incidents.
Boring stack on the edges.
Cutting-edge model in the middle. Reliable infrastructure around it. Stability where it earns its place.
- ×Hallucination on long-tail queries
- ×Stale documents silently used
- ×Missing or wrong citations
- ×Retrieval that fails on the queries that matter most
Common questions.
01How long does it take to build rag systems for E-commerce?
How long does it take to build rag systems for E-commerce?
Discovery is one week. A working prototype (Spark) is 2–3 weeks. Full production Build for e-commerce and retail typically runs 8–12 weeks depending on data complexity, integrations, and compliance scope. We commit to a precise timeline at quote stage.
02What does pricing for rag systems typically look like?
What does pricing for rag systems typically look like?
Every engagement is scoped to outcomes, not hours. Discovery starts in the low four figures. Spark and Build are priced per project. Embed retainers are monthly. We return a quote within 24 hours of inquiry.
03Can you take over an existing RAG project that's stalled?
Can you take over an existing RAG project that's stalled?
Yes — it's a common engagement. We review what's there, tell you honestly what stays and what we'd rebuild, then ship it to production. E-commerce engagements often start this way.
04What's different about your approach to rag systems?
What's different about your approach to rag systems?
Retrieval evals before any LLM call, citation-rate monitored as a first-class metric, and explicit refusal when confidence falls below threshold. We hold the same engineering bar across every engagement, regardless of industry — but the specifics for e-commerce and retail are tuned to your trends and pain points.
Same capability, different industry.
Same industry, different capability.
Have an AI product
that needs to ship?
Tell us where you are — early concept, broken prototype, or scaling something that already works. We'll come back within 24 hours with a take and a quote.