SmartDuke Technologies
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AI copilots for E-commerce.

Production-grade copilots engineered for e-commerce and retail.

In brief

SmartDuke builds ai copilots for e-commerce and retail — systems that augment users inside an existing product surface with grounded, in-context intelligence. Conversational commerce isn't hype anymore — agents are driving GMV in stores that have invested in production-grade AI infrastructure. Our approach: Latency budgets enforced from day one, retrieval tuned to in-product context, and copilot UX co-designed with your product team — not parachuted in.

Why e-commerce and retail are doing this now

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.

01 / 03

Product discovery breaks at scale

Beyond a few thousand SKUs, traditional search and filters lose to a conversation that understands intent.

02 / 03

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.

03 / 03

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.

Use cases

Three things we'd build
first.

Concrete starting points for ai copilots in e-commerce and retail. We pick the one with the highest leverage and the cleanest measurement story.

  1. Idea 01

    Product-finder agent that asks clarifying questions and recommends across catalog with reasoning

  2. Idea 02

    Review-summary copilot that synthesizes customer feedback into structured pros/cons per SKU

  3. Idea 03

    CX agent that handles 80% of inbound and intelligently escalates the rest with full conversation context

Outcome metric → Conversion rate, support-deflection rate, and average order value
How we engineer it

Production-grade,
from day one.

Tool-calling against the product API, RAG against in-product context, and structured outputs that the UI can render natively rather than as walls of text.

01 /04

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.

02 /04

Telemetry from day one.

Traces, latency budgets, token costs, and error rates wired up before the first user touches the system.

03 /04

Guardrails as architecture.

Input validation, output verification, escape hatches, and human handoff paths designed in — not bolted on after incidents.

04 /04

Boring stack on the edges.

Cutting-edge model in the middle. Reliable infrastructure around it. Stability where it earns its place.

Common failure modes we engineer against
  • ×Slow latency that breaks the typing flow
  • ×Generic responses that don't use product context
  • ×No grounding, leading to user trust collapse
  • ×Poor UX integration that feels bolted on
Vercel AI SDKSupabase + pgvectorClaude Sonnet 4.6Braintrust
FAQ · 04

Common questions.

01

How long does it take to build ai copilots 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.

02

What does pricing for ai copilots 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.

03

Can you take over an existing copilots 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.

04

What's different about your approach to ai copilots?

Latency budgets enforced from day one, retrieval tuned to in-product context, and copilot UX co-designed with your product team — not parachuted in. 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.

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