SmartDuke Technologies
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Solution·AI copilots·SaaS

AI copilots for SaaS.

Production-grade copilots engineered for SaaS companies.

In brief

SmartDuke builds ai copilots for SaaS companies — systems that augment users inside an existing product surface with grounded, in-context intelligence. Every SaaS shipped an 'AI feature' in 2025. The durable ones are now investing in production-grade infrastructure to keep them shipping. 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 SaaS companies are doing this now

The problems
we keep solving.

Every SaaS shipped an 'AI feature' in 2025. The durable ones are now investing in production-grade infrastructure to keep them shipping.

01 / 03

Onboarding scales poorly with headcount

Customer onboarding is a per-account workflow that doesn't compound — every new customer requires roughly the same human time.

02 / 03

Support ticket volume fragments across surfaces

Tier 1 tickets eat up engineering time; Tier 2 routing is opaque; customer-facing visibility is poor.

03 / 03

Power users want automation but admin tools are clunky

The customers most likely to expand are blocked by manual workflows your roadmap never prioritizes.

Use cases

Three things we'd build
first.

Concrete starting points for ai copilots in SaaS companies. We pick the one with the highest leverage and the cleanest measurement story.

  1. Idea 01

    Onboarding agent that walks new accounts through setup, surfacing relevant features based on use case

  2. Idea 02

    Support agent that resolves Tier 1 tickets and intelligently escalates the rest with full context

  3. Idea 03

    Reporting agent that generates weekly exec dashboards and answers ad-hoc questions over the data

Outcome metric → Time-to-value, support deflection rate, and expansion revenue per account
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 SaaS?

Discovery is one week. A working prototype (Spark) is 2–3 weeks. Full production Build for SaaS companies 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. SaaS 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 SaaS companies are tuned to your trends and pain points.

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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.