SmartDuke Technologies
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Solution·AI agents·SaaS

AI agents for SaaS.

Production-grade agents engineered for SaaS companies.

In brief

SmartDuke builds ai agents for SaaS companies — systems that autonomously complete multi-step workflows that previously required human orchestration. Every SaaS shipped an 'AI feature' in 2025. The durable ones are now investing in production-grade infrastructure to keep them shipping. Our approach: Eval coverage on every loop, observability into every tool call, and explicit guardrails around action surfaces before the agent ever touches production.

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 agents 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.

We use planner-executor, ReAct, or supervisor-worker loops depending on the problem shape — and resist multi-agent orchestration when a single-agent loop solves it.

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
  • ×Runaway loops on ambiguous inputs
  • ×Tool-call failures invisible to standard APM
  • ×Context truncation as conversations grow
  • ×Hallucinated actions when grounding is weak
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FAQ · 04

Common questions.

01

How long does it take to build ai agents 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 agents 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 agents 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 agents?

Eval coverage on every loop, observability into every tool call, and explicit guardrails around action surfaces before the agent ever touches production. 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?

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