SmartDuke builds rag systems for SaaS companies — systems that ground AI answers in your own data, with citations and freshness guarantees. Every SaaS shipped an 'AI feature' in 2025. The durable ones are now investing in production-grade infrastructure to keep them shipping. 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.
Every SaaS shipped an 'AI feature' in 2025. The durable ones are now investing in production-grade infrastructure to keep them shipping.
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.
Support ticket volume fragments across surfaces
Tier 1 tickets eat up engineering time; Tier 2 routing is opaque; customer-facing visibility is poor.
Power users want automation but admin tools are clunky
The customers most likely to expand are blocked by manual workflows your roadmap never prioritizes.
Three things we'd build
first.
Concrete starting points for rag systems in SaaS companies. We pick the one with the highest leverage and the cleanest measurement story.
- Idea 01
Onboarding agent that walks new accounts through setup, surfacing relevant features based on use case
- Idea 02
Support agent that resolves Tier 1 tickets and intelligently escalates the rest with full context
- Idea 03
Reporting agent that generates weekly exec dashboards and answers ad-hoc questions over the data
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 SaaS?
How long does it take to build rag systems 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.
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. SaaS 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 SaaS companies 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.