Productized engagements with explicit scope and timelines. Pick the one that matches where you are. Every quote returns within 24 hours.
An independent look at your AI product. Architecture, evaluation gaps, and a practical plan for what comes next.
Validate the opportunity, define the experience, and map the architecture. Leave with a clear scope and build estimate.
A working prototype to put in people’s hands, with an evaluation harness and a roadmap to production.
A complete AI product, designed and engineered for real users. Integrated, evaluated, and ready to launch.
Your dedicated AI engineering partner for continuous improvement, new capabilities, and life after launch.
Each engagement is a clear contract. Same disciplines applied; different scope and depth.
SmartDuke designs, engineers and operates custom AI products, including copilots and internal tools. Start with the engagement that matches the stage of your project.
Discovery covers use-case validation, system architecture, an evaluation strategy and a build estimate. If you need a working prototype before committing to production, Spark includes an evaluation harness and a go-to-production roadmap.
Our SaaS copilot approach combines tool-calling against the product API, retrieval from in-product context and structured outputs that the interface can render. Build is the production engagement, with evaluations, observability and guardrails.
Explore how we build copilots for SaaSBuild ends with documentation, evaluation suites, observability dashboards and an operating runbook. For ongoing engineering support, Embed includes evaluation and incident monitoring, continued capability development and quarterly architecture reviews.
Whichever tier you pick, these are the disciplines we hold ourselves to. Not optional. Not extra cost.
Diagrams, decision logs, and a runbook your team can operate without us.
Frozen test sets, scoring rubrics, and pass/fail thresholds — handed over with the code.
Input validation, output checks, escape hatches, and human handoff paths.
Auth, secrets, data classification, RBAC, and audit logs by default — not as add-ons.
What shipped, what didn't, what we learned, what's next. No corporate fluff.
Code, infra, dashboards, and the full operating mental model — transferred to your team.
Four engineering disciplines we apply to every product we ship — independent of model, framework, or industry.
Every AI product ships with a graded eval suite — unit tests on key behaviors, LLM-as-judge regression scoring, and human review samples. If it can't pass eval, it doesn't ship.
Traces, latency budgets, token costs, error rates — wired up before the first user touches the product. You can't fix what you can't see.
Input validation, output verification, escape hatches, and human handoff paths designed in — not bolted on after the first incident.
Cutting-edge model in the middle. Reliable, well-understood infrastructure around it. Novelty where it earns its place; stability everywhere else.
A new idea. An unsolved problem. A next chapter.
We’d love to hear what you have in mind.
