Solutions
Add AI to Your SaaS Product
Adding AI to a SaaS product means discovery, a working prototype, then production hardening with evals and monitoring — not a demo that only works once. We pair AI and full-stack engineers so the feature ships inside your actual product, not next to it.
The problem with most “add AI” projects
Most AI features start as a prompt wired directly into a chat box, tested against a handful of friendly examples, and shipped before anyone asks what happens when a user types something unexpected. It works in the demo. It becomes a support burden within a month of real traffic, because nobody built the evals that would have caught the failure modes before launch.
The approach: discovery, prototype, production
We run this in three stages, not as a single fixed-scope build:
Discovery. A short period scoping what the feature actually needs to do, which data it needs access to, and what “good” looks like for your specific product — before any code gets written.
Prototype. A working version inside your actual product, not a sandboxed demo, so you're evaluating the real experience from day one.
Production, with evals and monitoring. Before launch, we build a test set that scores outputs against good-answer criteria, so quality is measured rather than assumed. After launch, that same eval set tells you whether a prompt or model change made things better or worse.
Proof this works
OnSense AI is the clearest example on our site: AI agents built into real diagnostic and support workflows across a web dashboard and a mobile app, plus Appliance GPT, a standalone consumer AI chat product built solo in React and Next.js. Both are live products, not prototypes — which is the bar this process is built to clear.
A sample timeline by plan
40 hrs / month
A single, well-scoped AI feature — for example, AI-generated summaries on a support ticket or record, shipped with basic evals.
80 hrs / month
A feature with more surface area, like an in-app AI agent or a RAG-backed help system, plus the monitoring to run it with confidence.
160 hrs / month
Multiple AI features shipped and maintained as a standing part of your product, with ongoing eval and cost monitoring.
Proof from past work
FAQ
Frequently asked questions
Where do we start if we don't know what AI feature we need yet?
A scoping call. We talk through your product, your customers' actual problems, and where AI genuinely helps versus where it would just be decoration.
How do you avoid shipping an AI feature that only works in the demo?
Evals. Before a feature ships, we build a repeatable test set that scores real outputs against what a good answer looks like, so quality is measured, not assumed.
Do you handle monitoring after launch?
Yes — ongoing evals and cost monitoring are part of how we run AI features in production, not a one-time check at launch.
What if we already have an AI feature that isn't working well?
We can audit and rebuild it. This often looks like adding the evals and monitoring that were skipped the first time, or restructuring a prompt-only feature into a proper RAG or agent setup.
Which roles are involved in this work?
AI engineers for the model, prompt, agent, and eval work, and full-stack developers to wire it into your actual product — the dashboard, the API, the data it needs to see.