Solutions / Ship AI Features
Ship AI Features, Not AI Demos
You have an AI feature on the roadmap and a folder full of demos that did not survive the trip to production. Product leaders, CTOs, and AI leads come to us when the model is the easy part and the product around it is the work. We bring the product judgment to pick the right AI surface, the agentic delivery practice that ships it, and the engineering discipline that keeps it working after the demo is over.
3 weeks
Concept to AI feature in production (Nike, BuildCo)
Day one
Evals, observability, and human-in-the-loop, included not bolted on
How we ship AI features
The expensive failures we see are not model failures. They are surface failures, evals failures, and discipline failures. The work below is what turns a demo into something your users actually use.
Pick the right AI surface
Not every problem wants an LLM, and not every LLM wants a chat UI. We help you pick the surface that fits the user job: an in-product agent, an inline action, RAG over your docs, or a multi-agent backend. The expensive failures we see are clients who shipped a chatbot when they needed an embedded action, or built a sidebar when the work belonged inside the main flow.
Built like a feature, not a demo
Real AI features need evals, telemetry, fallbacks, prompt and model versioning, and a human-in-the-loop story. We design those into the first release because we know what breaks at month two. The result is a feature your team can debug at 2am and your users can trust the rest of the time.
Multi-agent when it earns its keep
Single LLM calls cover most product features. Multi-agent systems with orchestrated workflows (Temporal, event-sourced state) are the right answer when the work actually needs durable agent execution. We can do both. The discipline is choosing the smallest answer that fits the problem.
From AI demo to AI feature
Most AI features die between the demo and the second week of production. The demo works on the happy path with a clean prompt and a fast network. Then the model has a bad day, the user hits a request the prompt did not anticipate, telemetry is silent, and there is no fallback. The demo never had evals, the prompt was tuned by feel, and nobody owns what "good" means a month from now. The product team learns the AI was a feature in name only.
We work the seam between the demo and the production version. We start where the user job lives, not where the model lives. We pick the AI surface that fits the work, build the evals that define what good looks like, ship the observability that tells you when good drifts, and design the human-in-the-loop story for the cases the model gets wrong. The first release earns its keep with users. The second release is a feature push, not a rewrite.
AI features we've shipped
Three weeks to launch a scalable AI-powered marketplace solution with secure governance
In just three weeks, we built and deployed a secure human-in-the-loop matchmaking system for a service marketplace platform. Using generative AI and an open-source AI governance platform, the solution minimizes operational costs, accelerates lead response times, and scales without increasing headcount, with oversight, traceability, and control at its core.
Case Study
Nike
How We Built Nike's Personalized AI Shopping Assistant in Just 3 Weeks
Discover how Nike minimized initial costs and achieved GenAI readiness with a lean, agile approach. Learn how.
Case StudyCapabilities behind the work
Agentic AI
Agentic Solutions
Apply AI to product problems. Agents, workflows, and smart experiences that reset customer expectations.
See how we help
Agentic Product Engineering
Agentic Product Engineering
AI-augmented engineering workflows that ship production systems in weeks, not quarters.
See how we help
New Product Development
Product Development
Launch new products with a traction-first approach to discovery, delivery, and growth.
See how we helpIf your AI problem is shaped a little differently
AI feature work sits next to a few adjacent problems we hear about in the same breath. If your starting point is closer to one of these, begin there and we will pull the AI feature work in when it lines up.
Greenfield product where the model is the actual product?
Launch new products
Stack can't carry AI features without a rewrite-everything plan?
Modernize your stack
AI features that need a substrate you actually own?
Achieve sovereign architectureWant AI feature work flowing through an ongoing maintenance loop, not a separate AI project?
Agentic product evolution
AI feature in a regulated market where the audit comes before the launch?
Ship AI through complianceGot an AI feature stuck in demo?
Bring us the demo. We'll bring it to production.
Tell us the AI surface you're trying to build, the user job it has to do, and what is blocking the production version. We'll come back with an honest read on whether the answer is a different surface, a different model, or just the discipline to ship it. No deck-ware.
Talk to us