Service — Applied AI
Applied AI product engineering
We build AI features that survive contact with production: LLM workflows, agents, browser automation, evaluations, permissions, cost controls, and the product surface around them.
AI tied to a workflow
We start with the business workflow, not the model. If AI does not change throughput, quality, or cost, it does not belong in the product.
Agent and tool architecture
Typed tools, permission boundaries, human approvals, replayable runs, and browser-control surfaces that make AI systems easier to inspect and operate.
Evaluation before scale
Prompts are not a reliability plan. We add test cases, review loops, fallback behavior, and cost visibility before usage grows.
Product engineering around the model
The useful work is rarely just the model call. We build the UI, backend, data flow, audit path, and operational controls around it.
Frequently asked questions
Which AI models and providers do you work with?
OpenAI, Anthropic, and open-source models — chosen per use case for quality, cost, latency, and privacy. We are not locked to a single vendor.
Can you integrate AI into our existing product?
Yes. Most of the useful AI work is embedding LLM and agent workflows into products that already exist without forcing a rewrite.
How do you keep AI features reliable and on-budget?
Evaluation, fallbacks, permissions, caching, review paths, and cost monitoring from day one — so quality and spend stay predictable as usage grows.
Have an AI workflow worth shipping?
Book a strategy call and we'll tell you what is realistic, what should stay manual, and what it takes to ship safely.