AI Application Development
AI-first products, built end to end
Products where AI is the core — RAG systems, autonomous agents, and fine-tuned models — designed, built, and deployed.
This is for products where the AI is the point, not a feature — a research assistant that reasons over thousands of documents, an agent that completes multi-step tasks across your tools, or a domain model fine-tuned on your data. We design the full system: retrieval, orchestration, evaluation, and the interface a real user actually works in.
We build with production discipline that AI prototypes usually skip. That means a proper retrieval pipeline with chunking and re-ranking you can tune, an evaluation harness that catches regressions before users do, tracing on every agent step, and guardrails so an autonomous loop cannot run away with your API budget or do something it should not.
Timelines depend on scope, but we work in milestones: a working thin-slice prototype in the first few weeks, then hardening toward production. You always have something runnable to judge, and we are candid about where current models are reliable and where they still need a human in the loop.
Design the system
We decide the architecture up front — what to retrieve, when to call an agent, which model does what — and set the evaluation criteria so 'working' is defined before we build, not argued about after.
Thin-slice prototype
We ship the narrowest end-to-end version that proves the core loop on real data, measure it against the eval set, and use those results to decide what to invest in next.
Harden for production
We add tracing, guardrails, spend caps, caching, and fallback paths, then load-test the retrieval and agent layers so the system stays reliable and affordable under real traffic.
Deploy and monitor
We launch with dashboards for accuracy, latency, and cost, plus alerting on quality drift, and hand over a documented system your team can extend.
Do we actually need fine-tuning, or is prompting enough?
Usually prompting with good retrieval gets you most of the way, and we start there because it is cheaper and faster to iterate. We only recommend fine-tuning or adapters when the data shows it clearly wins on quality, latency, or per-request cost.
How do you keep an autonomous agent from going off the rails?
Every agent runs with step limits, tool allow-lists, spend caps, and full tracing, so it cannot loop forever, call tools it should not, or quietly burn your budget. High-stakes actions are gated behind confirmation or a human review step.
How do you prove the system is good enough to launch?
We build an evaluation harness against a labelled set of real cases early, so quality is a number we track across every change rather than a subjective judgement. You see accuracy, failure modes, and cost before anything ships to users.
READY TO BUILD?
LET'S SCOPE IT.
Tell us what you're building. We reply within 4 hours — no sales fluff, just a straight answer.