AI agents that automate real work, on infrastructure you control.
We build LLM-driven agents that do specific things: triage support tickets, summarise contracts, run a first pass on data quality. They run on your infrastructure or a cloud account you control, against models and policies you can audit.
We do not build general assistants. Every agent we ship has a defined task, a measurable success metric, and a fallback for when it gets it wrong.
Chat and voice agents on top of Claude, GPT, or open-weight models. We pick the model based on the task, not the brand. Retrieval-augmented generation against your data, with citations the user can click through to.
Agents that run on a schedule or a queue: monitor a stream, take an action, log what they did. Wired into your existing tooling, not a separate platform with its own login.
Fine-tuning when the base model fails on your domain. Prompt engineering and eval datasets first, because most "we need fine-tuning" conversations end with a better prompt and a retrieval index.
We ship with an eval harness so you can see whether the agent improved when we update its prompt or model. Rollout is gradual: shadow mode, then a percentage of traffic, then default.
Self-hosted on AWS, GCP, Azure, or your bare metal. Logs, traces, and cost dashboards are part of the deliverable, not an afterthought.
Production web apps in React and Node. Handed off ready to run.
iOS and Android in Flutter, from a single codebase.
Marketing sites and brochureware that hold up after launch.
Bespoke software when off-the-shelf won't fit. Node, NestJS, Laravel.
Outside opinion on stack, architecture, and team. We say what we see.
Smart contracts and on-chain apps when the use case calls for it.
SEO, paid, and content. We measure what we ship.
Interface design that survives contact with real users.
Get a ballpark estimate in 60 seconds — cost, timeline, and team size.
