AI & Agentic Systems
Real agent architecture, not a chatbot bolted on
Most 'AI integration' work bolts a chatbot onto an existing product. We build the underlying agent architecture — deterministic logic where it matters, language models where they add real value, and a clear boundary between the two.
Our point of view
The mistake we see most often is handing judgement to a language model that should live in code. In our own on-device assistant, the significance judgement — what matters, and what to do about it — is deterministic; the model only phrases the output. That boundary is the difference between an AI feature you can trust and a demo that impresses once and fails quietly in production. We design that boundary deliberately, then build on the right side of it.
What we build
Agent architecture
The system underneath the AI — how tasks are decomposed, when tools are called, and where a human stays in the loop — designed for reliability, not demos.
LLM integration
Language models embedded where they add genuine value inside a product, with the prompts, guardrails, and evaluation to keep outputs trustworthy.
Automation pipelines
Agentic workflows that do real work — qualifying leads, keeping records clean, drafting documents — wired into the systems you already run.
On-device & private AI
Local model deployment for cases where data cannot leave the device, drawing on our own on-device assistant work.