What we build.
Four practices, one standard: if we wouldn't run our own company on it, we don't ship it.
AI & LLM Engineering
Language models are genuinely useful — once they're wired into your business properly. We design and build that wiring.
- Assistants that answer from your documents, policies, and data — with sources, not guesses
- AI features integrated into your existing product or internal tools
- Document intelligence: reading, extracting, and validating what arrives as forms, PDFs, and scans
- Agent workflows that carry a task end-to-end with human checkpoints where they belong
- Local and offline AI: open-weight models running on your own hardware — up to fully air-gapped — when the data must never leave the building, and edge inference where latency or connectivity demands it
Every build ships with evaluation and guardrails — accuracy is a feature we engineer, not an accident we hope for.
Business Automation
Most businesses lose hours daily to work a system should be doing: copying data between tools, chasing follow-ups, assembling the same report again.
- Intake automation — leads, orders, applications captured, qualified, and routed the moment they arrive
- Back-office workflows — documents, approvals, scheduling, reporting on rails
- Follow-up sequences that never forget, with a human approving what goes out
We use AI where it earns its place and deterministic software where reliability matters more than cleverness. You'll always know which is which.
Software & Data Engineering
The unglamorous truth: most "AI projects" fail on ordinary engineering. Data lives in six tools, nothing has an API, nobody trusts the numbers. We fix that layer.
- Data pipelines that consolidate scattered sources into something queryable and trustworthy
- APIs and integrations between the tools you already run
- Internal dashboards and tools your team actually opens
Applied R&D & Advisory
Every vendor says you need AI. We'll tell you where that's true — and where it isn't.
- A structured evaluation of your operations: where AI helps, where plain software wins, where neither is worth it
- A working prototype of the highest-value use case, so the decision is made on evidence
- A roadmap with verdicts and honest costs — not a slide deck
Three steps, no surprises.
1. A conversation
You describe the problem in plain language. If we're not the right fit, we say so in the first call.
2. A scoped build
Defined outcome, defined price, written plan. No open-ended billing.
3. Operate & improve
Systems live in the real world; we stay accountable for ours after they ship.