A working catalog of what we build.

We work alongside your team on the questions that benefit most from outside perspective: architectural reviews when a system is about to be scaled, technical diligence when something is being acquired, and rescue work when an in-flight project has lost its way.

Where appropriate we go deeper — embedding for a stretch as the senior engineer the work needs, leading a rewrite, or owning a difficult subsystem end-to-end. The goal is always to leave your team stronger than we found it, with documents, tests, and decisions they can defend.

Past engagements have spanned the load-bearing work that quietly holds modern systems together — event- and queue-driven architectures, high-volume payment processing, idempotent request handling, and the back-pressure and retry semantics that keep all of it honest under load — alongside the newer shape of work: agentic systems, retrieval pipelines, and the evaluation harnesses that keep them safe to ship. We're language-agnostic on principle; the right tool for a system is the one the team operating it will still understand years from now.

We design and deliver intelligent control systems for large resort-style homes — estates, compounds, and primary residences where lighting, climate, security, audio/video distribution, and an emerging layer of on-premise AI all need to feel like a single, calmly orchestrated property.

Our work begins where most installers stop. A coordinated bus design that spans dozens of rooms and outbuildings. Multi-zone audio and 4K/8K video distribution that hands off cleanly between cinema, lounge, and pool deck. Security as a calm system — cameras, access, and intrusion under one roof, visible from a single pane. And an on-premise intelligence layer that learns the household's rhythms, anticipates them, and stays useful when the internet doesn't.

We bias toward local-first stacks — open protocols, on-prem hubs, and documented subsystems — so a property built today is still serviceable a decade or more from now, by a different team, regardless of which cloud vendor is still in business.

We bridge the gap between the controls cabinet and the cloud. Our work spans new line builds, retrofits, and the kind of carefully sequenced upgrade that has to happen on a production floor that can't stop running.

Where a plant has a working PLC layer but no useful data, we add a clean instrumentation tier — Sparkplug or OPC-UA up to a historian, a unified namespace, and the few dashboards an operator actually opens. Where the controls layer itself needs work, we'll touch that too, with the change-management discipline that real factories require.

Before a single piece of equipment is ordered, the hardest engineering work has already been done — or it hasn't. We help capital projects get this part right. A master plan from us is a written, defensible document: site layout, phased rollout, vendor strategy, schedule, budget, and risk register, all anchored in a clearly stated set of objectives.

Plans are written to be operated from. They're indexed, versioned, and the decisions they encode are traceable to the people and constraints that drove them.

Sometimes the right move is not "build it" but "find out". A design study is a short, fixed-scope engagement that ends in a written recommendation backed by prototypes, measurements, costed options, and the trade-off table you can take into a boardroom.

Studies are also the right way to start a larger engagement at low risk — they let both sides discover whether the work is real, whether the team is right, and what the implementation will actually require.

We take on the kind of research where the success criterion is operational: a sensor that survives the environment, a perception stack that classifies correctly under stress, an instrumentation system that yields a clean dataset. Our research engagements are small, multidisciplinary teams operating with a written hypothesis, a budget, and a clear go/no-go cadence.

Where appropriate we collaborate with academic groups and contribute back — publications, open-source releases, public datasets — but the primary deliverable is always something the client can deploy or build on.

Most organizations don't need another generic chatbot pilot. They need a sober answer to a harder question: which parts of our work can an LLM or agent reliably do, what does it cost to operate, and how do we keep it safe, auditable, and within policy? We help companies answer that — then build the first deployment that earns the second one.

Our work spans the full arc: strategy and use-case selection, agent architecture (retrieval, tool-use, planning, supervision), prompt and eval engineering, safety and red-team review, and the integration work that gets a model into the systems your team actually uses. We pay equal attention to the unglamorous half — observability, cost ceilings, prompt versioning, golden-set evaluations, and the runbooks for when the model misbehaves.

For regulated industries we also handle the compliance layer: data-handling review, model-card and lineage documentation, alignment with the major AI governance frameworks your buyers and regulators care about, and the audit trail that lets your legal team sleep. Where the data is too sensitive for a public API, we deploy on-premise — open-weight models, private inference, and an evaluation harness your team can run themselves.