Industry-agnostic machine learning and AI pipelines from firmware to the cloud.
Custom engineering, end to end: from first prototype to production systems your team can run on its own.
Systems built around large language models: knowledge management, product oracles that know your product cold, chat bots, and content generation. Each ships with the retrieval and evaluation plumbing underneath.
LLM Orchestration / RAG / Agents / Evals
Hard as it is to believe, many problems are still best solved with traditional machine learning: no slow, expensive LLM required. Classifiers, forecasters, and anomaly detectors that run in milliseconds on hardware you already own.
Classification / Forecasting / Anomaly Detection / Recommendation
Legacy codebases ported to new languages and architectures, with inputs and outputs validated against the original at every step so behavior survives the move. Then the extensions: cleaner interfaces, test coverage where there was none, new capability layered onto software you already rely on.
Incremental Migration / Behavioral Validation / Test Harnesses / API Design
Digging into the shape of your data to find what is buried in it: profiling, exploratory analysis, and the unglamorous cleaning that real answers depend on. When a one-off analysis needs to become routine, it gets a custom transformation pipeline built around it.
Exploratory Analysis / Data Cleaning / Transformation Pipelines / Reporting
From feasibility study to working prototype in weeks: model selection, benchmarking against your real data, and honest answers about what the current state of the art can and cannot do for your problem.
Prototyping / Benchmarking / Model Selection / Due Diligence
Vibe-coded messes untangled, no judgement. When AI-generated code has grown past what anyone can safely change, the job is to work out what it actually does, pin that down with tests, and restructure it so people - and their agents - can build on it again.
Code Archaeology / Test Coverage / Refactoring / Documentation
Software for hardware: embedded firmware, motion control, sensor fusion, and the device-to-cloud plumbing in between. The work reaches down to the timing-and-interrupts level and up to the fleet dashboard, including the models that run on-device.
Embedded C / RTOS / Sensor Fusion / Edge Inference
Every engagement, three commitments.
What the system is supposed to do gets written down as tests, run against real data, and proven again on every change. Correctness is demonstrated, never assumed.
Inputs go bad, networks drop, hardware dies. Systems here expect that: they degrade gracefully, recover on their own, and say plainly what went wrong.
The simplest design that solves the problem, with clean seams where the next capability will attach. Your team can extend it without a rewrite.
Procyon is an engineering consultancy in Southern California, built on more than twenty years of shipping intelligent systems and the infrastructure under them. The method does not change: understand your problem first, then design the simplest, most performant solution possible. Much of the work lands in software you already run - thinning out accumulated complexity, hardening what fails, and leaving deployments easier to extend than they were found. The finished system arrives documented and tested, and it is entirely yours: your team runs it, changes it, and builds on it without calling anyone.