Where we came from
Ten years, three eras of AI, one team.
We did not arrive with the generative wave. We were building production ML before it, which is why our agentic work is engineered rather than assembled.
Classical ML and computer vision
The firm started in applied machine learning and vision — detection and counting on live highway camera feeds, satellite monitoring of industrial sites, and the data engineering underneath both. Work where accuracy is measured, not claimed.
Data platforms and decision support
Warehouse engineering, executive dashboards and BI delivery for operational businesses — the layer that makes everything downstream trustworthy.
Generative, agentic and regulated AI
Multi-agent systems on AWS Bedrock, retrieval-grounded regulatory drafting, natural-language analytics over enterprise warehouses, and the advisory work that decides how enterprises adopt all of it safely.
How we think
Six positions we hold, including the unprofitable ones.
01
The architecture matters more than the model
Models change every few months. Retrieval quality, evaluation discipline, data boundaries and cost control are what determine whether a system still works a year from now. We spend our time there.
02
A demo is not a system
Anything looks impressive on five hand-picked examples. We build against golden datasets, measure regression, and instrument production — because the failure modes only show up at volume.
03
We will tell you when not to use AI
Some workflows want a rules engine, a better form, or a fixed process. Recommending that costs us revenue and earns us the next three projects.
04
Domain depth beats generic capability
Knowing what a clinical study report contains, or why a container yard looks different month to month, is the part that cannot be prompted. We go deep in a few industries rather than shallow in all of them.
05
Your data stays yours
Zero-retention endpoints, no-training terms, redaction at the boundary and audit trails you can show a regulator. Designed in from the first architecture session.
06
Build so we can leave
Documentation, evaluation harnesses and training are part of delivery. The goal is a system your team owns — not a dependency on ours.
Recognition
Independently validated, not self-declared.
Semifinalist — NOVA AWS Challenge
AutoAdvisor multi-agent system
Nominee — Detroit Innovation Competition
AutoAdvisor multi-agent system
Listed on the Deloitte Marketplace
Validated enterprise readiness
Full detail on each of these sits in the case studies.
Talk to the people who would do the work.
No account managers between you and the engineers. The first call is with the people who would architect and build your system.