Pillar 01
AI Engineering
Designing and shipping the AI and agent systems your business runs on. Model selection, integration, evaluation, and the architecture that holds it together.
Senior Fractional Engineering
Enterprise AI Engineering, MLOps & AIOps — built on disciplined data engineering.
Architonomy is a senior fractional engineering practice. We design, build, and govern AI and agent systems in production — because your data is what determines whether any of it actually works.
01 · What we do
Three disciplines.
One foundation.
We work across three pillars of production AI — each one built on the same disciplined data engineering. The AI is the output. The data is the work.
Pillar 02
MLOps
The operational discipline beneath production AI. Deployment, monitoring, drift detection, and observability — the systems that catch problems before your customers do.
Pillar 03
AIOps
Applied AI for infrastructure and operations. Automated incident response, anomaly detection, and cost control. AI that works on the systems that run AI.
The Premise
99%
of agent work happens beneath the model
Your AI is only as good as your data.
Most AI projects fail not because the model is wrong, but because the data foundation beneath it was treated as someone else's problem. We treat data engineering as the discipline that makes the rest hold. That's the difference between a demo and a production system.
02 · How we evaluate
The Four C's of production AI.
Every engagement evaluates the system against four dimensions. Each one is a data question first, an AI question second. Most evaluations get one or two right. We work the full four.
Context — Does the AI have the right data, in the right shape, at the right time?
Control — Is the data governed so AI decisions can be audited, limited, and explained?
Cost — Does the data architecture scale economically as the workload grows?
Choice — Can the infrastructure flex across models, vendors, and deployment options?
03 · Who we serve
Engineering and data leaders putting AI into production.
Architonomy works best with organizations that already understand data matters. Our buyers are VPs of Data, Chief Data Officers, Directors of ML, and Heads of AI Engineering — typically in industries where production failure has real consequences.
- 01Healthcare & HealthTech
- 02Insurance
- 03Financial Services
- 04Public Sector / Federal
- 05Pharma & Life Sciences
- 06Legal & Compliance
04 · About
A practice built around what actually breaks in production AI.
More than fifteen years of the unglamorous engineering that makes AI hold in production — across regulated industries where the cost of getting it wrong is real.
More about the practice →- Experience
- 15+ years in production AI/ML
- Engagement
- One senior engineer — no account managers, no handoffs
- Scope
- The same person scopes the work, writes the code, and sets the monitoring
- Accountable
- To what runs in production, not to the deck
Get started
Book a 90-minute Architecture Diagnostic.
A focused call plus a written architecture assessment of a specific AI initiative. Identifies the three biggest production risks across Context, Control, Cost, and Choice — and what to do about them.