AI that survives contact with your enterprise.
Most AI programs stall between the pilot and production. Automa Consulting closes that gap — architecting agentic systems, automation, and data foundations that hold up under real governance, real security review, and real users.
Where we're useful
Engagements are scoped small and deliberately: a decision to make, a system to design, a team to bring along. We work alongside your architects and engineers, not around them.
AI strategy & roadmapping
A target-state architecture and sequenced roadmap that names which use cases earn investment now, which wait, and what has to be true before either ships.
Process automation
Agentic and workflow automation applied where the work actually is — document handling, intake, review queues, handoffs between systems that were never designed to talk.
Custom AI builds
Multi-agent systems designed for the failure modes that matter: hallucination, silent omission, drift. Validator agents, human-in-the-loop review, and feedback capture as first-class design elements.
Data readiness & infrastructure
The unglamorous prerequisite. Integration patterns, pipelines, retrieval design, and governance guardrails that make your data safe to point a model at.
Team training & enablement
AI across the delivery lifecycle — design generation, code generation, code review, test creation — taught to engineering teams as practice, with the guardrails that keep it honest.
Fractional AI leadership
Senior architectural judgment on retainer: design reviews, architecture governance, and the standing authority to say no to the wrong build.
Vendor selection & due diligence
Independent evaluation of platforms, models, and AI vendors — including the line between user-assistive AI and architected enterprise systems, so you don't buy a copilot licence to solve an orchestration problem.
Four phases, no theatre
Every engagement produces something you can act on without us. Architecture decisions get written down, with the reasoning and the alternatives that were rejected.
Diagnose
Two to three weeks with your stakeholders, systems, and data. We map the current state and the constraints — budget, risk, security, and the people who have to run it.
Architect
Target-state design: agents, data flows, integration boundaries, security posture, and governance. Sequenced so the first increment is small enough to be real.
Prove
Build the increment with your team. Instrument it, measure accuracy and reviewer burden, and let the pilot's failure modes change the design before scale does.
Transfer
Standards, guardrails, scorecards, and coaching, so your architects own the next one. We'd rather be a phone call than a dependency.
Two decades of enterprise architecture, pointed at AI
Automa was founded on a simple observation: the hard part of enterprise AI is rarely the model. It is integration, data, security review, and the organizational patience to get something into production and keep it there.
Our practice was built on the unglamorous side of technology — integration architecture, data platforms, enterprise applications, and the governance around them — across manufacturing, retail, agriculture, and multi-site operations at national scale.
That background shows up in how we advise: conservative where risk is real, ambitious where it can afford to be, and specific about what has to be true before an AI initiative deserves funding. We have carried multi-million-dollar technology portfolios, rationalized them, led delivery teams through modernization, and shipped agentic AI to production — including the validator patterns, human-in-the-loop review, and feedback capture that keep it trustworthy once real users arrive.
Certified Azure solutions architecture, plus hands-on delivery on Azure AI Foundry, Claude, and OpenAI models.
Multi-agent systems live in production, not slideware — designed around the failure modes pilots expose.
Comfortable in front of security, procurement, and finance — the reviews where most AI projects actually die.
A track record of coaching architects and engineers through AI adoption across the delivery lifecycle.
Bring us the problem you can't sequence.
A short note about what you're trying to build — or what stalled — is enough to start. First conversations are free and usually clarifying either way.