AI Automation Consulting
AI Automation That Actually Ships
Most AI pilots die in a slide deck. We build AI systems that run in production, handle real work every day, and keep a human in the loop wherever accuracy matters.
Most Teams Are Stuck in One of Three Places
Drowning in repetitive work
Support triage, document processing, reporting, data entry. Everyone knows it should be automated. Nobody has the bandwidth to build it.
Pilots that never shipped
You tried an AI tool. It demoed beautifully. Months later nothing runs in production and nobody trusts the output enough to rely on it.
No idea where to start
The tooling landscape shifts weekly. It is hard to tell what is real, what it costs, and what deserves your team's attention this quarter.
What We Build
Four Ways We Put AI to Work
Every engagement starts by finding the work worth automating, then building something your team can actually run without us.
AI Workflow Automation
Document intake and extraction, support ticket triage and routing, automated reporting and summarization. We map the operational work that consumes hours every week, then build systems that handle it reliably, with human review checkpoints wherever accuracy matters.
AI Adoption Strategy
Where AI fits in your stack, which tools justify the spend, what guardrails you need, and how to get your team using them well. You get a prioritized roadmap and realistic cost modeling, not a vendor pitch.
AI-Augmented QA
Test case generation from specs and designs, structured bug reports with reproduction steps, regression scoping driven by change analysis, and automated verification of multi-step business flows. This is where we have the deepest track record.
Evaluation & Guardrails
An AI system you cannot measure is a liability. We build evaluation harnesses, define acceptance criteria, and set up monitoring so you know when output quality drifts. This is quality engineering applied to AI, and it is the part most implementations skip.
How We Work
Quality Engineering, Applied to AI
We came to AI from test engineering, and it shows in how we build. Every system we ship gets the same treatment we give a test suite: defined success criteria, measurable output, a way to catch regressions, and documentation so your team owns it.
That is the difference between an AI demo and an AI system. Demos work once, on a good day, with a clean input. Systems keep working.
We start with the work, not the tool
The right question is what is slowing you down, not which model to use.
Human review where accuracy matters
Full autonomy on low-risk steps, a person in the loop on the ones that count.
Measurable from day one
If we cannot show you what it saved and how often it is right, it is not done.
Your team owns it when we leave
Documented, maintainable, and built so you are never locked into us.
Let's Find the Work Worth Automating
Tell us where your team is losing time. We will show you what AI can realistically take off their plate, and what it cannot.