Practical AI, honest automation
Every vendor is selling AI; almost nobody is on the hook for whether it works, what it costs, or where your data goes. We build the infrastructure AI workloads actually need, automate the processes that eat your team's week, and put governance around both — practical wins first, science projects never.
The problems that bring people here
"Leadership wants an AI story"
The board is asking; the team has demos and no production path. You need something real, sized to your business, with costs you can defend.
"Shadow AI is everywhere"
Staff are pasting company data into whatever tool is trending, and nobody can say what's leaving the building or on what terms.
"The manual work is drowning us"
Onboarding, reporting, ticket triage, reconciliation — the same hands doing the same steps every week, with errors to match.
"The GPU bill made no sense"
AI infrastructure was stood up in a hurry — oversized, always on, and unowned. It needs the same cost and lifecycle discipline as everything else.
AI & automation capabilities
- AI/ML infrastructure. The platforms AI workloads run on — managed AI services or self-hosted, sized honestly, cost-gated, and torn down when idle rather than billing around the clock.
- Process & workflow automation. The unglamorous automation that pays: integrations, scheduled jobs, document handling, and approval flows that remove the copy-paste from your week.
- AI-assisted operations. Where a model genuinely helps — triage, summarization, drafting, classification — wired into your existing tools with a human in the loop where it matters.
- AI governance & data boundaries. Usage policy, tool vetting, and technical controls that keep company and customer data out of the models — the same architectural boundary we run in our own delivery.
- Vendor & model selection. Commercially licensed, non-training terms, exit paths, and real cost-per-outcome comparisons — before you're committed.
- Cost & lifecycle discipline. AI workloads under the same rules as everything else we build: priced before they run, capped, monitored, and retired cleanly.
Governance and security for AI sit with the security practice — same team, one recommendation.
Assess → Design → Build → Steward
Assess
Find the processes worth automating and the AI use cases with a real payback — and the shadow-AI exposure you already have.
Design
A target for the automation and AI stack — tools, data boundaries, costs, and the governance baseline — written down before anything runs.
Build
Ship the automations and the AI infrastructure through the same gated, documented delivery as everything else we build.
Steward
Operate and tune what we built under managed services, or hand it off clean with runbooks your team can follow.
A low-risk way to begin
AI Readiness & Governance Review
Where AI can help, where it can't, and how to keep company data out of it — including shadow-AI discovery and a governance baseline you keep.
Automation Opportunity Assessment
We map the manual processes eating your team's time, rank them by payback and risk, and hand you a prioritized automation plan — with the first quick win scoped and ready to build.
Want the AI win without the AI mess?
Bring us the process that eats your week or the AI question the board keeps asking — we'll give you a straight answer and a costed plan.