AI that does the work, under control.
We automate the repetitive, error-prone work in IT and back-office operations, using the right engine for each job. Local AI when data has to stay in-house, cloud AI when you need top capability. Guardrails, logging and ownership included — like everything else we run.
AI promises a lot. Most of it never leaves the demo.
A team pilots a chatbot, it impresses for a week, then nobody trusts it with real work — because there are no guardrails, no logs, and no one owns the failures. Meanwhile staff quietly paste sensitive data into public tools with no policy at all.
- "AI projects" that stay demos and never reach production
- Client data pasted into public chatbots — no policy, no audit trail
- Automations that break silently, and nobody notices for weeks
- No line drawn between what should be a script and what should be AI
- Deterministic scripts for routine work; AI only where it earns its place
- Local AI for data that can't leave your premises
- Hard guardrails, full logging, human approval on anything irreversible
- A documented, owned, measurable production system — not a toy
Local AI or cloud AI? Both, for the right reasons.
Most "AI vendors" send everything to the cloud and hope you don't ask where your data goes. We don't. We pick the engine per workflow.
Local AI, on your hardware
- Data never leaves your network
- Built for accounting, legal and healthcare, where confidentiality is the point
- Predictable cost, no per-token bill
- Runs on a dedicated on-prem GPU server we size, deploy and manage
Cloud AI, when capability wins
- Best-in-class reasoning for complex, low-volume tasks
- Nothing to maintain
- Used where capability matters more than residency
What we automate.
Each automation has one clear job, a guardrail, and a log.
Email & ticket triage
Classify, route, and draft replies for human review.
Reporting
Monthly client and management reports, generated from your own systems.
Monitoring & security
Enrich alerts and match advisories to your actual stack.
Document & data workflows
Extract, structure and file what's now done by hand.
Lead & CRM enrichment
Score and prioritize at scale.
Custom internal agents
Bounded to one task, guardrailed, never set loose.
Four steps. No black boxes.
Assessment
Map repetitive workflows, data sensitivity, and where AI actually helps (and where it doesn't).
Design
Decide per step: deterministic, local AI, or cloud AI. Define guardrails and approval points.
Build
Implement, log everything, keep a human in the loop on anything critical or irreversible.
Operate
Monitor, measure hours saved and error rate, improve. No set-and-forget.
Where it pays off first.
Accounting firms
Local AI for client-data workflows: document extraction, report generation, intake. Nothing leaves the office.
Law & professional services
Confidential summarization and intake processed on-prem, not in someone else's cloud.
Call centers
Ticket triage, call QA, and automated reporting that used to eat hours every month.
Before you automate.
Will our data be sent to OpenAI or Anthropic?
Isn't AI unreliable?
Do we need expensive hardware?
How do you prove it's working?
Stop doing by hand what should run itself.
An automation audit maps your workflows and shows where automation pays off — and, just as important, where it doesn't.