Service
Practical AI and automation that improve real work
AI earns its place when it makes a defined piece of work faster or helps people actually use the information they already have. The rest is noise.
I help organizations find the practical opportunities, weigh the risks, and build something they can support. I’m not reselling an AI platform, and nothing I recommend pays me a commission. That keeps my read honest, including on where AI won’t help you.
What this looks like
- AI opportunity assessments
- Internal knowledge assistants
- Policy and procedure search in plain language
- Document classification and summarization
- Workflow and document automation
- Structured data extraction from forms and reports
- Reporting assistance
- Staff training on using these tools well
- AI governance and acceptable-use policy
- Secure integration with internal systems
- AI assistants connected to the systems your staff already use
- Human review and approval built into the workflow
- Proof-of-concept builds before you commit
How I keep it responsible
- Start with a real operational need, not the technology.
- Never automate a broken process before it’s understood.
- Keep a person accountable for decisions that matter.
- Protect confidential and regulated data. None of it goes into unapproved tools.
- Measure whether it actually saves time or improves quality.
- Prefer controlled, supportable implementations over novelty.
- Be clear about how the tool works and where it falls down.
A few real use cases
- Search internal policies by asking a plain question.
- Summarize long documents for review.
- Pull structured data out of forms and reports.
- Route requests based on what they contain.
- Draft routine communications for a person to finish.
- Help classify and triage service-desk tickets.
- Compare contract terms or vendor proposals.
- Turn existing data into a first-draft report.
Connecting AI to the systems you already run
Some of this work connects an AI assistant directly to the systems your staff already use, so a question gets answered from live data instead of turning into a report request. It’s built on the Model Context Protocol, an open standard. It runs against the systems you already own, with access controls you set. Nothing you depend on gets replaced or migrated.
Questions people ask about AI projects
How is an AI project scoped?
I scope the work first, then quote a fixed price. Engagements run from short advisory work through multi-month development projects, and a proof-of-concept build is often the right first step before you commit.
What happens to our confidential data?
Confidential and regulated data stays protected: none of it goes into unapproved tools. Where an assistant connects to your systems, it runs against the systems you already own, with access controls you set, and a person stays accountable for decisions that matter.
Who supports it afterward?
Security, ownership, support, and lifecycle get settled up front, before the build. I document what I deliver and hand it over responsibly, and I can support it on an ongoing basis, quoted separately from the build.