AI on your data.
Inside your own walls.
Most "AI solutions" ship your private documents to someone else's servers to get an answer back. I build AI that runs inside your boundary — your cloud account, your network, your control. The data your team asks about never leaves the building.
No procurement marathon. One engineer, one scoped pilot, a working demo on your data.
The problem
You want what ChatGPT does. You can't send it your data.
Legal won't sign off. Compliance has questions. Your customers' records, your contracts, your internal playbooks — none of it should be pasted into a public model. So the AI conversation stalls, and your team keeps digging through wikis and Slack threads by hand.
Data can't leave
HIPAA, SOC 2, contracts, PII. Sending it to a third-party API is a non-starter — and rightly so.
Vendors are a black box
"Trust us, it's secure." Where does the data sit? Who can see it? What gets logged? No clear answer.
Knowledge stays buried
The answers exist — in docs, tickets, runbooks. Nobody can find them fast, so the same questions get re-asked forever.
What I build
A private answer engine over your own documents
Point it at your knowledge — policies, contracts, support history, engineering docs — and your team asks questions in plain English. It answers, with citations back to the source. The model and the data both live in your environment. Nothing is sent to OpenAI, Anthropic, or anyone else unless you explicitly decide it should be.
It runs where you do
Your cloud account or an edge boundary you control (Cloudflare, AWS, on-prem). The whole pipeline — retrieval, the model, the logs — sits inside a perimeter you own and can audit.
It only knows your stuff
Answers are grounded in your documents and cite where they came from. No hallucinated policy, no made-up numbers — if it's not in your source material, it says so.
It's wired like production
Access controls, audit trails, monitoring, and a kill switch — built in from day one by someone whose day job is keeping systems up and locked down.
Why me
Built by the person who runs the infrastructure
I'm not an AI startup chasing a demo. I'm a military-trained Site Reliability Engineer with 15+ years keeping production systems online and secure for millions of users. Security and uptime aren't a feature I bolt on at the end — they're the whole job.
That means the AI I hand you is something you can actually put in front of auditors: a clear boundary, real access controls, and an honest answer to "where does our data go?" — nowhere it shouldn't.
How we start
A scoped pilot, not a year-long project
We pick one painful question your team asks constantly, point the engine at the documents that answer it, and stand up a working demo — in your environment — so you can see it on real data before committing to more.
1 · Scope call
20 minutes. We find the one use case worth proving and confirm where your data lives. Free.
2 · Pilot
A fixed-scope build inside your boundary, on a slice of your real documents. You see it work before you scale it.
3 · Roll out
Expand to more sources and teams, hardened and monitored like any system I'd put my name on.
Have data you can't send to the cloud?
That's exactly the problem this solves. Tell me the question your team keeps asking — I'll show you how it gets answered without your data ever leaving home.
No pitch deck. Just a conversation with the engineer who'd build it.