Private AI that runs inside your walls. Your data never touches a public model.
We start by auditing where AI belongs — and where it does not. Then we build only what earned a go, integrated with your systems and running inside your own environment.
Built by operators with deep experience in Fortune 500 manufacturing, healthcare systems, and enterprise technology — now applied to regulated, complex operations that cannot send their data to a public model. The build is optional. The audit is the starting point.
- Airekka builds private AI systems that run inside a client's own environment — proprietary and regulated data is never sent to a public model like ChatGPT or Copilot.
- Every engagement starts with an operational AI audit: where AI belongs, where it doesn't, and what to do first — before anything gets built.
- Human-in-the-loop gates, audit trails, and access control are designed in from the start. Teams work from Toledo, OH and Orlando, FL.
You don't need to know whether you need AI. That's what the first call is for.
Most people land here knowing something is wrong — a process that eats every Monday, three systems that don't agree, a team drowning in rework — but not what to call it. Workflow automation? An AI agent? A different tool? Nothing at all?
You don't have to sort that out before you talk to us. That's our job. The first call is a conversation: you tell us what's costing you, we ask questions, and we figure out together whether there's a real problem worth solving — and whether we're the right people to solve it.
Sometimes the honest answer is a small change, not a build. Sometimes it's keeping your data in-house on a local model instead of a public AI tool. Sometimes it's “not yet.” You'll get our real read either way.
The market is pushing you toward public AI. Your data isn't allowed to go there.
The default on offer is a public assistant — paste your data into a chat window and hope. For a regulated operation, that is not a shortcut. It is exposure: proprietary records, customer PII, and files under contract, processed by a third-party model you do not control.
So it gets banned outright and nothing improves — or it happens anyway, on personal accounts, off the record. Either way, the real work still has to move.
There is a third option: AI that runs inside your own environment, integrated with the systems you already use, where the data never leaves. We start by auditing where that belongs — and where it does not.
If the honest answer is “don’t automate this,” you get that in writing. That is the point of an audit that is not a sales process.
Airekka audits and builds for operations leaders from Toledo, OH and Orlando, FL — then recommends where private AI belongs before anyone spends on a build.
Six chapters.
The work, not a catalog of tools.
Workflows, systems, documents, people, risk, sequence. This is what the audit actually inspects — then you get go, fix-first, or don’t automate.
流れ · The real path of work
The SOP nobody follows does not count. We map where time dies, where exceptions pile up, and where two teams argue about whose number is right.
Best for: Monday reconciles, prior-auth packets, claims routing, the mailbox that is really a ticket queue
Audit. Recommend.
Pilot. Govern.
Audit
We map the real workflow — people, systems, documents, and the unofficial steps. You see the work as it actually runs.
Recommend
Each candidate gets a go, fix-first, or don’t-automate call, with the why. You leave with a sequence, not a catalog of tools.
Pilot
Only if something scored a go. We build against your real data and stack — optional, scoped, earned.
Govern
If a pilot ships, human-in-the-loop gates, audit trails, and access control are designed in from the first sprint — not bolted on after go-live. When a regulator, auditor, or client asks who decided what and when, the answer is already in the system.
A recommendation map. Not a 90-page binder.
Go
The process is stable enough, the data is reachable, and the payoff is real. This is a candidate for a scoped pilot.
Fix first
AI would amplify a mess. Clean the handoff, the master data, or the exception path — then re-score.
Don’t automate
Low volume, high judgment, thin data, or unacceptable risk. Leave it with people. That is a finding, not a failure.
Any operation that can't send its data to a public model.
We see this most often in manufacturing, healthcare, and insurance — but the pattern is the constraint, not the industry: regulated records, customer PII, or contractual data that a public AI tool is not allowed to touch.
Support operations · in progress
The mailbox was the ticketing system. The third-party tool was the extra hop.
A live engagement: we are replacing a third-party ticketing layer with a private, in-house AI system the client controls end to end. Client-facing mailboxes are the intake. AI opens the ticket, closes what it can, and escalates what it cannot. No invented outcomes. The work is in flight.
- Mailbox intake. The client-facing mailboxes are the intake — the work is taken in where it already arrives, with no second desk to keep in sync.
- Ticket the message. Each message is opened as a ticket by an AI app the client controls, not by a second desk.
- Close what AI can close. Routine, well-bounded requests are resolved and closed by the app.
- Escalate the rest. What the model cannot manage is handed to a person. That gate is the point.
The system runs on a local model inside the client's own environment — message contents and attachments never reach a public AI service.
See the engagementWe will tell you not to buy AI. That is the product.
Consultancies show a deck. We start with the work.
The audit is interviews, system maps, and a written recommendation — not a catalog of use cases copied from last quarter’s webinar.
We do not start by ripping out ERP. We will replace a ticketing layer if the mailbox is where the work already lives.
The stack you run stays until the audit says it is in the way. A ticketing tool that is only a second desk is a candidate for replacement. The ERP that is your system of record is not.
Vendors need a yes. We will write a no.
“Don’t automate this” is a successful audit. Human gates, audit trails, and access control are designed in from the start if a recommendation later becomes a build — not bolted on before an audit or a regulator asks for them.
Frequently asked questions
What is an Airekka AI audit?
A structured look at how work actually moves through your company — people, systems, documents, and handoffs — so you can decide where AI belongs before you spend on a build. It is an operational diagnostic, not a model-ethics review and not a sales deck dressed as discovery.
Is this just discovery by another name?
Discovery is how many firms bury an assessment inside a build sale. The AI audit is the product: a written recommendation map you own whether or not you ever ask us to pilot. A build is optional, and only for work that earned a go.
Do you always recommend AI?
No. A useful audit tells you where AI does not belong — high-exception work, thin data, irreversible risk, or a process that is broken before it is a candidate for automation. “Don’t automate this” is a successful finding.
What do we walk away with?
A current-state map, a ranked list of opportunities, and a go / fix-first / don’t-automate call on each one — plus a sequence for the next 90 days. If a first pilot is justified, you get a scoped recommendation for that too.
Do you replace our ERP or EHR?
No. We look at the systems you already run — SAP, Oracle, NetSuite, Epic, Cerner, CRM, and legacy bridges — and recommend where AI should plug in, if at all. A ticketing layer that is only a second desk on top of the mailbox is a different question: that is a candidate for replacement when the audit says the work never lived there.
Who is this for?
Operations leaders at regulated or data-sensitive companies who are being pushed toward public AI tools but cannot send proprietary or regulated data to a third-party model. Complexity and exposure matter more than headcount. We work with manufacturing, healthcare, and insurance teams, and with other operations where disconnected systems and sensitive data are costing real money.
What happens after the audit?
You own the brief. You can run the recommendations internally, hire someone else, or ask us to pilot the first item that scored a go. We do not treat the audit as a trap to force a build.
Does our data ever go to a public AI model?
No. Systems we build run inside the client's own environment, integrated with their existing stack. Regulated records and PII are never sent to a public model like ChatGPT or Copilot. If the audit finds that a use case can only be done safely with a public model, the recommendation is don't automate it that way — not a workaround that sends the data out anyway.
What's eating your week?
Tell us the process that is eating your week. We'll respond within one business day with whether it is a candidate and what we would look at first.
The first call is free and there's no pitch at the end of it. Tell us the process that's eating your week — we'll come ready to talk about that specific thing, and we'll tell you honestly whether it's a candidate, what we'd look at, and what an audit would cost.