Why someone who flies aircraft for a living ended up building enterprise AI.
I'm Russell Randol, founder of LogicsStack. Before this, most of my work was operational rather than theoretical — flying unmanned aircraft in environments where the paperwork matters as much as the flight.
In regulated work, a report isn't a formality — it's the artifact everything else rests on. An inspection finding, a compliance filing, a survey deliverable. Somebody signs it. Somebody relies on it. If a number in it is wrong, the consequences are real and they land on a person, not on a system.
When teams started drafting those documents with AI, the failure mode was immediately obvious to anyone who has had to sign one:
That's not a problem you fix by using a better model. Every model has the same property. You fix it by checking each claim against the records that are supposed to back it, and being explicit about what you couldn't check.
Enterprise AI and automation systems that check their own work. The core of it: the system reads AI-generated text, breaks it into individual factual claims, and checks each one against the documents you provide — your survey logs, your policies, your case files. Every claim comes back cited to its source, flagged as conflicting with your records, or marked as something your records don't establish.
It verifies against your sources, not against the internet. You decide what counts as true. The same principle runs through the rest of the work — document intelligence (turning contracts, inspection reports, claims files, and invoices into checked, structured data), workflow automation, and agents that execute under governance rather than on trust.
I don't sell anything I don't run myself. LogicsStack operates on the same stack it delivers — it's the reason one operator can do the work of a small team, and it shows up three ways:
Security isn't a feature bolted on — it's the premise. A verification company that leaks the records it's checking has already failed. What's in place today:
Your files stay on the machine. They're not retained
after the work is delivered, and nothing is used to train any model.
NDA first. I'll sign your NDA before you send anything; a closed file or one
with names removed is fine for the first pass.
Private by default, cloud by choice. Retrieval and your private data stay
local — a fully air-gapped mode is available — and heavy cloud reasoning only runs on content
you explicitly approve.
Encryption at rest so stored data is unreadable if a device is lost or stolen.
Audit logging on every model call — a record of what was asked and answered,
so nothing happens off the books.
For a business location, the right setup is a hardened edge appliance — a small, locked-down box behind your own firewall running LogicsStack on-site, with your internet provider as the transport and small hubs at each site. The near-term addition on my side is a named cybersecurity advisor on the record, so the security story carries an expert's name and not just my word.
You won't find invented customer counts or fabricated case-study numbers on this site. Where a figure is illustrative, it says so. A verification company that fudges its own marketing has already failed its premise.
The system says "can't verify" when your records don't establish something, rather than guessing. That's the whole product. Softening it to look more impressive would make it useless.
LogicsStack is small and I run it. If you have a question about how it handles your documents, you'll get an answer from the person who wrote the code.
Pricing is anchored to what the work saves you, not what it costs — but that only means something if the number is yours, not mine. So we baseline it. Before any build, we measure the current process — hours per document, rework and error rate, and dollars leaked to fraud or missed catches — then measure the same numbers after. The free first document is the opening data point.
On start-up cost: loading and tuning to your records is the setup fee — there's no separate charge to bring your documents in. The $1,500 setup (plus $500/month while we prove it out) installs on your workflow and tunes to one document type against your records. The $5,000+ build is for multiple document types, custom integrations, and team training; an unusually large corpus is scoped into that tier. No retainer required to find out whether it's useful.
The fastest way to evaluate this is on your own material: send me a recent AI-drafted document along with the records behind it, and I'll return it marked up — every claim cited, flagged, or noted as unverifiable. No commitment.