I build LLM automations for businesses where errors are expensive.
With the evals and guardrails to prove they don’t make them. The system knows when it might be wrong and escalates to a human before a mistake reaches your client or your bank balance.
The core principle
An LLM can’t be made perfect. It can be made to know when it might be wrong.
Anyone promising an AI that’s never wrong is selling you something. The engineering job is to make the system recognise its own uncertainty and route those cases to a human before they cause damage. That’s the whole product: reliability you can put your name behind.
How I build for reliability
Two guardrails go into every automation I build. Three more are layered on, scoped to what an error in your workflow would actually cost. Together they’re the difference between a tool a business trusts to leave running and one they’re afraid to turn on.
Built into every automation
Confidence-gated human review
The system doesn't act on every output. Anything it isn't sure about is held and escalated to a person for sign-off before it takes effect — so the automation handles the routine 90% and a human catches the 10% that would have been the costly mistake.
Cost ceilings
Every automation runs under hard token and spend limits. A runaway loop or an adversarial input can't quietly run up a four-figure bill — the system stops and alerts instead of silently spending.
Layered on, scoped to the stakes
Eval suites with pass thresholds
Tested against a known set of cases — graded on action and answer — and must clear a defined accuracy bar before it goes live. No “it seemed to work.”
Structured output validation
Outputs are validated against a strict schema, so malformed or hallucinated responses are caught at the boundary. On failure it corrects or escalates, never passes a bad value through.
Fallback and escalation paths
When the system can't complete a task confidently, it retries, fails over to a backup, degrades gracefully, or hands to a human. It never fails silently.
How far each is pushed is scoped to the actual cost of an error in your workflow. A marketing draft and a financial calculation don’t warrant the same rigor — pretending they do just wastes your money.
See one running
A working demonstration, not a mockup
A voice assistant that answers the phone, checks the calendar, and books the meeting — and hands off to a human the moment it isn’t sure. Watch it run, then see exactly how it’s wired.
Selected work
Real case studies land here as engagements complete — with the numbers that matter to the business. I don’t invent proof, so there’s nothing here until there’s something true to show.
Start with a reliability audit
Not sure where automation fits? Have a quick chat below — I’ll help you pin down the one workflow worth automating, then follow up to set up a call.
A quick 2-minute chat — a person reads everything you send. By sending, you agree your details are used to respond to your enquiry. Privacy
Prefer email? Reach me directly at emanuel@projectapollo.app