About Ontic Labs
Truth over fluency. Evidence before authority.
We build systems that verify consequential AI outputs before anyone can act on them.
Mission
Give regulated industries proof that their AI told the truth.
Our Values
These are operating constraints, not brand language.
Truth Over Fluency
A confident wrong answer is worse than no answer. Plausibility is not a success metric.
Authority Is Earned, Not Generated
Evidence comes first. Claims without verifiable authority do not pass.
Refuse When You Should
Sometimes the correct output is refusal. Missing evidence should halt emission, not trigger guessing.
Build the System, Not the Fix
Quality is structural, not inspected. Reliability comes from enforcement architecture, not post-hoc patching.
The Problem We Solve
Models are trained to produce plausible outputs, not to prove claims. In consequential domains, plausible is not good enough.
That failure mode is Systematic Architectural Fiction: outputs that look complete and confident but are not verifiably true.
Our Approach
We do not ask the model to self-police. We enforce a verification boundary so consequential claims are checked against authority before emission.
- —Specification First — If required state is missing, the system requests specification instead of guessing.
- —Evidence or Refusal — Measurements, classifications, and high-stakes recommendations require verifiable provenance.
- —Structural Enforcement — The gate enforces policy in architecture, not in prompt intent alone.
How We’re Different
Most approaches try to improve model behavior. We enforce what can be emitted.
Model Tuning and Prompting
Fine-tuning, prompt engineering, and guardrails improve behavior but do not create proof requirements.
Problem: Behavior can improve while unsupported claims still slip through under pressure or ambiguity.
Post-hoc Output Filtering
Screens content after generation for policy issues.
Problem: Useful for moderation, but too late for proof-bound claims if unsupported content was already generated.
Ontic Enforcement Architecture
The system checks required state and authority before emission. Unsupported claims are refused by design.
Difference: This turns governance from guidance into enforcement: no evidence, no emission.
Why It Matters
In regulated and high-consequence workflows, errors are not just bugs — they are compliance, legal, and safety events. Systems that cannot prove claims are systems you cannot safely trust in production.
Our Team
Ontic Labs was founded by engineers and researchers who’ve built systems at the intersection of AI, security, and high-stakes domains.
Stephen
Founder & CEO
Built a recipe app where AI generated content and Ontic validated the nutrition data. The validation part was the hardest thing he’d done in 40 years. That’s why Ontic exists.
Bruno
Chief Engineer
Won’t let you build until you’ve answered the hard question. Spent years watching teams ship first and ask questions never. Now he asks the questions that save you from yourself. That’s the point.
Eric
Full Stack Engineer
Ships code that future engineers won’t curse. Learned early that most production fires start with someone who didn’t think about what happens next. Believes in indexes, explicit errors, and leaving things better than he found them.
Lamartine
Full Stack Engineer
Makes complex systems look obvious. Spent enough time untangling other people’s abstractions to know that simplicity isn’t the starting point — it’s what’s left after you’ve removed everything that doesn’t need to be there. Opinionated about APIs.
Phil
Full Stack Engineer
Former chef. Ran kitchens where the wrong ingredient or wrong instruction meant the whole service failed. Turns out software works the same way. Now builds systems where the recipe has to be right before anything gets plated.
Ruby & Olive
Office Dogs
Ruby (left) is obsessed with reflections. Will stare at glass, mirrors, or anything shiny until someone intervenes. Olive (right) is obsessed with balls. Has never met a ball she didn’t need to have immediately. Neither has shipped production code, but morale metrics remain strong.
Violet
QA
Obsessed with anything on a monitor. Will track your cursor across the screen with the focus of someone who’s found a critical bug. Has never filed a ticket, but her attention to visual detail is unmatched. Watches code reviews happen in real time. Has opinions.
Contact
Talk to us about deploying AI with proof-grade governance in consequential domains.
enterprise@onticlabs.com