Govern Your Agents
Security, Accuracy
& Governance
In One Engagement
An AI Assurance & Governance Firm - Controls Built Inside Your Own Stack, Owned By You
What We Do
You Cannot Govern What You Cannot See
Most AI goes to production unmeasured, unscoped and unrecorded. We fix the boundaries around the model - permissions, evals, approvals, tracing - inside your own stack.
How It Works
Evidence Before Recommendations
01
Audit
A read-only diagnostic of what you run today: every AI system in use, what each can reach, where untrusted input can get to a privileged action, what data leaves, and whether anything is measured or recorded. It ends in a written roadmap ordered by risk.
02
Instrument
We build the things that have to exist before anything else can be judged - tracing through model and tool calls, an eval set from your own data, and the system inventory. Nothing here is a product of ours; it is built in your stack and stays there.
03
Govern & Operate
Controls go live - scoped permissions, approval gates on irreversible actions, alerting, regression gates in CI. Every week you get what changed, what the numbers did, and what we would do next. Controls that are not delivering value are removed.
04
Re-audit
Each cycle ends back at the diagnostic rather than at onboarding. Systems change, models get swapped, someone adds a tool - so what the last cycle established becomes the baseline for the next one, and the picture compounds instead of going stale.
Insights
The Thinking, In Public
Questions
The Things People Ask First
What does an AI assurance engagement actually cover?
Five lines of work: security and red-teaming, accuracy and evaluation, governance and policy, monitoring and incident response, and efficiency and cost control. In practice an engagement starts with the audit, and the audit tells you which of the five has the most upside on your systems - which is frequently not the one you came in asking about.
What is included in the initial AI audit?
A read-only look at what you are running now. We inventory every AI system in use, map what each one can reach, trace where untrusted input can get to a privileged action, check what data leaves and under which terms, and establish whether accuracy is measured or actions are recorded at all. It ends in a written roadmap, ordered by risk, and you keep that roadmap whether or not you work with us.
Is this about the model, or about the system around it?
The system around it, almost entirely. Model choice matters less than most teams expect, and permissions, retrieval quality, tool scoping, approval gates and instrumentation matter more. A capable model wired into an unscoped set of credentials is a worse position than a weaker one that cannot do damage.
Can you guarantee our AI will not hallucinate or be jailbroken?
No. Neither problem is solved at the current state of the art, and no responsible provider can guarantee otherwise. What we commit to is what we control: measuring the real error rate on your data, bounding what a compromised system can reach, and making sure that when something does go wrong it is visible and reconstructable rather than silent.
How do you price engagements?
Retainer by default, scoped after the audit rather than quoted blind, because scoping this work without reading the systems first would be guesswork. Model and infrastructure spend is always separate and paid by you directly to your providers - our fee is never a cut of it.
Do You Know What Your AI Can Reach?
Start with the initial AI audit - read-only, at no cost and under mutual NDA. It covers every system you have in production, what each one can reach, what data leaves, and whether anything is measured or recorded. The written roadmap is yours either way.