AI Operations
Every AI system you run kept safe, accurate and accountable, on a schedule instead of by accident.
For US med-spas, home-services firms, dental practices and solo-legal offices running more than one AI agent, chatbot or automation who need a standing, independent way to prove the whole estate is still behaving, not just hope it is.
Every engagement is directed by a technical specialist and reviewed before delivery.
What this is
The AI Governance and QA Program is Raveneye Global's standing retainer for keeping every AI system you run safe, accurate and affordable, on a schedule instead of by accident. It assembles the whole discipline into a single coordinated engagement: a full inventory of your live agents and automations, a risk tier for each one, a recurring evaluation cadence that scores accuracy against your own facts and red-teams each system for manipulation, continuous monitoring for drift, errors and runaway cost between those reviews, and a governance register that records what was tested, what was found and what was fixed. One technical specialist owns your whole estate, sets the cadence, and reads every result before it reaches you. The outcome is a single, defensible answer to the question most owners cannot answer today: are all your AI systems still behaving, and how would you know?
The problem
Why this matters now
Most small businesses now run more than one AI system without realising it. There is the voice agent that answers the phone, the chat widget on the site, the knowledge bot that quotes policy, the automation that routes leads. Each was set up once, judged by whether it looked fine on day one, and then left alone. Nobody owns them as a group, nobody re-checks them on a schedule, and no one can say in a sentence whether they are all still correct and safe today.
The reason this matters is that AI systems fail quietly and they fail in more than one way at once. They drift, where answer quality decays slowly as prompts, models and your own data shift underneath them. They can be manipulated, where a stranger typing the right words makes an agent ignore its rules, leak information or misuse a tool it can act on. And they can run up cost, where a bad prompt or a runaway loop doubles your token spend overnight. A one-time audit catches the state on the day it ran. It says nothing about next month, and next month is where the failure usually lives.
The recognized frameworks are blunt about this. The NIST AI Risk Management Framework treats govern, map, measure and manage as a continuous, iterative lifecycle rather than a one-time check (NIST AI RMF 1.0, 2023), and accepted red-team practice is to test before and after deployment and again on every meaningful release, because a single prompt edit or model swap reopens the attack surface (StingrAI, AI Red Teaming, 2026). Independent 2026 analysis still puts customer-support chatbot hallucination in the range of roughly 15 to 27 percent in live use (SQ Magazine, LLM Hallucination Statistics, 2026). Governance is not a document filed once. It is a cadence that is kept.
What you need is not another point-in-time inspection or another screen to babysit. It is a standing office over your whole AI estate: an inventory, a schedule, a red-team rhythm, a cost ceiling, and a register that proves what was checked and when. This program is that office, run by a technical specialist, so the systems you already paid for stay trustworthy long after launch day.
How it works
The mechanism, made checkable
- 01
Inventory the estate and tier every system by risk
Before any cadence is set, a specialist maps every AI system you use: what each one answers, what it can do, where it runs, and what it can touch. A bot that only reads business hours is tiered differently from a voice agent that books appointments, looks up records or sends messages. This inventory is the spine of the program, and the risk tier decides how hard and how often each system is tested, following the map-and-measure discipline the NIST AI Risk Management Framework prescribes (NIST AI RMF 1.0, 2023).
- 02
Set the baseline, the policy and the cost ceilings
For each system, we record what good looks like today: accuracy against your own approved facts, response latency, error rate and cost per interaction. We agree the rules your estate is held to: what it must never say or do, when it must hand off to a human, and the token-cost ceiling that should trigger an alert. This is the reference the whole program measures against, because drift and overspend are only visible when today can be compared to launch day.
- 03
Run the scheduled evaluation cadence
On an agreed rhythm, and again after any meaningful release or model swap, a specialist re-runs each system against its evaluation set: accuracy and hallucination scored against your facts, plus an adversarial red-team using the recognized attack families, jailbreak and role-play prompts, injected instructions, off-topic bait and tool-misuse attempts. Prompt injection remains the number-one risk in the OWASP Top 10 for Large Language Model Applications (OWASP, 2025), so it is where the testing pushes hardest on anything that can do more than talk. Every failing result is captured with the exact prompt that produced it.
- 04
Monitor continuously between reviews
A scheduled audit catches the state on the day it ran; the monitor covers the gap in between. We instrument each system for health, quality and cost, run canary questions against known-good answers, and flag a downward slope before a customer meets it. When a threshold is crossed, an alert reaches your team with the trace and a plain-English reason attached. The monitor watches and warns. It never edits, deploys or changes a system on its own.
- 05
Triage, prioritize and route the fixes
An alert or a finding is a signal, not a verdict. A specialist reviews the trace, confirms whether it is a real regression or noise, ranks it by severity, and states plainly what is happening and what to do. Fixing is a separate decision that stays with you: act on the ranked list, hand it to whoever built the system, or use it to decide whether to keep, rebuild or retire an agent. We grade systems the same way whether or not Raveneye Global built them.
- 06
Keep the governance register and review the posture
Everything the program does is written down: what was inventoried, what was tested, what was found, what was fixed, and when. On a periodic cadence, the specialist walks you through your estate's governance posture in plain English, recalibrates the baselines and thresholds as prices and policies move, and delivers a defensible record you can show to a partner, an insurer or a regulator. This is the auditable, documented management system the ISO/IEC 42001 standard is built around (ISO/IEC 42001:2023), run at a scale a small business can actually keep.
What is included
What is delivered
- A full inventory of every live AI system, with a documented risk tier for each based on what it answers, where it runs and what it can act on
- A baseline capture per system: accuracy against your approved facts, latency, error rate and cost per interaction
- A written governance policy and threshold set: the rules each system is held to, its hand-off and refusal boundaries, and its token-cost ceiling
- A scheduled evaluation cadence that scores accuracy and hallucination against your own facts and captures every failure verbatim, incorporating the Agent Evaluation and QA Audit discipline
- A recurring adversarial red-team across jailbreak, injection, off-topic-bait and tool-misuse families, re-run after every meaningful release or model swap
- Continuous health, quality and cost monitoring between reviews with drift detection and alerts routed to your team, incorporating the AI Systems Monitor discipline
- Specialist triage on every meaningful finding and alert, with a severity rating and the exact prompt that reproduced it
- A governance register that records what was tested, what was found and what was fixed over time, as an auditable trail
- A periodic posture review and baseline recalibration, with a specialist walkthrough in plain English
The outcome
What it moves
- A single, defensible answer to whether every AI system in production is still safe, accurate and inside its cost ceiling, backed by a record instead of a hope
- A living inventory of your AI estate with each system risk-tiered, so effort and scrutiny land where the danger actually is
- A scheduled evaluation and red-team cadence that re-checks accuracy and manipulation after every meaningful release, not just once at launch
- Continuous monitoring that catches drift, errors and runaway token cost in hours, with a specialist confirming what each alert means
- A ranked, reproducible fix list across the whole estate, so the dangerous failures get addressed before the cosmetic ones
- A written governance register and periodic posture review you can hand to a partner, an insurer or a regulator to prove the systems are managed, not just running
What you get
What you get, and how it is priced
You are priced on the actual estate: a single read-only FAQ bot and a fleet of agents that book, quote and send are very different governance problems. The program coordinates the individual disciplines, the Agent Evaluation and QA Audit and the AI Systems Monitor among them, into one owned cadence rather than a stack of disconnected checks. Below is exactly what the program assembles, how it runs through the year, and the levels it comes in. Scope and cadence are confirmed in writing before any work begins.
| Single-System Governance. The program run over one AI system that matters: baseline and policy, a scheduled evaluation and red-team cadence, continuous monitoring between reviews, and a governance register with specialist triage. The right fit when you rely on one agent or automation and want it governed on a rhythm rather than checked once. Cadence and depth are set after the inventory and confirmed in writing. | Quoted |
| Estate Governance. The program across several AI systems at once, with per-system risk tiers, one coordinated evaluation cadence, monitoring and cost ceilings for the whole estate, and a single governance register that reads across all of them. Built for practices and trades running more than one system that need them governed together, not one at a time, with a periodic posture review. | Quoted |
| Assured Operations. The deepest coverage for operations where a silent AI failure would reach customers quickly and cost real money: tighter thresholds, faster alerting, a heavier red-team schedule, release-gated re-evaluation, and a specialist actively reviewing the estate's posture and recalibrating baselines on an agreed cadence. Scoped around how fast your systems change and how much they can act on your behalf. | Quoted |
You see the full deliverables and cadence first, then a price built for your business, confirmed in writing.
Straight answers
Questions about AI Governance & QA Program
How is this different from a one-time Agent Evaluation audit?
The audit is a snapshot; the program is the schedule. A single Agent Evaluation and QA Audit shows how one system behaves on the day it ran, which is genuinely useful and often the right first step. But AI systems drift, get manipulated in new ways, and get edited between checks, so a clean audit last quarter says little about this quarter. The program takes the same rigour and runs it as a standing cadence across your whole estate, with monitoring in the gaps and a register that proves the state over time. For one system and a single read, the audit is the right fit. For several systems that need to stay trustworthy, this is the office that does it.
What does the program actually govern, and who owns it?
It governs every AI system in production: voice agents, chat agents, knowledge agents and automations, whoever built them. One technical specialist owns your estate end to end, keeps the inventory current, sets the risk tiers and the cadence, runs the evaluations and red-teams, triages the monitor's alerts, and maintains the governance register. You do not need to assemble a stack of separate checks and stitch them together in-house. The whole discipline goes to one accountable person who reads the result before it reaches you.
Do you have to have built our AI systems to govern them?
No. The program is independent by design and works across any agent, chatbot or automation in production, including ones bought from other vendors. An outside read is often more useful precisely because we did not build the thing and have no reason to flatter it. When a system came from someone else, you get a neutral verdict and a reproducible fix list you can take straight back to them, with the exact prompts that failed attached.
Will this just turn into a pitch to rebuild everything?
No. The program is assurance, not a sales funnel. When we find a problem it is ranked and explained, and what happens next is entirely your decision: fix it in-house, hand it to whoever built the system, or rebuild. We grade systems the same way whether or not Raveneye Global built them: a verdict you can't act on helps no one. If a fix does call for new engineering, we state that plainly and scope it separately. It is never bundled in to inflate the retainer.
Is any of this checking synthetic, churned out by a machine grading itself?
No. A technical specialist designs the evaluation sets, chooses the attacks, sets the thresholds, judges the results and writes the register. Tooling helps run large batches of test prompts and watch systems continuously, but a person builds the ground truth, interprets what the numbers mean, and signs off. What you get is a human verdict on the machines, not a machine marking its own homework. Every engagement is directed by a technical specialist and reviewed before delivery, and nothing in the register ships without that review.
How often do you re-evaluate, and why on a cadence at all?
More often than most owners expect, because AI systems change more often than they think. A model update, a prompt edit or a newly connected tool can reopen a risk that tested clean before, so accepted 2026 practice is to test before and after deployment and again on every meaningful release (StingrAI, AI Red Teaming, 2026). We set the rhythm to how fast your systems actually change: heavier scrutiny on the ones that book, quote or send, lighter on the ones that only answer. Between scheduled reviews the monitor covers the gap, so a quiet update does not quietly break something.
What exactly do you guarantee?
That a technical specialist owns your estate, runs the cadence, and reviews every result, that every finding is reproducible with the exact prompt attached, that the monitor watches and warns but never changes a system on its own, and that the governance register stays with you. There is no guarantee that no system will ever fail or that every issue will be caught before any customer does. AI systems are probabilistic, AI Overview selection is undocumented, and engine behavior shifts. We commit to a real baseline, a kept cadence, continuous measurement, and a human who explains what a signal means and what to do about it.
We are a small practice, not an enterprise. Is formal AI governance overkill for us?
It is the opposite. Enterprises have compliance teams; a small practice usually has one owner and no standing way to see inside the systems it paid for. The recognized frameworks are explicit that governance should be tailored to an organization's size and maturity, not copied wholesale from a bank (NIST AI RMF 1.0, 2023). This program is that tailoring: the same continuous discipline, sized to a handful of systems and a schedule you can actually keep, producing a defensible record without hiring a department to build it.
Related
Where this connects
AI Systems & BizOps
The full family this program sits in. See the agents, knowledge systems and automations we build, and the individual audits and monitors this retainer coordinates into one governed cadence.
ExploreAgent Evaluation & QA Audit
The point-in-time inspection this program runs on a schedule. If you want one independent read on a single system before committing to a standing cadence, start here.
ExploreAI Systems Monitor
The continuous watch that covers the gap between scheduled reviews. It is the monitoring discipline this program folds into the wider governance rhythm across your whole estate.
ExploreProvenance
Sources
- NIST AI Risk Management Framework (AI RMF 1.0), NIST, 2023 (govern, map, measure and manage as a continuous, iterative lifecycle tailored to organization size and maturity, not a one-time check)
- ISO/IEC 42001:2023, Artificial Intelligence Management System (the first auditable international standard for a documented, managed AI governance system)
- OWASP, Top 10 for Large Language Model Applications, 2025 (prompt injection ranked LLM01), with the OWASP Gen AI Security Project extending it to agentic systems, 2026
- SQ Magazine, LLM Hallucination Statistics 2026 (customer-support chatbot hallucination roughly 15 to 27 percent in live use), 2026
- OpenObserve, LLM Monitoring Best Practices: Complete Guide for 2026 (drift as a slope needing a baseline; cost reviewed weekly as a first-class metric), 2026
- StingrAI, AI Red Teaming: How to Test LLM and Agentic Apps in 2026 (test before and after deployment and again on every meaningful release), 2026
Begin with where the business stands.
No obligation. The deliverable is a measured starting position and the corrections that move it most.