AI output auditing

AI output auditing is an automation that samples and scores the work your AI produces before or just after it reaches a customer. It captures each output your assistants draft, client emails, quotes, summaries, support replies, and checks it for the things that go wrong in practice: facts that do not match your own records, a tone off from your brand, a policy breach, a detail with no source behind it. Each one gets a risk score, and the risky ones are held for a person to approve or fix. It is not model training and it is not legal advice. It is the check between what your AI writes and what your customer sees.

Spot-checking works until volume beats it. When your team sends hundreds of AI-assisted outputs a week, a person can read a handful, and the bad one is usually not in that handful. This scores every output, not a sample of your choosing, and it applies the same rules every time instead of depending on who is paying attention that day. It does not read every one for you either, that is not realistic. What it does is grade all of them and pull the risky ones into a queue, so the human attention you do have goes to the outputs most likely to be wrong, not to whichever ones happened to get looked at.

No, and it is not built to. A person still owns the judgment on anything that matters: the held quote, the sensitive client reply, the report going to a regulator. This scores and routes; it does not sign off. What it removes is the impossible part, reading every output by hand, and the random part, hoping the right person catches the right draft. It hands your reviewer a short, ranked queue of the outputs most likely to be wrong, with the reason attached, so their time goes to real risk instead of skimming everything. Done well it makes the human check faster and far more likely to land on the output that would have caused a problem.

No, and any honest answer says so. It samples and scores, and a score is a judgment about risk, not a guarantee. Two things set the limit. First, it can only check what it can compare against: if there is no record of the right price or the real policy, it can flag that a claim is unsourced but it cannot always know the claim is false. Second, calibration is a trade-off. Tuned too strict, it flags so much that people bypass it; tuned too loose, a bad output slips through. So the setup goes to defining what "wrong" means per workflow and setting the bar against your real outputs, and the high-risk categories are always held for a person rather than trusted to the score. It lowers the odds of a bad output reaching a customer a lot; it does not make them zero.

On the input side, wherever your AI output is created and sent: Gmail or Outlook for email, Slack for messages, Intercom or Zendesk for support replies, HubSpot or Attio for CRM, Google Docs or Notion for reports and summaries. On the delivery side, the review queue lands in Slack or email, so the person who owns it sees held outputs where they already work, with the flagged reason attached. Most tools with an API can be added. The audit maps your exact stack and which outputs are worth checking first.

Usually two to three weeks. The first days map which AI outputs go where and which ones carry real risk if they are wrong. Then the scoring runs against your live outputs for a week or two, so the rules and the risk bar get calibrated on your actual work, a support reply and a client quote fail differently, before anyone relies on it. You start with the highest-stakes outputs, customer-facing quotes and replies, and widen from there once the queue is landing on real problems and not drowning your reviewer in noise.

Two parts. Tooling runs as a modest monthly cost for the scoring and any model usage, and it scales with how many outputs you check rather than a flat platform fee. Implementation is a fixed scope, quoted once the audit maps which outputs you produce, where they go, and how you want risky ones routed and held, so you are pricing a defined build, not an open-ended retainer. The audit itself is where the scope and price get set against every other opportunity in your business, priced as part of the audit, so you are not guessing at the effort up front.