AI you can audit
at 2 a.m.
AIOps monitoring for production AI systems, plus security assessment, access-control review, and model validation — because “it seemed fine in the demo” is not an operations plan.
Illustrative console. Yours is configured for your services, your thresholds, and your escalation order — and the review queue exists on purpose: when confidence drops, a human gets the case.
Uptime is table stakes.
We watch the answers too.
Traditional monitoring tells you the service is up. Operating AI means also knowing the outputs are still right — measured continuously, not assumed.
Uptime & latency monitoring
Model endpoints, queues, and integrations watched around the clock — availability, response times, and error rates for every AI service in the chain.
Output quality tracking
Sampled outputs scored by human reviewers against known-good answers. Accuracy becomes a metric you can chart over time — and a trend you can act on.
Drift detection
When your documents or calls change shape — new formats, new phrasing, new edge cases — confidence signals shift. We know before you feel it.
Alerting & escalation
Thresholds you agree to, alerts that reach the right people in the right order. Quiet when things are fine. Loud, specific, and early when they are not.
Usage & cost telemetry
Calls, tokens, and spend tracked per workflow — so you know what each process costs to run and see the anomaly before the invoice does.
Incident response
When something breaks, a human is on call — 24/7. Triage, rollback, root cause, and a plain-language postmortem. No ticket black holes.
Secure the pipeline,
not just the server.
An AI system is a pipeline: data in, a model in the middle, actions out. We assess all of it — then prove the model does what it claims before it touches production.
AI security assessment
A threat review of the whole pipeline: where data enters, what the model can access, what its outputs can trigger, and where an attacker — or just a bad prompt — could bend it. Findings arrive as a prioritized fix list, not a scare deck.
Model validation
Accuracy tested against your labeled ground truth before launch, and re-tested after — on a schedule and on drift alerts. Vendor benchmarks measure someone else’s data; validation measures yours.
Access control review
Least privilege for people and services. We review who — and what — can reach models, data, and pipelines, then cut the access nobody could justify. Service accounts get the same scrutiny as staff.
Secure deployment
Enterprise document intelligence and other AI systems deployed hardened: secrets managed, endpoints private, dependencies pinned, changes logged. Runs on our own infrastructure — see Secure AI Hosting.
Validation before trust.
Every model earns its way into production the same way — and stays there only as long as the numbers hold.
Baseline
We test the model against your historical data — real documents, real calls, real records — and measure accuracy before anyone depends on it.
Threshold
Together we agree what accuracy is acceptable for this workflow, in writing. Below the line, the system does not ship.
Launch gated
Early weeks run with human review on consequential outputs. Autonomy is earned with evidence, not assumed on day one.
Re-validate
Accuracy is re-tested on a schedule and whenever drift alerts fire — because the data your model saw at launch does not stay still.
The questions you should ask any AI vendor
What exactly gets monitored?
Who sees the alerts?
How is model quality measured?
Do you monitor AI systems you didn’t build?
Ask us the uncomfortable questions. That’s the service.
How would we know it’s wrong? Who gets paged at 2 a.m.? What happens when the documents change? Bring the questions your last vendor dodged — we answer them for a living.