The problem this solves
AI systems fail quietly. A model update changes tone overnight, a data source drifts and answers get subtly worse, costs creep as usage grows, and the first time anyone notices is when a customer or a rep complains. Most companies that ship AI automation have nobody assigned to watch it: engineering considers it done, business assumes it works, and the system degrades in the gap between them.
How we work
We take ownership of the watching. The first cycle sets the baseline: what each AI system is supposed to do, what good output looks like, and which signals we can measure, from quality samples and error rates to usage volumes, latency, and spend. Where logging is missing, we add it, because an unmeasured agent cannot be managed.
Every cycle after that follows the same rhythm: review the signals, sample and grade real outputs, investigate anomalies, and ship fixes. That means prompt and instruction updates, context refreshes, guardrail adjustments, or escalation rule changes. Model and platform updates get tested before they surprise you.
You get a monthly report a business owner can read: what the systems did, what changed, what it cost, and what we recommend next. Hands-on work happens within an agreed monthly hour band; larger rebuilds are scoped separately so monitoring never gets displaced by firefighting.
Deliverables
- Monitoring baseline and quality rubric per AI system
- Monthly quality, usage, and cost report in plain language
- Drift and regression detection with investigation notes
- Implemented fixes: prompts, context, guardrails, escalation rules
- Tested responses to model and platform updates before they hit production
What buyers ask before scoping.
Which AI systems can you monitor?
Both HubSpot-native Breeze features and custom-built agents or LLM automations, including systems we did not build. The first cycle is an inventory: what exists, what it touches, and what is observable today. Anything without logs gets instrumented before we commit to quality numbers on it.
What happens when you find a problem bigger than a tune-up?
Fixes that fit the monthly hour band are simply done. Anything that amounts to a redesign, like replacing a model, rebuilding a data pipeline, or rearchitecting an agent, gets a separate scoped proposal with its own timeline. The boundary is explicit in the retainer doc, so monitoring hours never silently turn into a rebuild budget.
Can you monitor systems another vendor built?
Yes, provided we get access to configurations and logs. The first cycle documents how the system works, which usually improves on whatever handover documentation exists. We coordinate with the original vendor where they are still involved, or take full operational handover where they are not.
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