Visibility into what your AI systems are doing, what they cost, where they are drifting, and what your customers are telling you that nobody has aggregated yet.
Two things that usually go missing. Operational visibility, so you know whether the system is working and what it costs. And the governance record, so when a client, an auditor or your own board asks how the AI makes decisions, there is an answer on file.
Teams can say the bot is live. They usually cannot say what proportion it resolves, what it costs per resolution, or whether it is better or worse than last month. Without that, there is no case for expanding it and no way to defend it.
A model update, a documentation change or a shift in what customers ask, and answers get worse without a single alert. The first signal is usually a complaint, by which point it has been happening for weeks.
Token spend is invisible until it is billed, and by then a badly scoped retrieval step or a chatty prompt has been running for a month. Cost per outcome is the number that matters and it is rarely tracked.
The same confusion, the same product complaint, the same missing feature appear in conversations for quarters. It is all captured, and none of it reaches the person who could act on it.
How does it decide, what data does it use, who reviewed it, what happens when it is wrong. Increasingly these arrive in procurement questionnaires, and not having a documented answer stalls the deal.
The parts that decide whether this survives contact with real data, real volume and real edge cases.
The question, what was retrieved, what the model returned, what action followed and how long each step took. Enough to reconstruct any single conversation rather than reason about aggregates.
Tracing and instrumentationThe evaluation set runs against live traffic samples, so a regression shows up as a number moving rather than as a customer complaint. Changes are compared against the baseline before they ship.
Continuous evaluationToken and infrastructure cost broken down per conversation, workflow and model, so you can see cost per resolution instead of a monthly total. That is what makes optimisation decisions obvious.
Cost attribution and optimisationWe monitor the shape of what customers are asking and how the system responds. When either shifts away from what was tested, it raises an alert rather than waiting for the quality to become visible.
Drift monitoringDecision logs, escalation records, human review actions, model and prompt versions, all retained. When procurement or an auditor asks how the system reaches a decision, the evidence already exists.
AI governanceRecurring themes clustered from real conversations, with volume and trend attached, delivered to product and leadership in a form that can be prioritised rather than a wall of tickets.
Conversation miningA 30 minute audit, no sales pitch. We map where this fits in your stack, what it would take to build, and whether it is worth doing at all.