Catch the regression before your customers do.
We build observability for production AI, drift detection, hallucination tracking, in production evaluation, latency and cost monitoring, and alerting that fires on the things that actually matter.
What we
actually watch.
Latency dashboards aren't enough, and pure model quality dashboards miss the operational picture. We instrument both, so you see the full health of your AI in one place.
Drift Detection
Input drift, output drift, and concept drift, measured continuously and alerted before users feel the regression.
In Production Evaluation
Sample real traffic and score quality against your eval suite continuously, so you know if quality is drifting up or down.
Hallucination Tracking
Citation grounding, factuality checks, and refusal rate metrics, surfacing when the model starts making things up.
Latency & Throughput SLOs
p50/p95/p99 with token level breakdowns, queue depth, and saturation indicators, burnable error budgets for AI services.
Cost Observability
Per request, per customer, per feature cost tracking, so you can see exactly which use cases are profitable and which are leaking money.
Smart Alerting
Composite alerts that combine quality, latency, and cost signals, so on call wakes up for real issues, not noisy drift in a single metric.
Production AI fails quietly. This is where the lights go on.
Software crashes loudly. AI degrades politely, still answering, still confident, just increasingly wrong, and the first detector is usually a customer. Every engagement here starts with one of these dark spots.
The Regression Nobody Noticed
A provider updates their model, a retrieval index goes stale, or last week's prompt change interacts badly with this week's traffic, and quality slides with no error thrown anywhere. The measured gap between instrumented and dark deployments is the whole argument: teams with continuous quality signals catch degradation in days, teams without them average months, and the loss compounds every day in between. In-production evaluation exists so the regression meets a dashboard before it meets a user.
Hallucinations With Legal Consequences
A tribunal has already held a company liable for its chatbot inventing a policy that did not exist, and paid out accordingly, which converted hallucination tracking from an ML nicety into a liability control. We instrument grounding and factuality continuously: citation checks against source documents, refusal-rate baselines, and alerts when the model's confidence outruns its evidence, so the answer that would have become a screenshot becomes a ticket instead.
The AI Bill Nobody Can Attribute
Finance sees one number: the monthly model spend, climbing. Nobody can say which feature, which customer tier, or which prompt is responsible, so nothing can be fixed. Our cost observability tags every request to its feature and customer, which routinely reveals that one workflow is quietly unprofitable while another is wildly cheap, and that attribution is the raw material our AI cost optimization practice turns into a smaller invoice.
Agents That Act Need a Flight Recorder
A chatbot that answers wrong embarrasses you; an agent that acts wrong costs money at machine speed, across a chain of reasoning steps and tool calls no single log line captures. We trace agentic systems end to end, every decision, every tool invocation, every intermediate result, replayable, so when an agent does something surprising, the question "why" has an answer measured in minutes, not a forensic project.
The Feedback Loop That Closes Itself
Observability that only alarms is half a system. The drift signals, quality scores, and failure samples we capture feed directly back into retraining and eval pipelines, so the same telemetry that catches the regression also supplies the labeled examples that fix it. Watching and improving stop being separate projects.
Three Different Questions Wearing One Word.
"Monitoring" hides three disciplines. Is the service up and fast? That is reliability engineering, the cloud and DevOps layer. Is someone attacking it? That is security operations, the adversary layer. Is the AI still giving good answers at a sane cost? That is this page, and it is the question the other two dashboards cannot see. Mature systems run all three, and confusing them is how companies end up with green dashboards and angry customers.
From dark stack to queryable history.
Most AI deployments are dark in production. We instrument them so every interaction is observable, queryable, and replayable.
Telemetry Audit
Map what's already being captured, where the gaps are, and what regressions you've missed in the past because the data wasn't there.
Instrumentation
OpenTelemetry traces, structured logs, and quality probes wired into the request path, without inflating latency.
Dashboards & SLOs
Dashboards your on call can actually read. SLOs that map to user pain. Alerts tuned to your burn rates, not vanity metrics.
Drill & Iterate
Game day exercises with synthetic regressions to validate alerting paths, then ongoing tuning as your usage grows.
What observability costs, what it saves, and what to instrument first.
AI observability is the cheapest layer of an AI stack and the one most teams skip until an incident writes the business case for them. Here are the numbers, ahead of the incident.
A focused instrumentation of one production AI system, traces, quality probes, dashboards, and tuned alerting, typically runs $15,000 to $40,000. Full-stack coverage across multiple systems with in-production evaluation and cost attribution runs $40,000 to $90,000. It is deliberately the least expensive engagement in our AI catalog, because it is built on open telemetry standards and your existing logging spine rather than a new platform purchase, and because the audit stage frequently finds you are already capturing half the data and just never wired it to a question.
Less than teams fear, by design. Trace and log capture rides asynchronously off the request path, adding negligible latency, and continuous quality scoring works by sampling, evaluating a few percent of real traffic rather than all of it, which typically lands the total overhead in the low single digits of your inference spend. Against what one undetected regression or one month of an unprofitable feature costs, it is the best-priced insurance in the stack, and every proposal states the projected overhead next to the coverage it buys.
Three concrete lines. Detection speed: instrumented teams catch model degradation in days against a months-long industry average without, and everything the model earns or protects is exposed for exactly that gap. Liability: the legal precedent now exists that a company answers for what its AI tells customers, and grounding metrics are the control that keeps that risk measured instead of discovered. And attribution: per-feature cost tracking routinely finds one workflow leaking money, and turning that off or rerouting it has paid for entire engagements in the first quarter. We baseline your current mean-time-to-detect before instrumenting, so the improvement is measured, not asserted.
Follow the money and the exposure, not the org chart. First: whatever AI output reaches customers unreviewed, because that is where the reputational and legal risk lives. Second: the highest-volume workflow, because that is where silent quality decay and cost leaks compound fastest. Third: anything an agent can act on without a human gate. Our telemetry audit ranks your systems on exactly those three axes and usually produces a first target nobody in the room expected, which is rather the point of auditing instead of assuming.
Both, and the cheaper part is underrated. Cost attribution per request and per feature is the prerequisite for every optimization that follows: routing expensive queries to cheaper models, caching what repeats, and retiring the feature that costs more than it returns. Teams operating blind cannot make any of those moves because they cannot see the target. Observability does not cut the bill by itself, but every bill we have ever cut started with the visibility this page builds, which is why the two practices are designed as one pipeline.
You own every trace, score, and dashboard, and the foundation is deliberately boring: OpenTelemetry-standard instrumentation feeding your existing stores and alerting, not a proprietary platform holding your history hostage. Vendors in this space love to make the telemetry the moat; we build it so the data outlives any tool choice, including us, and any engineer fluent in standard observability tooling can operate and extend it. The history is the asset, every production interaction queryable and replayable, and an asset you cannot export was never yours.
Bring the story of the last time production AI surprised you, what happened, and how long until someone noticed. The audit will show you which signal would have caught it, and how many other surprises are currently in flight.
Questions about
Monitoring & Observability
Datadog covers infra and latency well. AI observability adds quality, drift, and cost tracking, usually as a thin layer that feeds into Datadog and your existing alerting.
OpenTelemetry as the spine. Langfuse, Helicone, or Arize for LLM tracing. Whylogs or Evidently for drift. Prometheus/Grafana for metrics. We integrate with what you already run.
LLM as judge with calibrated rubrics, embedding distance from reference outputs, user feedback signals (thumbs, escalations), and periodic human review of flagged samples.
Yes, most engagements start with a side car instrumentation layer that captures telemetry without touching the core code path.
PII redaction at capture, encrypted storage, role based access. Where regulation requires it, we keep telemetry within your perimeter, see our on premise offering.
Stop experimenting.
Start deploying AI that works.
Book a free discovery call. Tell us what's gone wrong in production lately, we'll diagnose what to instrument first.
info@croncore.com