The observability layer that keeps deployed AI accurate, safe, on-budget, and reliable
An AI feature that worked on launch day is not an AI feature that works today. Models drift, prompts rot, costs creep, and a single upstream change can quietly turn a helpful assistant into a confident liar. Most teams find out from an angry customer or a shocking invoice. AI monitoring is the instrumentation that catches it first — evals, tracing, guardrails, and alerting wired around your AI so you know it's still doing its job before anyone else does.
Traditional software is deterministic: the same input gives the same output until someone changes the code. AI is not. The same prompt can return a different answer today than it did last week, because the model provider shipped an update, your data shifted, a knowledge base went stale, or a retrieval step started pulling the wrong context. Nothing in your codebase changed — and the behavior changed anyway. That's the core reason AI needs its own monitoring discipline, separate from the uptime dashboard that watches your servers.
The failure modes are specific and expensive. Hallucination: the model states something false with total confidence, and without evals nobody catches it until it reaches a customer. Drift: answer quality slides a few percent a month until the feature is quietly useless and nobody can point to the day it broke. Cost blowout: a prompt change or a runaway retry loop doubles your token spend and you find out on the invoice. Latency creep: p95 response time climbs past the point where users give up and abandon. Prompt injection and unsafe output: someone talks your agent into doing something it was never supposed to do. None of these throw a 500. Your servers stay green while the AI silently fails, which is exactly why standard monitoring never sees them coming.
The deeper problem is trust. Once a team stops being able to say with confidence that its AI is right, safe, and affordable, it either rips the feature out or leaves it running on hope. Monitoring is what lets you keep shipping AI without either of those outcomes — it turns invisible failure into a signal you can act on, and it turns 'we think it's fine' into a number you can defend. That's the difference between shipping AI once and operating it for years, and operating it is where the real risk and the real value both live.
You can't manage what you don't measure, and 'it seems fine' is not a metric. We build evaluation suites tailored to your use case — graded test sets, LLM-as-judge scoring, and golden examples — that run continuously against real and synthetic inputs. When quality slips, the eval score moves before your customers notice, so a regression becomes a caught alert instead of a churned account.
Hallucination and drift are the two failures that erode trust fastest and show up latest. We instrument groundedness checks that flag answers unsupported by your source data, and we track eval scores over time so a slow slide in quality surfaces as a trend, not a surprise. When a provider silently updates a model behind an API, our baselines catch the behavior change the day it happens.
AI costs scale with usage in ways that are easy to lose control of — a retry loop, a bloated prompt, an over-eager context window, and spend doubles overnight. We meter tokens and dollars per feature, per model, and per user, and we watch p95 and p99 latency so slow paths get caught before users abandon them. You get a real-time picture of what your AI costs and where the money goes.
When an AI feature misbehaves, 'the model did something weird' is not a diagnosis. We instrument full tracing across the chain — prompt, retrieved context, tool calls, model response, and post-processing — so you can replay any request and see exactly where it went sideways. Structured logs turn a vague complaint into a reproducible case you can fix.
Monitoring that nobody sees is just expensive logging. We put guardrails inline — input filtering, output validation, prompt-injection defense, and safety checks — so bad responses are caught before they ship, and we wire alerting into the channels your team already lives in. A threshold breach becomes a Slack message or a page, not a discovery three weeks later.
Because AI you don’t measure quietly degrades and burns budget.
We start by cataloging every AI touchpoint you run — models, prompts, agents, retrieval, integrations — and identify where quality, cost, safety, and reliability are currently flying blind. You get an honest picture of your real exposure.
Every model, prompt, agent, and integration in production.
Where hallucination, drift, cost, and latency risk actually live.
Current quality, spend, and latency so change is measurable.
Monitoring is only useful if it measures the right things. We design eval sets from your real inputs, choose the metrics that map to your business risk, and set thresholds that trigger action instead of noise.
Test sets and scoring built around your specific use case.
Quality, cost, latency, and safety signals tied to real outcomes.
Tuned to catch problems without drowning your team in noise.
This is the step advisors can't do. We write the tracing, wire the evals into CI, deploy the guardrails inline, and connect alerting to the tools your team already uses — working software running in your environment, not a slide deck.
Tracing, logging, and evals wired into your actual code.
Inline input/output checks running in production.
Slack, PagerDuty, or email hooked to real thresholds.
AI monitoring is a living system. We review the signals, cut false alarms, expand eval coverage as new edge cases appear, and adjust baselines when providers update their models — so the monitoring stays as sharp as the day it shipped.
Regular passes to confirm alerts are firing on what matters.
New edge cases folded into the test suite as they surface.
Re-baselined when models or data shift underneath you.
Most firms selling 'AI governance' hand you a framework and a maturity model and leave the actual instrumentation to you. We're an engineering shop that added AI to a build practice, not a consultancy that rebranded — so we write the tracing, wire the evals, and deploy the guardrails into your codebase ourselves. When we're done the monitoring is running in production, catching real failures, not described in a report you have to go implement.
Talk to an AI engineer →AI features shipped inside a product face constant model updates and unpredictable user inputs — quality can regress silently between releases while every server stays green.
We gate every prompt and model change behind eval suites in CI, meter token cost per feature and per customer so usage-based pricing stays profitable, and trace every request so support can reproduce 'the AI said something weird' instead of guessing.
Learn more →In finance a wrong AI answer is a compliance event, and an unmonitored model can drift into advice it was never meant to give — with regulators and auditors watching.
We enforce groundedness checks so outputs trace back to approved sources, log every request immutably for audit, and put guardrails inline that block responses outside policy — with alerting the moment quality or safety signals cross a line.
Learn more →Healthcare AI is YMYL under a magnifying glass: a hallucinated dosage or a leaked identifier is a patient-safety and HIPAA problem, not just a bug.
We build evals that flag unsupported medical claims, redact PII on the way into and out of the model, and hold human-in-the-loop review on high-stakes outputs — with drift monitoring so accuracy never quietly slides below the safety bar.
Learn more →Legal AI that cites a case that doesn't exist is the field's most famous failure mode — and it happens because nobody was checking the citations against reality.
We wire citation-verification evals that confirm every referenced authority exists and says what the model claims, trace the retrieval chain so a bad answer is diagnosable, and alert when groundedness scores drop on any practice area.
Learn more →AI shopping assistants and support bots run at high volume, where a small quality slip or a runaway cost multiplies across thousands of conversations before anyone notices.
We track cost and latency per conversation so spend spikes get caught same-day, run evals on product accuracy and tone at scale, and guardrail against off-brand or unsafe replies before they reach a shopper mid-checkout.
Learn more →Internal agents and automations get trusted precisely because they're out of sight — which is exactly why a degraded one can process bad data for weeks unnoticed.
We instrument agent tool-calls with tracing and output validation, set thresholds on success and error rates, and alert your team the moment an automation starts failing — so an internal AI can't quietly corrupt a workflow behind the scenes.
Learn more →Automated quality checks that gate every prompt and model change before it reaches production.
Quality, cost, latency, and safety signals in one place — running in your stack, not a static report.
Inline input/output validation and injection defense deployed and enforcing your policies.
Threshold-based alerts routed to Slack, PagerDuty, or on-call — so failures surface in hours, not weeks.
Because uptime and AI quality are different things. Your servers can be perfectly healthy while the model quietly hallucinates, drifts, or burns twice the tokens it did last month — none of which throws an error. AI monitoring watches the behavior of the model itself: is it still accurate, still safe, still on-budget, still fast. That layer is invisible to a standard uptime dashboard.
Evals are automated tests for AI output. Instead of checking whether code returns the right value, they score whether the model's answer is correct, grounded, on-tone, and safe — using graded test sets, rules, and sometimes a second model as judge. They matter because they turn 'it seems fine' into a number you can track and gate releases on, so a quality regression gets caught before it ships instead of after a customer complains.
We integrate with what you already run. We instrument with OpenTelemetry-compatible tracing and can route metrics and alerts into your existing observability and on-call tools rather than forcing another dashboard on your team. Where a dedicated AI-eval or LLM-observability platform genuinely fits, we'll recommend and wire it in — but the goal is one coherent picture, not tool sprawl.
By attributing spend to a source you can act on. We meter tokens and dollars per request, per feature, and per user, then alert on anomalies — so a runaway retry loop or a bloated prompt gets caught the day it starts, not on the invoice. We also flag where a cheaper model tier or a tighter context window would cut cost without hurting quality.
Yes — and often the honest answer is that a prototype needs to be hardened before it can be monitored properly. We rebuild AI-prototyped apps into production software, which almost nobody else offers, and instrument observability as part of that work. If the prototype is solid, we'll wrap monitoring around it as-is; if it's fragile, we'll tell you straight and fix the foundation first.
Basic tracing, cost metering, and alerting can be live in a couple of weeks — enough to stop flying blind quickly. Robust eval suites and tuned guardrails build up over the following weeks as we capture real inputs and edge cases. It's not a one-time install; monitoring is a living system we keep sharp as your models, data, and providers change underneath it.
Book an AI monitoring assessment and we'll map where your deployed AI is flying blind — quality, hallucination, drift, cost, latency, and safety — then show you exactly what to instrument first and build it into your stack.
