AI Observability.
See inside every model you run.

If your LLM or agentic application is in production, something is happening inside it right now that you can't see: drifting outputs, runaway costs, silent failures, or prompts doing things you didn't intend. Our AI Observability tool gives you real-time visibility into prompts, outputs, latency, drift, cost, and anomalies, in one pane of glass.

Explore Observability Talk to an Expert
What you can see
01Prompts & Outputs
02Drift
03Cost & Latency
04Anomalies
05Agent Traces
The Problem

You can't govern
what you can't see.

Most teams ship LLM and agentic features with the same monitoring mindset they use for traditional software: uptime, latency, error rates. But AI systems fail differently. A model can be "up" and still be wrong, biased, leaking data, or drifting away from the behaviour it shipped with.

Without AI-specific observability, those failures surface through customers, not dashboards.

What You Get

Visibility built for AI systems,
not retrofitted from APM.

Prompt & Output Logging

Every prompt, response, and intermediate step captured and searchable: the actual conversation your model is having, not just a status code.

Drift Detection

Continuous comparison against baseline behaviour, so you catch quality degradation before users do.

Cost & Performance Tracking

Token spend, latency, and throughput broken down by model, feature, and endpoint, so cost overruns get caught early rather than at the invoice.

Anomaly Alerts

Real-time flags on unusual patterns: unexpected outputs, unusual request volume, or behaviour outside expected bounds.

Agentic AI Visibility

Multi-step agent workflows traced end to end, so when an agent chain fails you can see exactly where and why.

Audit Trail

A record of AI system behaviour, not just application behaviour, for the security and compliance teams who need it.

How It Works

Drops into the stack
you already run.

Step 01

Integrate

Lightweight SDK or API-level integration into your existing LLM or agentic pipeline. No architecture rework required.

Step 02

Monitor

Every interaction is logged and streamed into a unified observability dashboard in real time.

Step 03

Detect

Drift and anomaly detection run continuously against your baselines, surfacing issues before they escalate.

Step 04

Act

Alerts route into the tools your team already uses, so response time does not depend on a dashboard check.

Who This Is For

For everyone who owns
a piece of a live AI system.

Engineering teams

Running LLM or agentic features in production with little to no visibility beyond basic logs.

Product & platform teams

Who need to catch quality drift before it shows up in customer complaints.

Finance & ops stakeholders

Who need clear, attributable cost tracking on AI spend across teams and features.

Security & compliance teams

Who need an audit trail of AI system behaviour, not just application behaviour.

Why GMAV

This tool exists because we needed it ourselves before we could sell it.

Built by the same team running AI security assessments and Axis365 in production. It is designed to plug into real production LLM and agentic stacks without requiring a rebuild, and it feeds directly into the rest of the AISec practice: what Observability surfaces, the Assessment and governance tools act on.

FAQs

Common questions about
AI Observability

Still have questions? Email [email protected] and we will respond within one business day.

Ask Our AISec Expert →
Which models and frameworks does it support?
The tool is built to sit alongside major commercial and open-source LLM providers, as well as common agent frameworks. We confirm coverage for your specific stack during scoping, before any integration work begins.
Does this require changing our existing architecture?
No. Integration is designed to sit alongside your current pipeline via SDK or API hooks rather than replace it. There is no architecture rework required to start collecting signal.
How is this different from standard APM tools such as Datadog or New Relic?
Standard APM tracks uptime and latency. This tracks model-specific signals: drift, prompt and output quality, token cost, and agentic reasoning chains. A model can be up and still be wrong, biased, or drifting, and traditional tools are not built to see that.
Can we set our own thresholds for alerts?
Baselines and alerting are tuned to your environment rather than fixed defaults, so what counts as anomalous reflects how your systems actually behave. We configure this with your team during onboarding.
Where do alerts go?
Alerts route into the tools your team already uses, so response time does not depend on someone remembering to check a dashboard.
Stop finding out about AI failures from your customers

See exactly what your
models are doing, in real time.

Before it becomes a support ticket or a headline.

Explore Observability Book a Demo Response within 1 business day