Continuous Quality

Continuous quality scoring on your live AI traffic.

We instrument your production stack to score every LLM output, detect quality drift, and alert on anomalies in real-time — delivering weekly reliability scorecards to your team and stakeholders.

100%

Output Coverage

Real-time

Drift Detection

Weekly

Reliability Scorecards

Zero

Latency Impact

Why this matters

Evals run at deployment time. But model providers push silent updates. User query distributions shift. Retrieval corpus goes stale. Production AI quality drifts gradually and quietly — until a user screenshots a bad response and it goes viral. Point-in-time evals don't catch this. Continuous monitoring does.

How We Do It

A structured process, every engagement.

01

Instrument production traffic

We capture LLM calls, inputs, outputs, and metadata from your production environment without impacting latency or user privacy.

02

Establish output quality baselines

Baselines established per endpoint and model — quality score, response length distribution, refusal rate, latency profile.

03

Run continuous quality scoring

Every production LLM response scored against quality dimensions in real time — not sampled, not batched.

04

Alert on drift and anomalies

Configured alert thresholds for quality drops, unusual refusal spikes, latency degradation, and anomalous output patterns.

05

Deliver weekly reliability scorecards

Quality trend report delivered weekly — suitable for internal teams and for stakeholder or board reporting.

What You Get

Tangible deliverables, not slide decks.

Production instrumentation (zero latency impact)
Quality baseline per endpoint and model
Continuous per-output quality scoring
Drift and anomaly alert configuration
Weekly reliability scorecards
Monthly executive quality summary

Who It's For

Built for teams where AI reliability is non-negotiable.

Post-launch AI teams

Deployed AI with no ongoing quality measurement — running blind to what users are actually receiving.

AI teams with SLAs

Quality or accuracy commitments to customers that require continuous verification, not annual audits.

Teams scaling AI usage

More users, more traffic, more model calls — the probability of quality incidents grows linearly without ongoing monitoring.

Ready to get started?

Book a free 30-minute AI Reliability Assessment. We'll review your stack, identify your highest-risk failure modes, and show you exactly what to fix first.

Book Your Free Assessment →