Production accuracy monitoring for AI agents. Detect hallucinations in real time, score output quality, and catch failures before they cost you $340K.
Real story: In Q1 2026, an enterprise AI agent hallucinated $47M in inventory data — the error went undetected for 3 weeks because nobody was monitoring output accuracy. Don't let this be your headline.
Full-spectrum accuracy monitoring for every agent in your fleet.
Real-time detection of factually inconsistent, fabricated, or contradictory agent outputs. Custom thresholds per agent and use case.
Every agent response gets a live accuracy score (0–100). Set minimum thresholds — agents that dip below trigger alerts, pauses, or escalations.
Fact-check agent outputs against your source documents, databases, and knowledge base. Flag ungrounded claims before they reach customers.
Slack, Telegram, or PagerDuty alerts when accuracy dips below threshold. Pre-configured escalation paths — auto-pause agents on critical failures.
Track accuracy trends per agent, per model, per tool over time. Spot degrading performance before it becomes a production incident.
Run agents through our accuracy test suite before deploying to production. Benchmark scores, compare model versions, gate releases by quality.
Agent failures that cost enterprises millions — detected before they hit production.
Agent fabricates numbers, dates, or facts. "Revenue grew 23%" when it actually dropped. $47M inventory error in real incident.
Agent says different things to different customers, or contradicts itself mid-conversation. Erodes trust and creates compliance risk.
Agent calls the right tool with wrong parameters, or passes bad data between tools. Cascading failures propagate undetected.
Model update or prompt change silently reduces output quality. No one notices until customers complain or a compliance audit flags it.
Agent enters a loop of self-correction, burning tokens and API credits while producing increasingly wrong outputs.
Agent gives advice that violates regulatory rules (financial advice, medical claims, legal opinions). Undetected = fines.
Drop-in accuracy monitoring. Your agents keep running — we just watch what they say.
Add our SDK or point your agent's output stream to our API. Works with any LLM provider — OpenAI, Anthropic, open-source, or custom.
Define accuracy thresholds, hallucination sensitivity, and escalation rules per agent. Start with pre-configured profiles and tune in minutes.
Every output is scored, verified, and logged. Low scores trigger alerts, agent pauses, or human escalations. See quality trends on your dashboard.
Most companies deploy agents and hope for the best. We don't recommend hope as a monitoring strategy.
| Capability | With AI Suite Monitor | Without |
|---|---|---|
| Hallucination detection | ✅ Real-time, per-output | ❌ Customer reports it |
| Accuracy scoring | ✅ 0–100 per agent call | ❌ Gut feel |
| Quality trends | ✅ Dashboard + weekly reports | ❌ None |
| Auto-pause on failure | ✅ Configurable thresholds | ❌ Incident after incident |
| Pre-release testing | ✅ Gate by accuracy score | ❌ Deploy and pray |
| Cost per failed project | ✅ £149/mo vs £340K failure | ❌ £340K average |
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We use a multi-pass approach: factual consistency checks against your source data, cross-referencing with knowledge bases, and statistical outlier detection. Each output is scored 0–100 with a per-component breakdown.
No. Our accuracy checks run asynchronously with sub-200ms latency. Your agent output is streamed through a pass-through proxy — we score it after delivery. No blocking, no added latency.
Security monitoring protects against external attacks (prompt injection, tool abuse). Accuracy monitoring protects against internal failures (hallucination, quality degradation). Different problems, complementary tools.
Yes. Each agent gets its own accuracy policy. A customer-facing sales agent might need 95% minimum, while an internal data aggregator can run at 80%. Thresholds, escalation paths, and notification channels are per-agent configurable.
Configurable actions per agent: send alert (Slack/Telegram), auto-pause the agent, escalate to human review, or route to a fallback model. History shows 74% of low-accuracy events are caused by model drift or prompt changes — both fixable in minutes.
Absolutely. Every accuracy score, flagged hallucination, and escalation is logged to a tamper-proof audit trail. Exportable for SOC2, ISO 42001, and EU AI Act compliance evidence.
Under 15 minutes if your agents use OpenAI or Anthropic — just add our proxy endpoint. Custom integrations take under an hour. We'll send you a one-page integration guide with cURL examples for Python, Node.js, and Go.
£149/month per agent. No setup fee. Cancel anytime.