Evals & Observability: Know how your AI is actually performing
Monitor, test, and measure your AI systems in production. Catch quality issues before your users do.
Key Features
Evaluation Suites
Test model accuracy, safety, and consistency on your specific use cases
Real-Time Monitoring
Dashboards for latency, token usage, error rates, and cost per query
Output Quality
Detect hallucinations, toxicity, and off-topic responses
Regression Testing
Ensure model updates don't break existing functionality
Technologies We Use
What is Evals & Observability?
Evals test whether your AI is producing correct, safe, and useful outputs. Observability tells you what's happening inside your AI systems in real time - latency, error rates, cost, and output quality. Together, they answer the question every stakeholder asks: "How do we know this thing is working?"
Benefits
Make your AI feel native to your business: faster, more accurate, and a true competitive advantage from day one.
Prove to regulators and stakeholders that your AI meets quality standards
Catch model degradation before it affects users or business outcomes
Make data-driven decisions about model updates, not guesses
Why It Matters
An AI model that worked last month might not work this month - data changes, user behavior shifts, model drift happens silently. Without evals and monitoring, you won't know until users complain or regulators ask. With them, you catch degradation early and prove performance to stakeholders with data, not promises.
What You Get
How We Deliver
We start by defining your evaluation criteria and identifying the metrics that matter for your use case and your regulators. Then we implement eval suites, set up monitoring infrastructure, and configure alerting thresholds. We integrate everything into your CI/CD and production workflows and train your team on the dashboards and response procedures.
Our Process
Assess
1–2 weeksDefine evaluation criteria, identify key metrics, establish baselines for current model performance.
Build
3–6 weeksImplement eval suites, set up monitoring infrastructure, configure alerting thresholds.
Deploy
1–2 weeksIntegrate into your CI/CD and production workflows, train team on dashboards and response procedures.
Use Cases
Clinical AI Validation
Continuous evaluation of clinical decision support models against gold-standard outcomes, with audit-ready reports.
Claims Model Monitoring
Real-time monitoring of auto-adjudication models for accuracy drift, bias detection, and processing anomalies.
Compliance Audit Readiness
Automated eval reports that show model performance, fairness metrics, and decision explanations for regulatory audits.
Related Services
AI Pipelines & MLOps
AI SERVICESBuild the infrastructure that takes AI models from experiments to reliable production systems - automated, monitored, and repeatable.
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LLM Fine-Tuning
AI SERVICESTurn a general-purpose AI into one that understands your business - your terminology, your processes, your standards.
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Predictive Analytics
AI SERVICESUse your historical data to forecast outcomes, detect anomalies, and score risk - so you act early instead of reacting late.
Learn More →Industries We Serve
Financial Services
AI-POWEREDAI that protects assets, automates regulatory burden, and gives your clients the instant, personalized experience they expect - without adding headcount.
Learn More →
Insurance
AI-POWEREDAI that automates claims from first notice to settlement, detects fraud patterns humans miss, and gives underwriters the data to price policies with confidence.
Learn More →Let's Talk
Discover how we can help your industry leverage AI for competitive advantage.
Get in Touch →Frequently Asked Questions
Common questions about Evals & Observability.
Evals test whether outputs are correct (quality). Observability tracks whether the system is healthy (performance). You need both.
Yes. We evaluate LLM outputs for accuracy, hallucination, relevance, safety, and consistency using both automated metrics and human review frameworks.
Eval reports provide the evidence regulators need - model performance over time, fairness metrics, error analysis, and decision audit trails.
Especially then. Models degrade silently. By the time you notice, the damage is done. Monitoring catches drift early.
Set up monitoring for your AI
Private AI that works with your existing systems and delivers transparent, compliant automation. Tell us where you're stuck - we'll show you what's possible.