# Evals & Observability — Suvegasoft

> Production-grade evaluation systems. Tracing, monitoring, and continuous improvement pipelines for AI that stays reliable.

- Canonical: https://suvegasoft.co.uk/services/evals/

## Key Benefits

- Catch AI failures early
- Full request tracing
- Quality benchmarks
- Continuous improvement

## Why Choose This Service

### Quality Metrics

Track accuracy, relevance, coherence, and custom metrics. Know exactly how your AI is performing, not just that it's running.

### Full Tracing

See every step of every request. Prompts, retrievals, model calls, tool usage. Debug issues in minutes, not days.

### Regression Detection

Catch quality degradation before it hits users. Automated alerts when metrics drift from baselines.

### Benchmark Suites

Custom evaluation datasets for your use case. Run benchmarks on every deployment to ensure quality.

### Real-time Monitoring

Dashboards showing live performance. Latency, error rates, token usage, and cost tracking.

### Continuous Improvement

Identify failure patterns and improvement opportunities. Data-driven iteration on prompts and retrieval.

## Our Implementation Process

1. **Metrics Definition** (1 week) — Define what "good" looks like for your AI system. Establish baseline metrics and quality thresholds based on your use case.
2. **Instrumentation** (1-2 weeks) — Add tracing and logging to your AI pipeline. Capture prompts, contexts, outputs, and metadata for every request.
3. **Eval Pipeline Setup** (2-3 weeks) — Build automated evaluation pipelines. Create benchmark datasets, configure quality checks, and set up CI/CD integration.
4. **Dashboards & Alerts** (1 week) — Deploy monitoring dashboards and alerting. Train your team on using the observability stack for debugging and improvement.

## Frequently Asked Questions

### What evaluation frameworks do you use?

We work with Braintrust, Arize Phoenix, LangSmith, Weights & Biases, and custom solutions. The choice depends on your stack, scale, and specific needs. We help you select and implement the right tools.

### How do you measure AI quality?

Through a combination of automated metrics (relevance scores, factuality checks, latency) and human evaluation for nuanced quality. We build custom evaluation criteria for your specific use case.

### Can you monitor RAG systems?

Yes. We trace the full retrieval pipeline: query embedding, vector search, chunk ranking, context assembly, and generation. You'll see exactly which documents influenced each answer.

### What about agent observability?

Agent traces show every reasoning step, tool call, and decision. See why an agent chose a particular path and where complex workflows succeed or fail.

### How do you handle sensitive data in traces?

We implement PII redaction, data masking, and retention policies. Traces can be stored on-premise for sensitive applications. You control what gets logged and for how long.

### Can evals run in CI/CD?

Yes. We integrate evaluation suites into your deployment pipeline. Every PR can run against benchmark datasets, blocking deployments that regress quality.

### What does an evaluation dataset look like?

A curated set of inputs with expected outputs or quality criteria. We help you build datasets from production traffic, edge cases, and known failure modes specific to your application.

### How quickly can you identify issues?

Real-time monitoring catches issues immediately. With proper tracing, you can go from alert to root cause in minutes. No more guessing why the AI gave a bad answer.

## Work With Us

Book a discovery call to discuss your use case: https://suvegasoft.co.uk/contact/