Evals & Observability
Production-grade evaluation systems. Tracing, monitoring, and continuous improvement pipelines for AI that stays reliable.
- ✓ Production-ready implementation
- ✓ Strong software engineering foundations
- ✓ Scalable and maintainable solutions
- ✓ Expert guidance throughout
Why Choose This Service
Production-ready solutions with proven results
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
From concept to production in 8-12 weeks
Metrics Definition
1 weekDefine what "good" looks like for your AI system. Establish baseline metrics and quality thresholds based on your use case.
Instrumentation
1-2 weeksAdd tracing and logging to your AI pipeline. Capture prompts, contexts, outputs, and metadata for every request.
Eval Pipeline Setup
2-3 weeksBuild automated evaluation pipelines. Create benchmark datasets, configure quality checks, and set up CI/CD integration.
Dashboards & Alerts
1 weekDeploy monitoring dashboards and alerting. Train your team on using the observability stack for debugging and improvement.
Compare AI Solutions
Choose the right approach for your specific needs
| Feature | RAG & GraphRAG | LLM Fine-tuning | AI Agents |
|---|---|---|---|
| Best For | Dynamic knowledge, Q&A | Domain-specific tasks | Complex workflows |
| Setup Time | 2-4 weeks | 4-8 weeks | 3-6 weeks |
| Cost | $$ | $$$ | $$ |
| Accuracy | High with good data | Very high | Variable |
| Maintenance | Low | Medium | High |
| Use When | Need latest information | Need consistent behavior | Need autonomy |
RAG & GraphRAG
- Best For
- Dynamic knowledge, Q&A
- Setup Time
- 2-4 weeks
- Cost
- $$
- Accuracy
- High with good data
- Maintenance
- Low
- Use When
- Need latest information
LLM Fine-tuning
- Best For
- Domain-specific tasks
- Setup Time
- 4-8 weeks
- Cost
- $$$
- Accuracy
- Very high
- Maintenance
- Medium
- Use When
- Need consistent behavior
AI Agents
- Best For
- Complex workflows
- Setup Time
- 3-6 weeks
- Cost
- $$
- Accuracy
- Variable
- Maintenance
- High
- Use When
- Need autonomy
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.
Still have questions? We're here to help. Contact us for more information.
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Let's discuss how we can help with your evals & observability implementation.