Stop shipping AI updates blind. Know what broke before your users do.
Most teams ship model updates with no regression tests and no monitoring. The result: silent quality drops, cost spikes, and trust erosion. We build the eval suites and observability layer that make AI deployments as rigorous as your code deployments.
Works with Azure OpenAI, OpenAI, Anthropic, open-source models, and custom fine-tunes.
What’s included
Eval suite design
Golden-answer datasets, edge-case catalogs, and automated scoring functions built for your domain — not generic benchmarks that miss your failure modes.
CI integration
Evals run on every pull request. Regressions block the merge — exactly like unit tests. No more "we'll test it in staging."
Red-team prompt library
Curated adversarial prompts that test for jailbreaks, prompt injection, data leakage, and off-topic drift. Updated as new attack patterns emerge.
Production dashboards
Latency P50/P95, token cost per request, error rate, and quality scores — all in one view your ops team can act on.
Drift detection
Automated alerts when model behavior shifts — whether from a provider-side update, a data change, or prompt drift. You find out in minutes, not weeks.
Cost optimization
Token-usage analysis, semantic caching, and model-routing recommendations. Typical result: 20–40% cost reduction with no quality loss.
How we keep it safe
Plugs into your existing stack
We integrate with your CI/CD (GitHub Actions, Azure DevOps), observability tools (Datadog, Grafana, Application Insights), and model providers. No new vendor login required.
Schema-validated eval outputs
All eval results output to a typed schema — easy to aggregate into dashboards, historical trend analysis, and automated gating decisions.
Audit trail
Every eval run is versioned and logged: dataset version, model version, prompt version, and score. Full traceability for compliance and improvement tracking.
Quality assurance
Multi-dimensional scoring
We don’t rely on a single accuracy number. Eval suites score faithfulness, relevance, safety, format compliance, and latency independently. A failure in any dimension blocks the deploy — because "mostly accurate" isn\'t a production standard.
Production failures become test cases
When a new failure mode surfaces in production, it automatically becomes a test case in the eval suite. The same issue never ships twice. Your AI gets more reliable over time, not less.
Data & privacy
- Permissioning: eval dashboards and logs are scoped to your team via RBAC — no cross-tenant data exposure.
- PII handling: eval datasets can be auto-anonymized. Production logs support PII redaction in the ingestion pipeline.
- Data boundaries: all monitoring data stays in your infrastructure. We deploy dashboards and alerting — we don't host your data.
Timeline & investment
Blueprint
10 days
Eval strategy + tooling assessment
Build
2 – 4 weeks
Eval suite + monitoring
Investment
$15K – $50K
Depends on system count
What we need from you
- • Access to the AI systems to be evaluated (APIs, prompts, model configs)
- • Subject-matter experts to define golden answers and review edge cases
- • Access to your CI/CD and observability stack for integration
- • Weekly 30-minute check-ins during setup
Security & guardrails your CISO will approve
Every AI system we ship includes these controls — in the first deploy, not a future phase.
Tool-call allowlists
The AI can only call tools you explicitly approve. Every external integration is registered with typed schemas — no unapproved operations, no unstructured side effects.
Schema-enforced outputs
Every response to a downstream system is validated against a JSON Schema before delivery. Malformed output is caught and logged, not silently propagated.
Eval suites in CI/CD
Regression tests, red-team prompts, and accuracy benchmarks run on every pull request. If eval scores drop below threshold, the merge is blocked.
Production observability
Latency P50/P95, token costs, error rates, and output drift — all in dashboards with configurable alerts. You see problems before users report them.
Human-in-the-loop gates
Configurable confidence thresholds route low-certainty decisions to a human reviewer before execution. The threshold is tunable without a code deploy.
Immutable audit trail
Every LLM call — inputs, outputs, token counts, tool invocations, cost, latency — is logged in an append-only store. Ready for compliance review or incident forensics.
Stop funding pilots that never ship.
A 10-day paid Blueprint gives you an architecture doc, risk register, costed backlog, and ROI model — artifacts you own and can act on immediately.
Get a 10-day paid BlueprintCedarNexus is an independent company and is not affiliated with Microsoft. Azure, Azure OpenAI, .NET, Microsoft Fabric, and Power BI are trademarks of Microsoft Corporation.