LLM Testing & Evaluation
Ship AI you can trust
Moving non-deterministic models into production requires continuous, automated verification. We provide you with an end-to-end evaluation framework that stress-tests answer quality, hallucination rates, and prompt injections across every release. Integrated directly into your deployment workflows, the framework gives engineering teams and risk stakeholders an unambiguous, data-driven answer: is this model ready for production?
Measurable Quality
defines empirical benchmarks for factual accuracy, context relevance, and brand tone across all model interactions.
Hardened Safety
systematically stress-tests against prompt injections, jailbreaks, and unsafe outputs to prevent corporate compliance risks.
Automated Evals
integrates regression suites directly into your CI/CD pipelines to validate every prompt, model update, and retrieval change before deployment.
Defensible Releases
replaces subjective gut feeling with audit-ready evaluation data, giving engineering and risk teams a clear basis for production sign-off.
Four stages to "Safe to Ship" AI applications
Deploying reliable, enterprise-grade AI requires an empirical, structured approach. Our four-stage value ladder guides your cross-functional team from initial risk mapping to fully managed, continuous quality assurance. Each stage delivers standalone architectural value and measurable confidence, laying the technical foundation for scalable, compliant model operations.
Discover
Define Quality & Safety: map operational risks, establish quantitative evaluation criteria, and define clear production thresholds in a 1-day scoping workshop.
Build
Test Suites & Guardrails: configure automated evaluation harnesses, adversarial edge-case datasets, and deterministic safety guardrails tailored to your stack.
Deploy
CI Integration & Telemetry: embed automated eval gates directly into your CI/CD pipelines to verify every release and monitor live production quality in real time.
Manage
Managed Quality Assurance: continuously update golden datasets, recalibrate benchmarks against new foundation models, and sustain guardrail resilience over time.
Discover: LLM Discovery Workshop
A focused working session to define objective quality and safety benchmarks for your AI application. Together with your engineering, product, and risk stakeholders, we uncover critical vulnerability vectors, map domain-specific failure modes, and establish clear criteria for what constitutes production-ready output.
Duration
1 day on-site
Scope
- Risk and quality definition
- Draft rubric
- Test plan and roadmap
Deliverables
- Production quality & risk rubric
- Four-Layer Evaluation Plan
- Prioritized Adoption Roadmap
Build: Test Framework & Guardrails
The core engineering phase. We translate your evaluation rubrics into a modular, code-based test suite that evaluates system behavior, factual fidelity, and safety guardrails. We assemble a verified golden dataset of expert-approved responses, automate core user journeys, and subject the system to systematic adversarial red-teaming before releasing it to production.
Duration
8 weeks
Scope
- Quality rubric and golden set
- Interface and end-to-end automation
- AI-assisted quality evaluation
- Adversarial and guardrail set
Deliverables
- Automated Testing Suite
- Engineered Guardrail System
- Proprietary Test Asset Package
Deploy: Production Monitoring & CI/CD Integration
Integrate the evaluation framework directly into your deployment pipeline so that every commit, prompt modification, and model update is automatically tested. In production, we continuously observe live interactions, track latency and inference costs, and trigger automated alerts the moment answer quality drifts or a safety policy is breached.
Duration
Scoped to your environment
Scope
- QA Pipeline and scheduled test execution
- Live evaluation of conversations
- Performance and cost tracking
- Drift and guardrail alerting
Deliverables
- CI/CD Pipeline Integration
- Continuous Observability, Cost & Latency Telemetry
- Automated Alerting & Incident Workflows
Manage: Managed Quality Assurance
We maintain and evolve your testing infrastructure as an ongoing managed service. Foundation model updates, changing user behaviors, and application modifications can introduce subtle regressions. We continuously calibrate your evaluation models, expand your test sets based on real-world edge cases, and tune guardrails to keep your application reliable and defensible over time.
Duration
Ongoing, monthly
Scope
- Per-release test runs and test set maintenance
- Evaluation re-validation and guardrail tuning
- Production monitoring and sign-off
Deliverables
- Continuous Golden Dataset Updates
- Verified Release Sign-Off Reports
- Monthly Reliability & Telemetry Reviews
Find out if your AI is safe to ship
Bring your AI app and the risks that worry you most. Together we will define what good and safe mean, and map a clear path to automated, continuous testing. Let's talk!
