Eliminate invisible operational risks. Establish enterprise-grade trust, accuracy, and governance for your AI agents with a dedicated Center of Excellence.
- 0% Critical Hallucinations
- 95%+ Regression Pass Rate
Traditional UAT is built for deterministic software. It fails when assessing non-deterministic agent behavior, creating an invisible quality gap driven by anecdotes and subjective feedback.
- Customer Service: Wrong policy answers or missed complaints.
- Claims Processing: Incorrect document guidance or false payout expectations.
- Underwriting & Compliance: Unsupported risk interpretations or unapproved product explanations.
- Broker Support: Unapproved product or process explanations.
- Internal Operations: Flawed, ungrounded summaries driving critical operational actions.
It’s Smart, Repeatable, Metric-Driven Governance. If software quality is measured through testing, AI quality must be measured through evaluation. Testing checks basic functionality, but evaluation checks institutional trustworthiness.
- Agent Inventory & Assessment: Baseline existing agents, initiatives, tech stacks, and business criticality into a secure agent register with explicit risk tiering.
- Enterprise Evaluation Framework: Move away from subjective testing. Introduce structured scoring rubrics and severity definitions across task completion, safety, and orchestration.
- Evaluation Infrastructure: Stand up automated test harnesses, custom benchmark datasets, and real-time validation dashboards to fill existing tooling gaps.
- Knowledge Transfer & Enablement: Equip delivery teams with CI/CD patterns, reusable scripts, production readiness checklists, and automated approval workflows.
Our “Pilot First, Then Scale” Operating Blueprint.
- Select & Isolate: Target 2–3 high-value enterprise agent applications to baseline.
- Automate Suites: Deploy evaluation suites against fixed, domain-specific benchmark datasets.
- Optimize Frameworks: Systematically improve prompts, retrieval mechanics, orchestration layers, and model-data fit.
- Operationalize CI/CD: Establish hard gates and production readiness thresholds within the live delivery lifecycle.
Standardized quality gates for AI agents prior to commercial or operational release.
| Metric | One-line Definition | Minimum Threshold |
|---|---|---|
| Accuracy Score | Correct, complete, and approved-source-aligned answers across benchmark test cases. | 90–95% (By risk level) |
| Critical Hallucinations | Invented or unsupported high-risk claims that could mislead users or operations. | 0 |
| Critical Safety Failures | Privacy, harm, unauthorized advice, or customer-risk violations in any response. | 0 |
| Policy Compliance Score | Responses following internal enterprise policy, regulation, approved wording, and conduct controls. | 95%+ |
| Regression Pass Rate | Existing evaluation tests that still pass after model, prompt, data, or workflow changes. | 95%+ |
| Escalation Accuracy | High-risk, complaint, exception, or unclear cases correctly routed to a human or process. | 95%+ |
| Grounding Score | Answers supported by the correct approved document, knowledge source, or system record. | 90%+ |
- Reduced Operational Risk: Mitigate non-deterministic model failures before production deployment.
- Enterprise AI Governance: Clear institutional line of sight into unknown agents, overall accuracy, and runtime risks.
- Data-Driven Investments: Concrete engineering metrics to justify, scale, or halt specific AI investments.
- Faster Adoption: Reusable tooling and automated operating playbooks that cut down time-to-market.
- CI/CD Pipeline Integration: Seamless webhooks and automated evaluation scripts triggered during deployment.
- Enterprise-Grade Security: SSO, role-based access controls (RBAC), and compliance-grade audit logs for every evaluation run.
- Model Agnostic: Out-of-the-box support for commercial LLMs, open-source models, and custom orchestration pipelines.