Enterprise AI platform leadership—from architecture to governance.

LLMOps Platform Architecture

Design of enterprise LLM platforms that integrate governance, lifecycle management, evaluation, and deployment into a scalable operating model supporting production AI systems.

Key Outcomes

• End-to-end platform design from inference to monitoring
• Prompt management and dataset governance pipelines
• Automated evaluation (LLM-as-judge, human-in-the-loop)
• CI/CD for model deployment and promotion workflows

RAG System Design & Evaluation

Architecture of enterprise retrieval platforms focused on reliability, observability, and measurable business outcomes—combining hybrid search, evaluation frameworks, and continuous improvement.

Key Outcomes

• 30–45% retrieval accuracy improvement track record
• Hybrid search with BM25 + vector + reranking
• Evaluation harnesses tied to business metrics
• Debuggable pipelines with full observability

AI Agents & Orchestration

Design of production-grade agent systems that coordinate tools, workflows, and decision-making within defined governance, safety, and operational boundaries.

Key Outcomes

• Multi-agent orchestration with LangGraph
• Structured tool use and planning loops
• Model Context Protocol (MCP) integration
• Safety boundaries and audit-ready logging

AI-Assisted Engineering Enablement

Modernization of engineering organizations around AI-assisted development—introducing AI coding agents, agentic SDLC workflows, and AI-augmented code review with the human accountability gates needed to accelerate delivery without compromising quality or security.

Key Outcomes

• AI coding agent adoption strategy and rollout (Claude Code, Cursor)
• Agentic SDLC workflows: spec-driven development with human review gates
• AI-augmented code review integrated with quality and security standards
• Team enablement, metrics, and guardrails for sustained delivery velocity

Continuous Evaluation & Agent Observability

Production-grade evaluation infrastructure that catches silent model and agent regressions before they reach end users—combining trajectory grading, regression testing, and LLM-as-judge pipelines with human review, instrumented with modern observability tooling.

Key Outcomes

• Agent trajectory grading and prompt/tool-call regression testing
• LLM-as-judge pipelines with human-in-the-loop review
• Drift detection and production AI observability (Braintrust, Langfuse, Inspect)
• Shortened release cycles for prompt and model updates

AI Governance & Compliance

Establishment of enterprise governance and security frameworks that enable safe, auditable, and compliant AI adoption—integrating risk controls, adversarial protections (including prompt injection), lifecycle management, and regulatory alignment.

Key Outcomes

• Model approval workflows and lifecycle management
• Bias testing, safety evaluation, and adversarial risk controls (e.g., prompt injection protection)
• EU AI Act, GDPR, SOC 2, NIST AI RMF alignment
• Audit-ready documentation and rollout guardrails

Cloud Infrastructure & Cost Optimization

Enterprise cloud and AI infrastructure strategy focused on scalability, cost discipline, and production reliability—aligning architecture decisions with long-term platform and business outcomes.

Key Outcomes

• Cloud migration strategy, cost modeling, and vendor evaluation
• Model quantization (GGUF, QLoRA), intelligent routing, and multi-model orchestration
• Kubernetes-based deployment and FinOps practices
• Up to 40% infrastructure cost reduction