Helping organizations move from AI experimentation to production-ready, governable systems

AI Architecture & Adoption 

Designing operable architectures for AI-enabled systems (GenAI, RAG, agents), with enterprise constraints as first-class concerns

Enterprise Readiness for AI-enabled Systems

Establishing platforms, guardrails, and integration patterns across data, security, and core systems — preventing pilots from becoming long-term liabilities.

Strategic Technology Advisory 

Independent, vendor-neutral architecture judgment for CTOs and Boards — reviewing and challenging decisions before they become costly commitments.

Prototyping for Decisions 

Focused prototypes to validate feasibility, economics, and organizational readiness — not innovation theatre.

Services 

Solution Architecture: 

• Leading the end-to-end design and architecture of AI based systesm e.g. autonomous and semi-autonomous AI agents (including multi-agent systems) that orchestrate complex, cross-functional business workflows

• Own strategic and hands-on technical decisions for models, orchestration frameworks, vector databases, runtime platforms, observability tooling, and policy/guardrail layers to balance performance, safety, and costs

• Drive the full lifecycle of agentic AI solutions, from PoCs through pilots, production rollout, and continuous optimization

• Build, mentor, and retain a high-performing team of ML/AI engineers, agent/prompt engineers, and product partners focused on delivering high-value agentic use cases.

• Define, implement, and enforce governance for safety, security, privacy, and ethics of autonomous agents, including human-in-the-loop controls, escalation paths, and clear accountability for decisions.

• Partner with security, risk, legal, and compliance functions to embed organizational policies and external regulations into agent behavior, access controls, and data handling.

• Design and implement robust monitoring and observability for agents, covering reliability, accuracy, latency, drift, hallucination patterns, business KPIs, and critical failure modes, with clear remediation runbooks.

• Design end-to-end AI/ML ecosystems, including data modeling, agentic workflows, RAG pipelines, and LLM orchestration

My clients

Enterprises modernizing AI-driven platforms

CTOs / CPOs under pressure to “do AI properly”

Scale-ups transitioning from MVP to enterprise