Enterprises need AI that is safe, compliant, and built for specific tasks. I architect retrieval-augmented generation systems with governance at the foundation, not as an afterthought.
I am a Principal AI Architect specializing in retrieval-augmented generation systems for regulated industries. My work focuses on the intersection of AI capability and enterprise requirements: compliance, auditability, cost governance, and safe operation.
Before focusing on AI architecture, I spent years building enterprise systems where failure was not acceptable. That perspective shapes everything I design. AI is powerful, but it must be constrained, monitored, and accountable.
I work with organizations that understand AI is not a product to buy but a capability to build responsibly.
The model is the easiest part. What determines success is everything around it: how data enters the system, how context is managed, how outputs are constrained, and how the entire pipeline can be audited when something goes wrong.
I prioritize what most projects ignore: ingestion pipelines, memory architecture, retrieval boundaries, and governance controls. These are where AI projects succeed or fail.
A system that does less but does it safely is more valuable than a system that does everything but cannot be trusted.
Compliance, logging, and auditability are architectural decisions made at the start, not features added before launch.
Knowing what an AI system should not do is as important as knowing what it should. Boundaries are designed, not discovered.
Every system I design has a defined purpose, measurable constraints, and a clear understanding of where it should and should not operate.
Retrieval systems designed for specific functions with appropriate chunking strategies, retrieval depths, and output constraints.
Systems built with audit trails, access controls, and data handling appropriate for regulated industries.
AI deployments that operate entirely within your infrastructure when data cannot leave your environment.
Embedding stores scoped to specific use cases, preventing unintended information leakage between functions.
Testing frameworks that measure accuracy, identify failure modes, and establish acceptable performance thresholds.
Monitoring systems that track cost per query, usage patterns, and total cost of ownership across your AI portfolio.
Every RAG system follows a deliberate lifecycle. Each stage has distinct requirements and failure modes that must be addressed explicitly.
Documents, PDFs, and other sources are converted to clean, structured text with metadata preserved for traceability.
Text is segmented according to the retrieval task. FAQ chunks differ from policy document chunks differ from technical documentation chunks.
Chunks are converted to vector representations using models selected for the specific domain and retrieval requirements.
Embeddings are stored in purpose-limited databases with access controls and namespace separation.
Queries return only relevant context within defined similarity thresholds and result limits.
The model synthesizes retrieved context into responses. It does not make autonomous decisions or take actions without oversight.
Every query, retrieval, and response is logged with sufficient detail to reconstruct the reasoning path.
A single RAG system for all purposes is a common and dangerous pattern. Each function has distinct requirements for chunking, retrieval, and risk tolerance.
| System Type | Chunking Strategy | Retrieval Depth | Risk Profile |
|---|---|---|---|
| FAQ / Help Desk | Small, self-contained answers | Shallow (1-3 results) | Lower risk, high volume |
| Employee Onboarding | Process-oriented sections | Medium (3-5 results) | Moderate risk, accuracy critical |
| Legal / Compliance | Clause-level with full document context | Deep (5-10 results with overlap) | High risk, requires human review |
| Technical Documentation | Hierarchical with code blocks preserved | Variable based on query type | Medium risk, version sensitivity |
| Customer Support | Issue-solution pairs with context | Medium with recency weighting | Customer-facing, tone sensitive |
Combining these into a single database creates unpredictable behavior, information leakage between contexts, and makes failure analysis difficult.
The patterns I see repeatedly are not technical limitations. They are organizational and architectural gaps that compound over time.
Technical excellence is necessary but insufficient. Enterprise AI leadership requires thinking about risk, cost, and organizational dynamics.
Every AI system has an accountable owner who understands failure modes and has authority to pause or modify the system.
Understanding the true cost of each interaction, including compute, storage, and human oversight requirements.
Designing systems so that failures are contained and do not cascade across functions or expose sensitive data.
Honest assessment of when custom development creates value versus when commercial solutions are more appropriate.
The willingness to reject projects that cannot be built safely within current constraints and capabilities.
Systems designed for the team that will maintain them in two years, not the team that builds them today.
A 30-minute conversation can clarify whether your organization is ready for enterprise AI and what the right first steps might be.
Schedule a ConsultationStrategic guidance for AI initiatives. Evaluating vendor claims, assessing build vs. buy decisions, and establishing governance frameworks.
Technical architecture for specific AI systems. From requirements through detailed design, with implementation guidance for your team.
Assessment of existing AI systems for risk, compliance, cost efficiency, and architectural soundness. Actionable findings, not just observations.
Ongoing support as your AI portfolio grows. Architecture reviews, incident response guidance, and strategic planning as capabilities evolve.
Enterprise AI is not about having the most advanced model or the most impressive demo. It is about building systems that work reliably, fail safely, and can be explained to anyone who asks.