I build AI systems — and the organizations that can successfully use them.
I design, deploy, and scale applied AI systems while helping engineering organizations turn emerging AI capabilities into measurable business and developer impact.
Recent enterprise AI transformation work at ZoomInfo, measured across production deployment, enablement, and adoption.
- 15+
- Production AI agents
- 500+
- Engineers enabled
- 95%
- Enterprise AI adoption
- 68
- Enterprise AI initiatives
LLM-powered agents deployed across 10+ business organizations.
Enabled globally in LLM development, prompt engineering, and Responsible AI.
Grew AI adoption from ~20% to 95% through deployment and enablement.
Led a portfolio spanning Engineering, Product, GTM, HR, Finance, Legal, and Ops.
Four disciplines that make AI work in production
Deploying AI successfully takes more than an API call. It requires architecture, evaluation, workflow design, enablement, and measurement working together.
AI Systems
Design and implementation of RAG systems, AI agents, LLM applications, evaluation frameworks, provider abstractions, and production AI architecture.
- Retrieval-augmented generation with grounding and citations
- Agentic workflows and tool-using LLM applications
- Evaluation harnesses for retrieval and generation quality
- Provider abstraction so models are replaceable, not load-bearing
AI Transformation
Turning AI experimentation into repeatable organizational capability through strategy, governance, workflows, and deployment.
- Use-case discovery through measured production deployment
- Reusable workflows, prompt libraries, and deployment playbooks
- Responsible AI governance and risk frameworks
- Executive alignment on where AI creates real value
Developer Productivity
Applying AI to engineering workflows, onboarding, enablement, knowledge access, and developer effectiveness.
- AI-assisted engineering and knowledge retrieval
- Onboarding and enablement systems for engineering teams
- Embedded assistants inside real developer workflows
- Measuring effectiveness, not just activity
Adoption & Enablement
Creating the systems, programs, measurement, and organizational behaviors necessary for teams to actually adopt AI.
- Enablement programs for hundreds of engineers globally
- Role-based guidance, assessments, and certification tracks
- Adoption measurement and continuous improvement loops
- Change management from experimentation to standard practice
Engineering Onboarding Copilot
Evaluated RAG for grounded engineering knowledge.
The problem
Engineering knowledge is fragmented across documentation, runbooks, architecture guidance, operational standards, and tribal knowledge. LLMs can produce fluent answers, but enterprise knowledge systems need more than fluency.
Enterprise knowledge systems need more than fluency:
- Relevant retrieval
- Evidence provenance
- Measurable quality
- Grounded generation
- Citations
- Safe abstention
Technology
Retrieval evaluation
Global corpusGlobal chunk-level retrieval evaluation over the full corpus. This is the noisier, more realistic setting — and it shows the measurable effect of reranking.
Generation evaluation
Curated suiteResults on the current curated evaluation suite. These measure the behavior of the generation and grounding layer on a defined set of cases — they are not claims of universal model accuracy.
- Grounding Accuracy
- 100%6/6
- Citation Presence
- 100%4/4
- Citation Validity
- 100%4/4
- Abstention Accuracy
- 100%2/2
- Knowledge DocumentsDocs, runbooks, standards
- Section-Aware ChunkingPreserve structure & context
- EmbeddingsOpenAI embeddings
- PostgreSQL + pgvectorVector store
- Vector RetrievalSemantic nearest neighbors
- Candidate EvidenceBroad candidate set
- RerankingBest evidence, not just nearest
- Context AssemblyGrounding context
- LLM GenerationAnswer synthesis
- Grounding / Citation ValidationVerify against sources
- Answer + SourcesCited response or abstain