RAG and company knowledge
Connect models to authorized, current information with deliberate retrieval, citations, access control, and measurable retrieval quality.
LLM INTEGRATION CONSULTING
Build LLM workflows people can actually trust.
I help teams connect language models to products, data, and business operations with the permissions, evaluation, observability, and recovery paths required for production.
Tool-using AI agents
Give models narrow, validated tools for research, browser workflows, internal operations, and multi-step tasks with approval gates.
Product LLM integration
Build streaming interfaces, structured outputs, conversation state, provider abstraction, fallbacks, and cost-aware model routing.
Evaluation and observability
Create representative test sets and trace prompts, tool calls, latency, cost, errors, and task outcomes across releases.
THE ENGINEERING PROBLEM
The model is only one component.
A polished demo can hide the hard parts: what the model is allowed to access, which actions it may take, how bad outputs are caught, and what happens when a provider slows down or fails.
A production LLM system needs deterministic boundaries around probabilistic behavior. That means typed inputs and outputs, server-side permissions, scoped tools, explicit budgets, human approval for high-impact actions, and tests built from the real tasks your users need completed.
I work across that complete system, from selecting models and designing context to integrating tools, measuring quality, and making the workflow observable after launch.
COMMON ENGAGEMENTS
Where LLM integration creates practical leverage.
Internal knowledge assistantsPermission-aware search, retrieval, answers, and source citations.
Research and browser agentsControlled tool use for data collection, comparison, and reporting.
Document workflowsExtraction, classification, validation, review, and structured handoff.
AI product featuresCopilots, analysis, generation, and workflow automation inside existing products.