Engineering expertise
AI Engineering & Agentic Workflows
I work with LLM integrations and AI-assisted product workflows, including contextual summaries, vendor-price extraction, and audio transcription interfaces. My engineering focus is on connecting model output to useful product context, validating structured results, and providing a review path when information is missing or uncertain. My broader toolkit includes RAG and agentic workflows.
Tools I work with
- LLMs
- OpenAI
- RAG
- LangChain
- LangGraph
- MCP
- Prompt Engineering
- Context Engineering
Related project work
Sheet Parser
An AI-assisted workflow that interprets inconsistent vendor spreadsheets, validates proposed pricing, and routes uncertain values for review before they reach shared catalogs or project estimates.
Read the case studyCRM Platform
An all-in-one CRM platform connecting customer records, sales pipelines, proposals, communication, scheduling, marketing, automation, and AI assistance in one workspace.
Read the case studyAudio Transcription
A browser-based audio transcription interface for recording, following processing status, editing transcripts, and reviewing AI-generated summaries.
Read the case studyRelated technical notes
AI / ML
Follow One Vendor Price from Source Row to Project Selection
Trace a vendor price through AI extraction, source review, catalogue storage, and project selection in an illustrative Sheet Parser workflow.
AI / ML
Human Review for AI Data: Design the Acceptance Boundary
Make extracted data reviewable with source evidence, field-level corrections, revision checks, and an explicit transition into accepted records.
AI / Agentic
Testing AI-Generated Code with an Independent Oracle
Verify generated changes with behavior-based assertions, failure cases, boundary tests, and deliberate checks that the tests can fail.