Key technical work and initiatives.
Reads two printings of one cookbook and returns a catalogue of sauces, every claim traceable to the line it came from. 151 preparations in the 1909 witness, 57 that state a parent, 94 that state no base at all — the measured bar anything cleverer has to beat. Deterministic extraction behind four layers with no runtime dependencies, so a model can be added later without touching the provenance guarantees. Corpus committed, so a clone runs offline. Counts are the v0.7.0 README census. MIT.
Fits large open models onto one GPU by measuring which layers survive being crushed. Scans per-layer quantization damage, solves a mixed-precision recipe against a hard VRAM budget, then packs it. Three published packs, each measured against its baseline with the losing numbers printed: Nemotron Super 49B on a 24 GiB card, Nemotron 3.5 Lightning 30B-A3B entirely on a 16 GiB card, and Gemma 4 31B solved for 86k tokens of context beside Google's own 4-bit build. Twenty recorded data points in the evidence ledger, losses included. MIT.
The compliance gateway for Google ADK — encrypted session storage in 5 minutes. Drop-in replacement that encrypts state and conversation history at rest using Fernet, closing the encryption gap for PHI, PII, and financial data. 4 dependencies. Apache-2.0.
Python docstring quality vetting that catches what linters miss. 31 rules across presence, completeness, accuracy, rendering, and visibility — including git-based staleness detection via diff and blame. Production/Stable, ~3,300 downloads. MIT.
Designed and built an enterprise generative AI agent framework adopted by multiple teams. Standardized how LLM-powered agents are built, tested, and deployed with built-in guardrails, observability, and prompt management.
Architected a scalable document processing pipeline that handled 500K+ documents. Automated classification, data extraction, and validation, replacing a manual review process and significantly reducing turnaround time.
Built an automated video processing system for analysis and content extraction at enterprise scale. Designed the end-to-end architecture from ingestion through output delivery.
Designed end-to-end CI/CD pipelines for ML model deployment across multiple environments. Established testing, versioning, and monitoring standards that became the team's baseline for all new projects.