Full-Stack Development · AI Integration · Automation
I build and harden AI-powered applications, internal tools, workflow automations, and research/data pipelines—especially where the prototype already exists but architecture, reliability, testing, or deployment still blocks shipping.
What I can take off your plate
For AI-assisted apps that mostly work but still have architecture, state-integrity, security, testing, or deployment gaps.
Fast first milestone: Repository audit + fix the highest-priority blocker + verified build/test state + exact remaining blockers.
Relevant proof: Multi-Repo Engineering Hardening, DriftGuard, TraceCrumb
For teams that need a bounded AI workflow inside a real application or operational process—not a standalone prompt demo.
Fast first milestone: One end-to-end vertical slice with structured inputs/outputs, failure behavior, and a human-control boundary.
Relevant proof: DriftGuard, Chat-to-Post Engine, SignalOps
For repeated research/data work that needs collection, normalization, deduplication, provenance, ranking, and reliable export.
Fast first milestone: One representative source integrated end-to-end through validation, dedupe, provenance, and deterministic output.
Relevant proof: Market Intelligence Pipeline, SignalOps, GitHub Issue Mining
Selected work
Converted partially complete systems into locally verified release candidates while preserving the distinction between offline proof and live production readiness.
View proof →A full-stack decision-boundary application that keeps hard constraints under deterministic control while using AI only for bounded semantic interpretation.
View proof →A research/qualification stack for ingesting evidence, normalizing it, preserving provenance, deduplicating records, ranking opportunities, and exporting deterministic downstream state.
View proof →An incident-oriented application focused on preserving the first diagnostic branch during the first minutes of an operational incident.
View proof →A provider-neutral transformation system that turns source material into evidence-bounded public artifacts with structured contracts, adversarial gates, and explicit human approval.
View proof →How I work
Separate what exists, what merely appears complete, and what still depends on credentials, infrastructure, users, or production conditions.
Work at the state, authority, integration, failure-handling, or deployment boundary that is blocking the desired outcome.
Deliver the implementation with tests/checks, explicit remaining blockers, and a clean next state rather than a vague “done.”
Evidence boundary
AI accelerated parts of implementation and analysis. The proof shown here distinguishes repository contents, offline validation, external interactions, and live/commercial outcomes. Local checks are not presented as production adoption; architecture is not presented as ROI; and unverified production behavior is labeled as such.
Start small
A good first milestone is usually one repository audit, one broken deployment path, one bounded AI feature, or one end-to-end automation slice with a verifiable success gate.