Chris France

Persistent AI memory with vector similarity retrieval. Memories reinforce with use, decay over time, and resurface automatically when relevant.

Automated LLM vulnerability testing across 10 attack categories — prompt injection, jailbreaking, data extraction. Scans local and cloud models.

Private document-grounded chat. Upload documents, ask questions, get sourced answers. Fully local — no API keys, no cloud dependencies.

Total cost of ownership for GPU builds vs cloud inference. Break-even analysis across 6 hardware tiers over 36 months.

Monte Carlo capacity forecasting across compute, storage, power, and cooling. AI-powered scenario analysis with decision timelines.

Datacenter investment analysis for PE/VC. Financial modeling, M&A valuation, and deal memos across 14 global markets.

Transit asset management platform built on public MBTA data. FTA condition scoring, capital planning, and state-of-good-repair analysis across 200+ assets.

12-lecture executive education course. Strategy frameworks, live demos, and implementation playbooks for enterprise AI adoption.

Full-stack model health scoring with 31 automated metrics, 209 passing tests, C# cloud extraction pipeline, and 15 dashboard pages.

Winning hackathon entry at Autodesk DevCon 2026 (Amsterdam). AI-powered MEP audit tool that detects dead legs in hospital domestic-water systems — a leading source of Legionnaires' disease. Extracts Revit topology via Dynamo, sends it to a remote MCP server for Claude analysis, then colors elements in the model by risk severity. "AI saves lives" theme — humanitarian framing won the room.

Adversarial security suite that thinks like an attacker, not a developer. 217 individual tests across 18 attack categories — JWT forgery, RBAC and IDOR escalation, SQL/code/path injection, file-upload abuse, race conditions, supply-chain compromise, Linux privesc, post-exploitation, tunneling bypass, LLM jailbreaks, container escapes, TLS & header hardening, and AI-powered source-code review via Claude Code Security. Every production app I ship is pushed through this suite as a build gate before it goes live — the count keeps growing as new attack patterns surface.

Infrastructure control plane for AI-powered apps. App registry, GPU monitoring, deploy/rollback, LLM load balancing, API cost tracking, and an embedded AI ops assistant — all from one dashboard.

Enterprise lifecycle platform tracking projects from BD pipeline through proposals, execution, and closeout. Role-gated dashboards, win/loss analytics, and lessons learned engine.

IP-protected email triage across multiple accounts with local LLM classification, calendar-aware meeting prep, real-time action item extraction from transcripts, and prioritized briefings — all processed on-device.

Fixed-scope audits for codebases that have become unknowable — legacy systems nobody understands, AI-generated code shipped faster than anyone can review, and the security exposure running through both. Surfaces what the code actually does, where the risk lives, and what's safe to touch. Runs on my own stack: the Black Hat suite (217 adversarial tests across 18 categories) drives the security layer, and the Model Security Scanner probes any embedded AI for prompt injection and data-extraction gaps, and a private Local RAG index grounds the analysis in the codebase's own docs so findings cite intent, not just code. The tool surfaces the data; the judgment is the deliverable.

Running a 2027 Charleston wedding the way I run a software build — AI-tracked vendor management, budget modeling, and deadline, contract, and payment tracking across a dozen vendors, plus honeymoon research and document generation. My AI executive assistant parses vendor email and assembles quotes, and my Query-Driven Memory — a private, local-embedding RAG — keeps every decision and deadline persistent and makes the full set of vendor contracts and insurance certificates answerable in plain language. Proof the same AI-augmented operating discipline works on any complex project — not just enterprise software.