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.

Tracks pursuits end to end — from the first pipeline entry through proposal, award, execution, and closeout — so the same opportunity is never re-explained twice. Role-gated dashboards, win/loss analytics, and a lessons-learned engine that feeds what was learned on the last pursuit into the next one.

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.

Software fails when “what I asked for” and “what I wanted” turn out to be two different documents. This platform interviews every stakeholder independently, preserves disagreement instead of letting the loudest voice settle it, digs past a stated solution to the need underneath, and produces evidence of the requirement nobody thought to ask for. It can also read an existing production database’s structure — metadata only, never data, enforced in code and asserted by tests — and derive a candidate data model with the relationships already answered. Five weeks of discovery, compressed.

A CIO-grade read on everything a business spends on technology — hardware, software, telecom, storage and backup, and the people running all of it. Surfaces overlap, waste, and the risks nobody priced in, then sizes each remedy against the size of the business: the right fix for a five-person firm is not the right fix for a five-hundred-person one. The analysis is automated. The judgment behind the recommendations is mine.

High-volume patient referral routing where every decision has to be reproducible, explainable, and defensible a year later — so there is no model in the runtime decision path. Deterministic rules decide; AI accelerates how the system gets built, not how it answers. Developed and tested entirely on synthetic records: protected health information never enters an AI context, by design rather than by policy.

Bulk ingestion for archives that grew past the point of being searchable. An escalating extraction ladder takes the cheapest capable path first and only spends heavier processing on the pages that need it, then embeds, de-duplicates, and indexes everything — so decades of scans, contracts, and correspondence become answerable in plain language. Cost-controlled by design: tens of thousands of pages for the price of lunch.

Multi-engine malware and integrity scanning on a schedule — with an independent watchdog on a separate host and a daily digest that proves the scan actually ran. The dangerous failure mode is never the scan that fails loudly; it is the one that quietly stops running and nobody notices for four months. The same pattern covers service uptime, backups, disk, and memory across the fleet, with priority-aware alerts that stay silent overnight and escalate when they shouldn’t wait.

A role-gated suite of internal business applications behind single sign-on — resource and staffing plans, opportunity pipeline search, fee estimating, and a people-and-skills directory — sharing one component library, one auth model, and one deployment pipeline instead of six one-off tools. Separate test and production environments, and nothing reaches production without clearing the security gate first.

Templated site generation and one-command deployment for small businesses that have great word of mouth and no web presence. Build, preview, and go live on a global edge network with no server to maintain and no plugin to patch. Engineered so the marginal cost of the next client rounds to zero — and so the client owns what gets built.