About Phil
I'm a software developer and AI implementation consultant. I help companies figure out where AI creates real value in their operations — and then I build the systems to capture it.
I've been building software for over 30 years. For most of that time, the work has centered on the same core problem: helping organizations get more done with better systems.
When AI tools became practically useful for business — not just research demos — I saw what most developers saw: an enormous opportunity to automate the tedious, repetitive work that slows businesses down. The difference is I followed through. I've spent the last several years focused specifically on applied AI — designing and building systems that solve real operational problems for real businesses.
I'm not an AI researcher. I'm not building foundation models. I'm a developer and consultant who understands how businesses operate, identifies where AI creates the most leverage, and builds production systems that deliver.
How I think about AI implementation.
I start with the business problem, not the technology. Most failed AI projects fail because someone started with “we should use AI” instead of “we need to fix this process.” I work the other way around.
I focus on high-value, high-feasibility use cases. Not everything should be automated. Not every workflow benefits from AI. Part of my job is helping you figure out what's worth building — and what's not.
I build for production, not for demos. A proof of concept that works in a meeting but fails in daily use is a waste of money. I build systems your team can rely on.
I make architecture decisions based on tradeoffs, not trends. Sometimes the right answer is an API call to a hosted model. Sometimes it's a self-hosted open-source model on your own infrastructure. Sometimes it's a simple rule-based system that doesn't need AI at all. I help you make the right call.
On privacy & open-source.
I take data privacy seriously and have deep experience with open-source AI deployments. For businesses with sensitive data — legal, financial, medical, or proprietary — I design systems that keep data where it belongs. That might mean self-hosted models, on-premise infrastructure, or carefully architected API integrations with appropriate data handling.
This isn't ideology. It's good architecture. Your AI systems should work for your business goals and your compliance requirements. I help you build accordingly.
AI-architected production systems.
These aren't prototypes or demos. They're production-grade systems built with AI as a core part of the development process — proving that with the right architect directing modern LLMs, you can ship enterprise-level code at extraordinary speed.
Ferrix — High-Frequency Trading Engine
Enterprise-grade Rust trading system designed for high-frequency execution on elite-level colocation servers. Ferrix is a complete integrated stack combining real-time execution with continuous machine learning strategy optimization — built to operate at the latency margins where microseconds determine profitability.
Architecture
- •Event-driven execution engine with actor-based position management
- •Walk-forward validation with holdout testing for ML models
- •Evolutionary parameter optimizer — 70,000+ backtests per second
- •Non-blocking hot-swap of optimized parameters during live trading
- •Tokio async runtime with Rayon parallel processing and SIMD-accelerated parsing
Why it matters
Ferrix demonstrates that AI-assisted development can produce the kind of low-level, performance-critical Rust code that typically requires deep systems engineering expertise. The entire codebase — from the actor model concurrency to the ML optimization pipeline — was architected and built using modern LLMs under experienced technical direction.
View on GitHub →ReplayState — Solana Backtesting Engine
A production analytics platform for simulating Solana transaction outcomes against real historical blocks. ReplayState combines deterministic slot replay with Monte Carlo shadow runs to estimate inclusion probability, slippage bands, and MEV exposure before capital is deployed.
Technical challenges
- •Deterministic slot replay against historical archival block state
- •Monte Carlo trial engine (1,000–100,000 runs) with stochastic ordering and contention models
- •Outcome distributions for inclusion probability, slippage bands, and MEV exposure
- •Reproducible seeded runs with signed report manifests for auditability
- •Production architecture tuned for high-volume simulation and batch analysis
Why it matters
ReplayState turns post-trade guesswork into measurable execution intelligence. By replaying real blocks and stress-testing fee strategy across stochastic conditions, teams can quantify inclusion reliability and adversarial risk before sending transactions to mainnet.
Visit ReplayState.com →TimelineSystem — Legal Timeline Workspace
A production workspace for attorneys to review timeline events, evidence, and export-ready case chronologies. TimelineSystem centralizes chronology building, source-document context, and presentation-ready output for litigation teams.
Technical challenges
- •Multi-matter event modeling across filings, evidence, and testimony timelines
- •Fast filtering and chronology search over large document and event sets
- •Export pipelines for attorney-ready timeline packets and case summaries
- •Role-based access controls for sensitive legal records and collaboration
- •Auditable history of edits, annotations, and chronology revisions
Why it matters
TimelineSystem shows how AI-assisted product development can support demanding legal workflows where chronology accuracy, evidence context, and export quality all have real-world impact for attorneys and clients.
Visit TimelineSystem.com →SettleRisk — Resolution Risk Scoring API
A production SaaS platform providing resolution risk scoring, dispute pricing, and settlement delay modeling for prediction market traders. Built on a Rust backend engineered for sub-200ms response times, serving market makers, prop trading teams, and DeFi protocols at scale.
Technical challenges
- •Rust backend designed for deterministic, low-latency risk computation
- •Customer-facing REST and gRPC APIs with batch processing (1,000+ markets per call)
- •HMAC-signed webhook delivery across 7 event types
- •Lognormal distribution modeling for settlement delay estimation
- •Multi-platform calibration (Polymarket + Kalshi) with platform-specific risk models
Why it matters
SettleRisk is a live, customer-facing SaaS product with paying users — not a side project. Building a scalable Rust API backend with complex financial modeling, secure webhook infrastructure, and production-grade reliability demonstrates that AI-assisted development works for real commercial software when guided by an architect who understands both the business domain and the systems engineering required.
Visit SettleRisk.com →The takeaway: Building enterprise-grade production systems with AI isn't about asking a chatbot to write code. It's about having a senior architect who understands system design, performance constraints, and business requirements — then leveraging modern LLMs as a force multiplier to ship at 10x the speed without compromising quality. That's exactly what I do for clients.
What working with me looks like.
I work directly with clients — no layers of account managers or junior staff. When you hire me, you get me.
Engagements range from focused audits and advisory work to full-cycle implementation projects. I'm comfortable working with founders at a whiteboard, presenting to a leadership team, or building production infrastructure. Most of my best work happens when I'm embedded enough in your operations to understand the real problems — not just the ones in the brief.
Want to work together?
If you have a business problem that AI might help solve, I'd like to hear about it.