Legal technology consultant and systems builder focused on reliable automation for law firms.
I build the layer between legal operations and software: intake systems, workflow automation, integrations, reporting, document automation, AI-enabled tools, and the controls that keep those systems inspectable and useful in production.
My public work emphasizes a simple idea: use models for language and judgment support; use deterministic software for rules, records, gates, provenance, and verification.
Engineering methodology → How I use AI agents to build deterministic systems without trusting the agents to be deterministic.
| Project | What it demonstrates |
|---|---|
| intake-triage-mcp | A deterministic MCP server for legal intake triage with structured validation, provenance, a hard conflicts gate, adversarial regression tests, and append-only logging. No model or network calls in the decision path. System proof |
| intake-eval-harness | A reusable MCP evaluation harness for answer + execution-trace assertions, JSON/JUnit evidence, provenance, latency/call budgets, and regression gates across stdio, SSE, and streamable HTTP. |
| mhsb-intake-leak-calculator | A browser-only law-firm intake model with explicit assumptions, sourced coefficients, automated tests, accessibility checks, and zero runtime tracking. Live tool |
| llm-security-for-law-firms | A practical threat model and adoption checklist for using LLMs in law firms. Read it |
| granolacowboy.dev | Source for my technical field notes and case studies, built as a minimal static Astro site with build-time verification. Visit |
- Deterministic where failure matters. Conflicts gates, validation, calculations, audit records, and policy enforcement should not depend on a model behaving itself.
- Evaluate systems, not demos. Golden cases, repeatable tests, CI, smoke checks, and explicit failure semantics matter more than impressive one-off outputs.
- Preserve provenance. A useful answer should make it possible to identify what data, rule, assumption, or source produced it.
- Keep humans in the control plane. Automation should surface decisions and exceptions clearly instead of hiding them behind “AI.”
- Minimize unnecessary data movement. Local-first and browser-only designs are preferable when the workflow does not require a remote service.
- Measure operational outcomes. Technology should improve throughput, quality, response time, consistency, or decision visibility, not merely add another interface.
- MHSB Solutions: legal-technology strategy, implementation, workflow automation, integrations, reporting, and AI enablement for law firms.
- LexLabs: productized Lawmatics implementation systems.
- efficient.esq: law-firm AI operating-model and governance work.
- Defensive security research: hardening the systems, agents, and infrastructure used to run the above safely.
The flagship intake work is deliberately split into inspectable layers: system demonstration → deterministic MCP → golden suite → evaluation harness → release evidence.
stars is my automatically maintained GitHub research index: thousands of repositories organized into topic-specific lists across AI, agents, security, automation, infrastructure, legal technology, and adjacent tooling.
- How I use AI agents to build deterministic systems without trusting the agents to be deterministic
- Anatomy of a legal intake automation
- Website: granolacowboy.dev
- Hugging Face: huggingface.co/granolacowboy
- GitLab: gitlab.com/granolacowboy
- Kaggle: kaggle.com/granolacowboy
- MHSB Solutions: mhsbsolutions.com
- LinkedIn: linkedin.com/in/mhsb
- Email: rich@mhsbsolutions.com