We shipped enterprise software for three decades without AI. Then it arrived — and we went all-in.
For 30+ years Leopard Data delivered Fortune-500 systems the long way — every design doc, every line, every test by hand. The past couple of years, every phase of delivery has run through AI tooling — Claude Code, Gemini CLI, custom MCP servers — with a human architect reviewing every pass. The result on real engagements: roughly 4× delivery acceleration, judged by engineers who know exactly what the output should look like. This page is about how we build with AI; building AI into your products lives on our AI & Machine Learning page.
How We Work AI-First
Six practices, applied across every engagement.
AI-Accelerated Engineering
Research, spikes, and scaffolding in minutes instead of days — so we iterate and test faster, explore more options, and spend senior time on the decisions that matter instead of the boilerplate.
Agentic Migration Pipelines
AI agents that analyze and port whole codebases — multi-pass loops that generate documentation, feed it back to the model, and iterate — producing deterministic output with an architect reviewing every pass.
Custom MCP Servers
We build Model Context Protocol servers that extend Claude with client-specific tools and context — your repositories, your APIs, your domain knowledge — so the model works inside your world, not a generic one.
Multi-Model Fluency
Claude, Gemini, ChatGPT / Codex-style code models, GitHub Copilot — we pick the model per task and benchmark them against each other on real work, not vendor slide decks.
AI-Assisted Architecture & Docs
Design docs, C4 and Mermaid diagrams, deployment diagrams, and Confluence documentation produced with AI and reviewed by a human architect — documentation that keeps pace with the code instead of trailing it.
Team Enablement
We set up Claude Code for your engineers and PMs, teach MCP architecture, and provide prompt-engineering guidance — pairing naturally with our Prompt Engineering service so the acceleration outlives the engagement.
The Daily Toolchain
The tools we actually build with, every day.
Daily Drivers
Built In-House
The Practice on Real Engagements
Not a lab exercise — how we deliver.
An AI Agent Porting 350+ Codebases from AWS to GCP
For a national healthcare technology platform, we built an AI agent in TypeScript running Claude on AWS Bedrock with custom MCP servers to analyze and port 350+ FHIR/HL7 codebases — multi-pass documentation loops, three Claude models routed per task, roughly 4× faster than manual porting, with an architect reviewing every pass.
Read the case study In-House Product · AI-First BuildGrade My Investments — 195K Lines, One Architect, Six Months
Our own SaaS is the receipt for the practice: the entire 195K-LOC system was built AI-first by one architect in six months — and Claude is also built into the product as the language layer over a deterministic ML.NET core, with a monthly cost cap on production AI spend.
Read the case studyAI-Accelerated Delivery Inside Fortune-500 Engagements
The everyday practice: evaluating container scanning, observability, and cloud services with AI research loops; producing architecture diagrams and design docs with AI; and teaching client engineers and PMs to set up and use Claude Code and MCP on their own work.
The daily default on every engagementThe Governance Behind the Speed
Acceleration without oversight is just faster mistakes. Ours comes with rules.
Nothing ships unreviewed
AI output never goes to production without a human architect reviewing it — every migration pass, every generated diagram, every scaffolded service. The model accelerates the work; it doesn’t get the final word.
Deterministic cores where it counts
Where correctness matters, we build deterministic cores with AI language layers on top — the pattern running in Grade My Investments, where ML.NET does the repeatable math and Claude handles the language.
Cost-capped in production
Production AI usage runs under hard spend limits — GMI enforces a monthly Claude cost cap — so the AI-first practice never turns into an open-ended bill.
Looking for AI inside your product?
This page is about how we build software with AI tooling. LLM features, AI agents, RAG, and machine learning built into your products live on the AI & ML page.
Want your delivery to move this fast?
Leopard Data brings the AI-first practice — and the architect who reviews every pass — to your next build or migration. Corp-to-Corp engagements out of Plano, TX.