FastTD3
A public C++17 reinforcement-learning implementation with MuJoCo integration and automated tests. It shows the engineering approach in inspectable code.
View the code
We build the systems behind the business.
Backend platforms, cloud infrastructure, and applied AI—engineered for production.
Engineering
Robotics & Physical AI
Develop simulation and robot-learning systems for scoped research and engineering programs, backed by inspectable public work.
Explore
Production AI
Connect models to real data and workflows with evaluation, permissions, human review, and operational monitoring.
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Cloud Infrastructure
Implement deployment paths, observability, and infrastructure controls for reliable releases and clearer cost ownership.
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Backend Systems
Refactor services, build APIs, repair integrations, and improve the reliability of software your operations depend on.
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Diagnostics
Fitzroy clarifies the technical question, examines the evidence, and defines scope and deliverables before implementation.
Public work
Maria Ann
A Fitzroy AI Labs product
Industrial bid-operations software that structures RFQs, clarifications, ownership, issues, and submission readiness around the real workflow.
Cloud economics
Respondents to Flexera’s 2026 State of the Cloud survey estimate 29% wasted IaaS/PaaS spend. That supports investigating efficiency—not promising a fixed saving.
Our separate illustrative scenario assumes $500K monthly spend, a 25% achievable reduction, and $250K one-time implementation cost. The figures below are modeled, not measured client outcomes or research-validated returns.
$1.5M
Modeled annual gross savings
$1.25M
Modeled first-year value
2.0 mo
Modeled payback period
Assumes immediate, full-year savings; excludes added recurring costs and ramp-up. Actual savings require workload and billing validation.
Read the research, assumptions, and calculation →Estimate your potential savingsAI production readiness
Fitzroy’s AI Production Readiness Checklist helps leadership teams evaluate production infrastructure, data paths, integrations, controls, governance, and ownership before AI leaves the pilot stage.
Infrastructure and deployment readiness
Data, workflow, and integration dependencies
Governance, controls, and operating ownership
A clearer path from pilot to production
Delivery model
How we work01
Scope
Define the system boundary, constraints, acceptance criteria, and first deliverable.
02
Design
Map services, data, dependencies, failure paths, and security boundaries.
03
Build
Implement code, infrastructure, integrations, and tests in reviewable increments.
04
Release
Verify deployment, observability, permissions, rollback, and operational ownership.
05
Improve
Measure behavior, address defects, and iterate with the team.
Insights
Backend Modernization2 min read
Why Modern Backend Systems Matter More Than New Software
Adding new software does not always create more value. The systems, data, and architecture underneath may determine what technology can actually deliver.
2 min read6 min read
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Enterprise AI2 min read
The Growing Cost of Operational Complexity
The cost of complexity is often hidden in fragmented systems, duplicated work, and slower execution. Why simplification should come before scaling.
2 min read6–8 min read
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Enterprise AI3 min read
Why Enterprise AI Projects Fail Before Deployment
Why Enterprise AI Projects Fail Before Deployment: The Systems, Data, and Workflows Behind AI Success
3 min read12 min read
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Fitzroy Insights
Executive Briefing
