Firewires OneIoT Case Study

Firewires OneIoT's engineering organization was growing, and its development activity was spread across workflows that had never been centralized. There was no single enterprise-managed platform, no consistent repository, branch and pull-request practice, and no clear governance or access management layer underneath it. Scaling the team without that foundation meant compounding the problem. At the same time, the organization wanted its developers to benefit from AI assistance across routine coding, code comprehension, debugging, testing and documentation, and wanted more than one model available so developers could match model to task. Any AI capability had to arrive inside a governed environment rather than alongside it, and the organization needed real visibility into whether it was being used.

Saints & Masters implemented GitHub Enterprise as the development and governance foundation, with enterprise organization setup, repository and workflow enablement, and access management configured before AI was introduced. Development collaboration was standardized onto repositories, branches and pull requests, giving Firewires OneIoT one governed platform in place of scattered practice. GitHub Copilot was then enabled on top of that foundation, so AI assistance appeared inside the workflow developers already used rather than requiring a separate environment. Anthropic Claude models are used across code generation and completion, code explanation, refactoring, debugging, unit test authoring, technical documentation and implementation research, with five Claude variants in active use and other model families available alongside them. Model level AI credit reporting gives the organization visibility into user activity and consumption by model.

Tech Stack & Integrations

• •GitHub Enterprise — enterprise development platform and governance foundation
• •Access & Governance Configuration — organization setup, permissions, access management
• •Repository Workflow Standardization — repositories, branches, pull-request collaboration
• •GitHub Copilot — AI assistance inside the existing developer workflow
• •Anthropic Claude (Sonnet 4.6, Sonnet 5, Opus 4.6, Opus 4.5, Haiku 4.5) — code generation, comprehension, refactoring, debugging, testing, documentation
• •Multi-model access — additional model families available alongside Claude
• •Consumption Analytics — model-level AI credit reporting and adoption visibility

• •Centralized enterprise GitHub platform with governance and access management
• •86% of reported AI consumption on Anthropic Claude models
• •5 Claude model variants in active use across development tasks
• •AI assistance embedded in the existing developer workflow, no separate tooling
• •Model-level visibility into AI adoption and consumption

Average development time per coding task reduced from 2.5 days before implementation to 1.5 days after implementation (40% reduction).