Faros for Microsoft and GitHub
Maximizing outcomes from AI engineering
Faros is the system for running engineering with AI. Come see how enterprise leaders are using Faros on Azure to measure GitHub Copilot impact, redesign Human + AI workflows, and turn token spend into faster, more efficient delivery.
The challenges every engineering leader is facing
Your CFO wants the ROI on AI coding tools. Your board wants to know if AI is actually changing how fast you ship. Your developers want to know if the new workflows are working. And you have one quarter to answer all three.
Faros gives engineering leaders a live picture of how work flows across people, code, AI agents, and tools, so the answer to "is this working?" stops being a story and starts being data.
Whatever stage of the AI transformation you're in, three questions are top of mind. Faros is built to answer them.
Where do we stand?
"How is our engineering performing today?"
A live, unified view of throughput, quality, and AI adoption across every team and repo
How do we improve?
"How can we drive 20% more productivity?"
Diagnostics that pinpoint the bottlenecks and the AI investments that will move them
How do we transform?
"How do we 10x engineering with AI tools?"
The measurement and orchestration layer for redesigning how software gets built
How Faros measures and improves GitHub Copilot impact
The Faros approach to AI in software engineering is a closed loop: evaluate before you commit, optimize what you deploy, monitor what's actually moving.
Find the right agent and model for your repos, before you commit budget
Faros replays historical pull requests through any AI coding agent using only what a human engineer would receive: the ticket and the codebase.
Output gets scored against the actual human-authored solution. You walk out with a ranked comparison across agents and models by outcome rate, cost per successful task, and engineering time recovered, per repo. In a Fortune 100 diagnostic, a mid-tier general model outperformed a purpose-built code specialist by 3x at comparable cost.
Make the agent perform like it knows your codebase
Once you've picked the right agent, Faros builds the harness around it: repo-specific rulebooks generated from failure cases, MCP server configuration, build and test processes, and ticket enrichment with the architectural context a tenured engineer would carry.
Each cycle compounds. The agent stops treating your repos as a cold start.
Measure what's actually moving, attribute it causally, alert before it breaks
Faros connects AI usage to engineering outcomes across throughput, quality, stability, and bottlenecks, then attributes impact causally, isolating AI's real lift from confounds like seniority and repo complexity.
Alerts fire on token waste, throughput regression, quality degradation, or adoption drop-off before they become a board conversation.
The data behind the conversation
The economic case for AI in engineering can't be made in the abstract. Our 2026 AI Engineering Report, The Acceleration Whiplash, analyzed two years of telemetry from 22,000 developers across 4,000 teams.
Epic throughput is up 66% under high AI adoption, but incidents-per-PR tripled over the same period.
That gap between output and quality is where every AI investment conversation now lives.
Why engineering leaders pick Faros to measure AI impact
Plenty of platforms claim to measure developer productivity in the AI era. Three things make Faros the one engineering leaders take to their CFO.
Team-level attribution, not org averages
Most tools report AI metrics at the org level, averaging across teams whose economics move in opposite directions.
Faros attributes impact at the team level by default, where AI programs actually succeed or fail and where decisions get made.
Causal, not correlational
Acceptance rate and PR lift are correlations. They tell you teams using AI ship more code. They don't tell you AI caused it.
Faros applies causal modeling per team to isolate AI's real lift from confounds like seniority, repo complexity, and team composition. That's the number you can take to a CFO.
Forward-cost modeling, built in
The industry is moving to consumption pricing. Vendors know usage is their friend. Data is yours.
Faros models ROI at forward unit prices so you can see which teams stay above water at 3x, 5x, and 8x. No other engineering platform does this.
How an industrial leader unified 40,000 engineers to drive AI transformation
"Faros is step zero. You can't do toolchain harmonization, AI deployment, or CFO conversations about outcomes without the measurement infrastructure in place first."
A global industrial technology company was pivoting from a diversified conglomerate into a unified software platform. 40,000 engineers. 300+ data sources. 80 source control systems. AI tools rolling out everywhere with no consistent way to measure developer productivity or AI impact.
They picked Faros to build the measurement backbone. Today, they have a single, continuously updated picture of engineering performance across business units, product lines, and personas, plus cohort-level analysis of how AI is affecting throughput, quality, and value delivery.
A 20% productivity improvement across 40,000 engineers represents nearly $1 billion in potential value. Faros is what makes that opportunity measurable.
A Microsoft partner,
not just a Microsoft integration
Faros is the 2025 Microsoft for Startups Partner of the Year. We're available on Azure Marketplace with MACC eligibility, deployed natively on Azure, and integrated with GitHub, GitHub Copilot, and Azure DevOps out of the box. For enterprises already standardized on Microsoft, Faros fits into your software development stack without friction.
Connect with a Faros Expert
See the platform in action.
