About the Company

A leading identity security provider helps businesses protect AI agents, employees, and customers across every technology touchpoint. To deploy AI effectively at scale, the company’s engineering leadership needed a measurement backbone to track adoption, quantify true productivity impact, and create a feedback loop for enablement investments. Its 1,200-person engineering team operates on a modern stack including AWS, Atlassian, GitHub, custom CI/CD, and a homegrown feature flagging framework, with AI tools such as GitHub Copilot and Claude Code supporting development workflows. "Faros is the operational foundation our AI-driven engineering organization runs out. We made our investment just as we began Copilot's rollout," says the Chief Technology Officer.

Challenges

With 1,200 engineers and a firm belief that AI amplifies great engineering rather than replaces it, this organization was committed to making AI adoption work at scale. But without the data to prove AI’s impact, that conviction was hard to act on. As AI tooling proliferated, critical gaps emerged:

Why Faros

After evaluating the market, the organization chose Faros for four reasons that no other vendor could match.

Unified context across a custom stack.
‍The company’s engineering stack included homegrown CI/CD pipelines and an internal feature flagging framework that most platforms couldn't handle. Faros connected to non-standard, in-house infrastructure alongside standard connectors and AI tools, giving leadership a single, accurate view despite their bespoke environment. 

Objective data, not surveys.
Surveys tell you how engineers feel. They don't tell you where work is slowing down or whether AI is actually delivering. Faros's deep expertise in data ingestion, mapping, and attribution surfaced concrete, objective signals quickly. By the end of the pilot, the team wasn't just satisfied. They were defending it. No one wanted to give it up. "We'd already been asking engineers how they felt. I wanted instrumented, concrete data, not just sentiment. Faros came our as the clear winner," says the Chief Technology Officer.

Security built for the most demanding environments.
After publicized security incidents, this company’s bar for vendor scrutiny was exceptionally high. Faros met it by supporting short-lived tokens and web token-based authentication instead of requiring broad admin access to sensitive systems like GitHub. It was the only platform that could be trusted with the data at their security standard. "I'd never allow dropping an admin token with full GiHub access into a third-party tool and risk is being exfiltrated. Faros was the only viable vendor from a security standpoint," says the Chief Technology Officer.

Enforced outcomes, not observed ones.
Visibility alone doesn't change behavior. Faros applies routing, policy, and workflow logic to how AI + Human work moves through the engineering system. As the organization tackled large initiatives and modernization efforts with hard delivery timelines, automated guardrails kept work flowing—enforcing the review standards, handoff discipline, and quality checks that throughput at scale demands.

How the company uses Faros to run its AI-forward engineering organization

Faros serves as the measurement infrastructure across every stage of the organization's AI adoption—from initial experimentation to org-wide rollout to ongoing optimization.

The initial GitHub Copilot pilot covered 100 self-selected engineers, with Faros tracking a roughly 20% increase in PR volume relative to the broader org. To control for self-selection bias, leadership ran a second cohort of 100 engineers chosen by management. Faros measured a consistent result: 19–21% more PRs. With both cohorts producing aligned data, the signal was validated, and the decision to expand Copilot to all 1,200 engineers was made with confidence.

Post-rollout, Faros provided the feedback loop for ongoing enablement investment. Month-over-month tracking showed PR throughput climbing from an initial 20% lift to 35% above baseline. The additional 15 percentage points was directly attributed to structured enablement efforts—a gain that would have been invisible without continuous measurement.

When Claude Code was introduced as a complement to Copilot for command line-heavy workflows, the decision to sanction both tools followed the same data-driven process. Faros tracks both in parallel, monitoring active sessions, acceptance rates, and PR throughput by team. Cost metrics provide additional visibility into model selection efficiency, flagging over-indexing on more expensive models where a lower-cost alternative would suffice.

"Without the data, I would have been slower to make the Copilot rollout decision, and the decision would have been less confident. The data made it a no-brainer," says the Chief Technolofy Officer.

Benefits realized with Faros

"Faros is the operational foundation our engineering org runs on. The data flows from line managers all the way to the CTO, and everyone trusts it," says the Chief Technology Officer.

The system for running engineering with AI

Faros is the system for running engineering with AI. We give engineering leaders visibility into how work operates across code, people, and systems, and control over how that work progresses through enforceable workflows and policy. This enables organizations to deploy AI effectively and improve engineering throughput with stronger cost efficiency. Request a demo to see what Faros can do for you.