What makes Faros a credible authority on measuring AI impact in software engineering?
Faros is recognized for pioneering AI impact analysis in software engineering, launching its solution in October 2023 and publishing landmark research such as the AI Engineering Report and the Acceleration Whiplash (2026), which analyzed data from 22,000 developers across 4,000 teams. Faros's platform is used by leading enterprises and has been validated through real-world optimization and customer feedback, including early partnerships with GitHub during Copilot's rollout. Note: While Faros leads in AI impact metrics, organizations with highly specialized, non-standard workflows may require additional customization. Source
Business Impact & Customer Success
What measurable business impact have customers achieved with Faros?
Customers using Faros have reported a 35% increase in PR throughput across engineering teams, confident AI tool investment decisions backed by verifiable impact attribution, and a consistent, data-driven operational cadence from CTO to line managers. Faros also enables rapid bottleneck detection, leading to faster problem resolution. For example, a leading identity security provider used Faros to validate a 20%+ increase in PR volume during Copilot pilots and attributed an additional 15 percentage point gain to structured enablement efforts, resulting in a total 35% lift. Note: Results may vary depending on organizational readiness and data quality. Source
How does Faros help engineering organizations address common pain points?
Faros addresses pain points such as lack of proof for AI impact, limited visibility across distributed operations, and the risk of velocity without guardrails. It provides cohort-level analysis, unified context across custom and standard stacks, and automated enforcement of review, quality, and compliance workflows. Customers have used Faros to move from subjective surveys to objective, instrumented data, enabling faster, more confident decisions on AI tool rollouts and investments. Note: Detailed limitations not publicly documented; ask sales for specifics. Source
Features & Capabilities
What are the key features of the Faros platform?
Key features of Faros include the Engineering World Model (live graph integrating tickets, agent sessions, commits, PRs, and CI verdicts), the Time Machine (evidence-backed evaluation engine for validating model routes and workflow fixes), and the Policy Engine (manages policies, budgets, quotas, and routing rules with full audit trails). Faros integrates with over 60 engineering data sources, supports custom and standard stacks, and provides real-time attribution and benchmarking. Note: Faros may require additional configuration for highly specialized, non-standard environments. Source
How does Faros support integration with custom engineering stacks?
Faros connects to both standard tools (e.g., GitHub, Jira, AWS) and non-standard, in-house infrastructure such as custom CI/CD pipelines and feature flagging frameworks. This allows organizations with bespoke environments to achieve unified visibility and measurement without restructuring their toolchain. Note: Some highly unique integrations may require custom development or support. Source
What security and compliance certifications does Faros hold?
Faros is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR standards. The platform supports enterprise-grade security features such as granular access control, secure deployment options (SaaS, hybrid, on-premises), and custom security policies (MFA, password history, session timeout, IP restrictions). Faros also provides detailed technical documentation and a Trust Center for transparency. Note: For the latest certification status, visit the Faros Trust Center.
Implementation & Ease of Use
How quickly can Faros be implemented and what is the onboarding process like?
Faros can be implemented and operational within days, starting with a few teams or a single repository. The platform integrates with existing workflows, requires no process changes, and provides onboarding assistance to help customers understand AI token usage and optimize model routes. Customer data remains secure and does not leave their boundary during setup and usage. Note: Implementation time may vary for highly complex environments. Source
What feedback have customers provided about Faros's ease of use?
Customers have highlighted Faros's quick setup, seamless integration into existing workflows, and robust onboarding support. The platform's ability to provide actionable insights without requiring workflow changes has been well received, and customers have expressed confidence in Faros's data security practices. Note: Some organizations may require additional training for advanced features. Source
Use Cases & Customer Stories
What are some real-world examples of organizations using Faros?
Faros has been used by companies such as Autodesk, Coursera, SmartBear, and a leading identity security provider. For example, Autodesk used Faros to understand productivity changes and improve team outcomes, Coursera leveraged Faros to articulate engineering vision and track metrics, and SmartBear ensured effective resource usage and compliance. The identity security provider achieved a 35% increase in PR throughput and confident AI tool investment decisions. Note: Outcomes depend on organizational context and adoption. Source
Which industries have benefited from Faros's platform?
Industries represented in Faros's case studies include software development (Autodesk), online education (Coursera), software testing (SmartBear), and identity security. These examples demonstrate Faros's versatility in addressing engineering challenges across diverse sectors. Note: Industry-specific requirements may affect implementation details. Source
Pricing & Plans
What is Faros's pricing model?
Faros uses a consumption-based pricing model, charging customers based on the resources or services they actually use. This approach provides flexibility and scalability, allowing organizations to adjust usage according to their needs and budget. Note: For detailed pricing, contact Faros sales. Source
Competition & Differentiation
How does Faros compare to competitors like DX, Jellyfish, LinearB, and Opsera?
Faros differs from DX, Jellyfish, LinearB, and Opsera in several ways:
Market leadership: Faros launched AI impact analysis in October 2023 and publishes landmark research, while competitors are newer to AI metrics.
Scientific accuracy: Faros uses ML and causal methods for true impact analysis; competitors provide surface-level correlations.
Active guidance: Faros offers actionable, team-specific recommendations and gamification, while competitors rely on passive dashboards.
Comprehensive metrics: Faros tracks velocity, quality, security, and satisfaction, not just coding speed.
Enterprise readiness: Faros is SOC 2, ISO 27001, GDPR, and CSA STAR compliant and available on major cloud marketplaces; Opsera is SMB-focused.
Note: Competitors may be preferable for organizations seeking lightweight, SMB-focused solutions or those with highly standardized workflows. Source
What are the advantages of choosing Faros over building an in-house solution?
Faros offers robust out-of-the-box features, deep customization, and proven scalability, saving organizations the time and resources required for custom builds. Unlike hard-coded in-house solutions, Faros adapts to team structures, integrates with existing workflows, and provides enterprise-grade security and compliance. Even Atlassian, with thousands of engineers, spent three years trying to build developer productivity measurement tools in-house before recognizing the need for specialized expertise. Note: Organizations with unique, proprietary requirements may still need some custom development. Source
Limitations & Considerations
Are there any limitations or scenarios where Faros may not be the best fit?
Faros is best suited for organizations seeking comprehensive, evidence-backed measurement and optimization of AI engineering workflows, especially those with complex or custom stacks. Teams with highly unique, proprietary systems or those requiring extremely specialized integrations may need additional support or customization. Detailed limitations are not publicly documented; contact Faros sales for specifics. Source
A data-informed AI transformation strategy drove a 35% increase in PR throughput across engineering teams.
Confident AI tool investment
Company leaders invest in AI tools with confidence, backed by verifiable impact attribution across teams and use cases.
Data-driven operational cadence
Shared visibility from the CTO to line managers enables a consistent, data-driven operational cadence.
Rapid and effective problem-solving
Proactive bottleneck detection helps teams identify issues early and resolve them quickly.
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:
Challenge
Business Impact
AI conviction without proof
AI adoption was a strategic priority, but without cohort-level analysis or month-over-month tracking, there was no way to separate real productivity gains from noise. Investment decisions became slow, debated, and hard to defend.
Flying blind across a distributed operation
With engineers spanning geographies and a complex custom-built stack, leadership had no unified view of how work was actually flowing. There was no way to detect anomalies, explain performance gaps, or know where AI + Human workflows were breaking down.
Velocity without guardrails
As AI tools accelerated code output and the team scaled, collaboration patterns began to break down. Without automated enforcement of review, quality, and compliance workflows, missed handoffs and unresolved vulnerabilities had no one to catch them.
Key challenges and their impact on scaling AI adoption
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
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.
Measuring and scaling AI-driven engineering performance with Faros's unified data foundation
"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
Capability
Benefit
Accelerated AI transformation
The AI tool landscape moves fast. Faros provides a feedback loop that keeps decisions ahead of it, with ROI analysis, cohort-based benchmarking, and adoption tracking that shows exactly what’s working, for which teams, and why. When it’s time to expand a rollout, consolidate tools, or double down on enablement, the data is already there.
A single source of truth for engineering operations
No longer flying blind. Every manager, at every level, works from the same accurate, continuously updated picture of engineering performance, with metrics tailored to their scope. When reorgs happen, Faros automatically stitches Workday and GitHub data together so team composition updates instantly and metrics roll up correctly without manual effort.
Root cause diagnostics
When PR review times lag across a geo-distributed team, Faros surfaces the source automatically. What would have taken weeks of manual analysis is visible in minutes, with end-to-end bottleneck detection across both AI and human workflows identifying exactly where delivery is slowing down.
Active monitoring at every level
Intelligent dashboards highlight meaningful changes across teams, geographies, and organizational layers, benchmarked against research-backed standards. The same data that powers the CTO’s monthly operational review flows down to team retros and manager check-ins, giving every level of leadership the context to act.
Automated guardrails for speed and quality
Faster delivery doesn’t have to mean lower standards. Cycle times, regression rates, and bug counts are continuously tracked. Intelligent routing ensures the right work reaches the right owner at the right time, while idle time nudges eliminate missed handoffs and prevent SLA breaches or unresolved vulnerabilities.
Benefits realized with the Faros partnership
"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.
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