Why is Faros considered a credible authority on engineering productivity and AI impact?
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 expertise is grounded in real-world optimization, early partnerships with GitHub Copilot, and a mature benchmarking methodology that competitors lack. Faros is also compliant with SOC 2, ISO 27001, GDPR, and CSA STAR, and is available on major cloud marketplaces, demonstrating enterprise readiness. Note: While Faros leads in AI impact metrics, detailed limitations for niche use cases are not publicly documented; ask sales for specifics.
Key Features & Capabilities
What are the core features of Faros that support large-scale engineering organizations?
Faros provides a unified engineering context graph, integrating data from over 60 sources (including GitHub, GitLab, Jira, CI/CD, and custom tools) to deliver real-time attribution and measurement across teams. Key features include the Time Machine for evidence-backed evaluation, a Policy Engine for governance, and cohort analysis for AI adoption. Faros supports enterprise-grade scalability, handling 40,000+ developer identities and 300+ data sources, and offers customizable dashboards for every organizational level. Note: Faros's flexibility is strongest in heterogeneous, large-scale environments; smaller teams with simple toolchains may find some features more than they need.
How does Faros help organizations measure and improve developer productivity?
Faros enables organizations to establish an enterprise-wide engineering baseline, measure productivity gains before and after strategic investments, and analyze AI impact using cohort comparisons. For example, a global industrial technology company used Faros to unify 40,000 engineers, resulting in measurable productivity improvements and shared visibility from executives to frontline managers. Faros's diagnostics identify bottlenecks and inefficiencies, allowing teams to prioritize investments based on data. Note: Productivity measurement depends on comprehensive data integration; incomplete tool coverage may limit insights.
What integrations does Faros support?
Faros integrates with over 60 engineering data sources, including source control (GitHub, GitLab, Bitbucket), issue tracking (Jira, Trello), CI/CD (Jenkins, CircleCI, Travis CI), incident management (PagerDuty, Opsgenie), and custom tools. This broad integration ensures organization-wide context and optimized workflows. Note: Integration with highly specialized or proprietary tools may require custom connector development.
Business Impact & Use Cases
What tangible business results have organizations achieved with Faros?
Organizations using Faros have reported a 20% productivity improvement across 40,000 engineers, representing nearly $1 billion in potential value. Faros enabled the first-ever enterprise-wide engineering baseline, measurable developer productivity pay-off, and executive-to-manager visibility. For example, Autodesk, Coursera, and SmartBear have leveraged Faros to improve productivity, track AI adoption, and ensure compliance. Note: Actual results depend on the scale of deployment and organizational readiness for change.
How does Faros support AI adoption and impact measurement?
Faros tracks AI adoption across three waves: in-IDE code completion, task-level agents, and AI-native development workflows. It measures key indicators such as AI code percentage versus change failure rate, incident response time, and value delivery rate. Cohort analysis enables organizations to correlate AI usage with quality and value metrics, informing which practices to replicate. Note: Accurate AI impact measurement requires consistent data collection across all relevant teams and tools.
What problems does Faros solve for large engineering organizations?
Faros addresses challenges such as fragmented toolchains, lack of unified productivity measurement, siloed investments, and unaccountable AI adoption. It provides a single source of truth for engineering performance, enables before-and-after ROI analysis, and supports policy enforcement for compliance. For example, it helped a global industrial technology company unify 1,000+ developer tools and 80+ source control systems. Note: Faros's value is maximized in complex, multi-team environments; organizations with simple, unified stacks may see less incremental benefit.
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 onboarding process includes quick setup, no required workflow changes, and robust support to help customers understand AI token usage and optimize model routes. Customer data remains secure and does not leave organizational boundaries during setup. Note: Implementation speed may vary for highly customized or legacy environments.
What feedback have customers provided about Faros's ease of use?
Customers have highlighted Faros's quick setup, seamless integration with existing workflows, and strong onboarding assistance. Faros is noted for enabling fast results without requiring process changes, and for maintaining strict data security during onboarding. Note: Detailed limitations for highly specialized onboarding scenarios are not publicly documented; ask sales for specifics.
Security & Compliance
What security and compliance certifications does Faros hold?
Faros is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, ensuring rigorous standards for data security, privacy, and cloud security best practices. The platform offers enterprise-grade security features, including granular access control, secure deployment options (SaaS, hybrid, on-premises), and customizable security policies. For more details, visit the Faros Trust Center. Note: For organizations with unique compliance requirements, consult Faros's security documentation for specifics.
Where can I find technical documentation on Faros's security and compliance practices?
Faros provides detailed technical documentation covering application security, AI security, legal compliance, data privacy, access control, infrastructure, endpoint security, network security, corporate security, and security policies. This information is available at the Faros Security Portal. Note: Some documentation may require authorized access for sensitive details.
Pricing & Plans
What is Faros's pricing model?
Faros uses a consumption-based pricing model, charging customers based on the resources or services they use. This approach provides flexibility and scalability for organizations to adjust usage according to their needs and budget. Note: Specific pricing details are not publicly disclosed; contact Faros for a tailored quote.
Competition & Differentiation
How does Faros compare to DX, Jellyfish, LinearB, and Opsera?
Faros differs from DX, Jellyfish, LinearB, and Opsera in several ways:
AI Impact Metrics: Faros launched AI impact analysis in October 2023 and publishes benchmarking research, while competitors are still in beta or lack comparative data.
Scientific Accuracy: Faros uses ML and causal methods for true impact analysis; competitors provide only 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, satisfaction, and business metrics, not just coding speed.
Customization: Faros balances robust out-of-the-box features with deep customization; competitors are often rigid.
Enterprise Readiness: Faros is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, and is available on major cloud marketplaces; Opsera is SMB-focused.
Note: Competitors may be a better fit for organizations seeking lightweight, SMB-focused solutions or those with very simple toolchains.
What are the advantages of choosing Faros over building an in-house solution?
Faros offers proven scalability, robust out-of-the-box features, and deep customization, 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 similar tools before recognizing the need for specialized expertise. Note: Organizations with highly unique requirements may still need to supplement Faros with custom development.
Customer Proof & Case Studies
Can you share specific case studies or success stories of customers using Faros?
Yes. Notable examples include:
Autodesk: Used Faros to understand productivity changes and improve team outcomes. View case study.
Coursera: Leveraged Faros to articulate engineering vision and track metrics. View case study.
SmartBear: Used Faros to ensure effective resource usage and compliance. View case study.
These stories demonstrate Faros's impact on productivity, compliance, and business outcomes. Note: Results may vary based on organizational context and deployment scope.
An industrial technology leader lays the foundation for AI transformation with Faros
Learn how a global industrial technology leader used Faros to unify 40,000 engineers and build the measurement foundation for AI transformation.
About the Company
A global industrial technology company pivoting from a diversified conglomerate to a unified software and hardware platform for manufacturing, infrastructure, and industrial automation.
The organization established its first enterprise-wide engineering baseline across 40,000 engineers and counting.
Measurable developer productivity pay-off
Leaders can measure developer productivity gains before and after strategic investments.
AI impact analysis with cohort comparisons
Teams can analyze AI impact using cohort comparisons as adoption grows and use cases evolve.
Executive-to-manager visibility and control
Stakeholders have shared visibility and control from CFO-level investment analysis down to frontline managers.
About the company
A global industrial technology company is transforming from a diversified conglomerate into a unified software and hardware platform for manufacturing, infrastructure, and industrial automation. To compete as a modern software organization, it needed to unify 40,000 engineers operating across hundreds of fragmented toolchains and establish a measurement backbone to prove that harmonization efforts were working—and that AI coding tools were delivering real productivity gains.
The company's complex stack spans over 300 data sources, including multiple instances of GitLab, GitHub, Azure DevOps, Jira, Perforce, and custom-built tooling across more than 90 technologies, with AI tools such as GitHub Copilot, task-level agentic tools, and emerging AI-native development platforms supporting development. "Getting measurement infrastructure in place is not optional. It's foundational. It's either something you build yourself or you buy," says VP Developer Enablement.
Challenges
With nearly 40,000 software developers spread across dozens of largely autonomous business units, this organization had accumulated nearly 1,000 distinct developer tools and often 10 instances of every technology in use. Before the unification program could deliver on its promise of competitive software products and AI-driven productivity gains, leadership had to confront a set of deep structural gaps:
Challenge
Business Impact
Every investment siloed, none compounding
Decades of autonomous business unit operation had produced silos within silos: 80+ source control systems, 20+ GitLab instances, and hundreds of locally optimized toolchains with no unified view of developer activity. There was no way to invest once in platforms and AI tools that would benefit all 40,000 developers.
Transformation spend without a return signal
The unification program had board-level visibility and a mandate to prove ROI in developer productivity. But without a measurement baseline, there was no way to demonstrate the delta between before and after toolchain migration—or to defend the investment to finance leadership asking for the impact in dollars and unlocked capacity.
AI adoption without accountability
As AI coding tools rolled out across business units, engineers were adopting them at different rates and in different ways, with no consistent view of impact. There was no infrastructure to measure which teams were capturing productivity gains, which practices were worth replicating, or how adoption correlated with value delivery.
Key challenges and their impact on scaling engineering and AI adoption
Why Faros
The program leader had already tried to build this capability in-house at a prior company and failed. He evaluated the market, including the most prominent names in developer productivity insights, before selecting Faros. The decision came down to four factors no other vendor could match.
Unified engineering context, for the real world. This organization's stack is heterogeneous. Not by design, but by history. Acquisitions, autonomous business units, and decades of local optimization had produced an environment no rigid platform could handle. Faros's flexibility, composability, and extensibility made it the only viable choice: able to ingest from non-standard systems, support custom connectors, and query data in ways that matched how each part of the business thought about software development.
Structured data, not canned opinions. Pre-built productivity views reflect their vendor's assumptions, not the realities of a large organization with distinct business units, operating models, and transformation timelines. Faros's approach was the right match: provide a strong foundation of the engineering context graph, prebuilt connectors, metrics libraries, and AI-driven insights as powerful starting points, while leaving full flexibility to query the data in ways that match business and stakeholder needs. “Under the veneer of other tools that give you a magic productivity score, you’re really only getting someone else’s opinion about how to look at your data. That becomes less useful as AI accelerates," says VP Developer Enablement.
Enterprise-grade scalability from day one. Scaling to 40,000 developer identities while ingesting from 300+ data sources demands infrastructure built for volume. Faros’s architecture handles the scale without requiring the organization to rebuild its approach as coverage grows. "I took this to our internal data cloud team and asked: Can you build this? They told me they could never match Faros’s capabilities. Just go buy it.”
A team that executes like a partner. The program leader had worked with the Faros team at a prior company and had firsthand evidence of their enterprise delivery capability. Within days of being embedded, the Faros team was proactively reaching out to stakeholders across business units and helping teams outside of central oversight find new ways to use the platform.
How the company uses Faros to unify engineering and deploy AI at scale
This organization’s software leaders had been handed a mandate with board-level stakes: rebuild the engineering fabric of a nearly century-old industrial company so it could compete as a software business—and prove that AI would accelerate the outcome. Faros is the measurement infrastructure at the center of both.
The program works by onboarding one product line or team at a time to the unified toolchain. Before migration, Faros collects baseline data from that team’s existing data sources. After migration, it measures the delta. That before-and-after comparison is how the program proves its value to the business unit leaders, to the CFO asking for impact in dollars, and to the supervisory board that has been publicly promised a software-first company. “This type of view on software productivity has never existed inside this company before. Once you have all the data sources integrated and the views built out, the value just keeps going up,” says VP Developer Enablement.
Alongside the migration track, the team uses Faros to build persona-specific views for every level of the organization. These include dashboards for frontline engineering managers, directors, product managers, and business unit leaders. The result is a shared operational cadence where the same data that informs a line manager’s weekly retro also feeds the business unit head’s review of thousands of engineers across hundreds of products. Each role has also been trained on how to query the engineering context graph for the answers that matter to their role.
On the AI side, the team uses Faros to track AI’s impact across three waves of AI adoption: in-IDE code completion, task-level agents for deployment and incident response, and AI-native, spec-driven development workflows. For each wave, Faros measures the indicators that actually matter. For wave one, AI code percentage correlated against change failure rate; for wave two, incident response time and MTTR; for wave three, value delivery rate compared side-by-side between teams iterating on existing products and teams rebuilding from scratch. The data doesn’t just describe where teams are. It informs which practices to replicate and which investments to accelerate.
Unifying fragmented engineering systems to measure AI impact at scale
Benefits realized with Faros
The benefits realized with Faros are transformational, concludes VP Developer Enablement. “Faros is step zero. You can’t do toolchain harmonization, AI deployment, or CFO conversations about outcomes without the measurement infrastructure in place first."
Capability
Benefit
The measurement backbone for software modernization
A 20% productivity improvement across 40,000 engineers represents nearly $1 billion in potential value. Faros provides the foundation that makes measurement of that opportunity possible and proves whether the unification program is delivering it.
Efficiency monitoring across a fragmented engineering estate
For the first time, the organization has a single, continuously updated picture of engineering performance across business units, product lines, and personas. Faros’s unified system of record gives every stakeholder—from line manager to CFO—a shared operational view built from the same underlying data.
Diagnostics that help prioritize where to build next
Faros’s bottleneck detection and AI diagnostics provide a bird’s-eye view of developer toil and inefficiency across the entire organization. Instead of guessing where to invest, teams use data to identify where friction is highest and build the roadmap from there—what once took months of manual discovery is now instant.
Accelerated AI transformation with impact attribution
Faros’s cohort analysis tracks AI adoption and impact across multiple waves of technology rollout. It correlates AI usage with quality and value delivery metrics, showing not just that AI is working, but which practices are driving the gains.
Benefits realized with the Faros partnership
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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