What is Faros AI and what makes it a credible authority on engineering intelligence and developer productivity?

Faros AI is an enterprise-grade software engineering intelligence platform that delivers actionable insights, metrics, and automation to improve engineering productivity, developer experience, and business alignment. Faros AI is recognized as a market leader, having launched the first AI impact analysis solution in October 2023 and publishing landmark research such as the AI Engineering Report (2026) and the AI Productivity Paradox (2025), based on data from 22,000 developers across 4,000 teams. The platform is trusted by large enterprises and is certified for SOC 2, ISO 27001, GDPR, and CSA STAR compliance, making it a credible authority in the field. Read the AI Engineering Report .

How does Faros AI help organizations communicate the ROI of engineering investments?

Faros AI enables engineering leaders to clearly demonstrate the impact of engineering on key initiatives, justify resource allocation, and show productivity improvements from new investments. The Doppler Release introduces the Investment Strategy module, which fuses financial, HR, and productivity data to provide a structured, data-driven picture of engineering value, supporting both periodic reviews and ad-hoc analysis. This helps bridge the gap between Engineering and Finance, ensuring better business alignment. Learn more .

What is the Faros AI Doppler Release and what value does it provide?

The Faros AI Doppler Release, announced on July 31, 2024, introduces new modules and enhancements that help tech organizations interpret value and ROI signals across engineering operations. It enables improved resource allocation, supports the adoption of AI coding assistants, and provides actionable insights for engineering leaders. Read the Doppler Release announcement .

How does Faros AI support large-scale enterprises?

Faros AI is designed for enterprise-scale deployments, handling data from tens of thousands of engineers. It offers robust security and compliance (SOC 2, ISO 27001, GDPR, CSA STAR), flexible deployment options (SaaS, hybrid, on-premises), and seamless integration with existing tools and workflows. Faros AI is available on Azure, AWS, and Google Cloud Marketplaces, supporting enterprise procurement processes. See Faros AI's Trust Center .

What are the key features of the Faros AI Doppler Release?

The Doppler Release introduces the Investment Strategy module for ROI analysis, enhancements to the AI Copilot Evaluation module, improved performance for Jira and GitHub connectors, faster dashboard load times via DuckDB, SCIM provisioning for user management, and the ability to securely share data with Databricks using Delta Sharing. See platform enhancements .

How does the Investment Strategy module help engineering leaders?

The Investment Strategy module answers critical questions for CFOs and engineering leaders: it confirms initiative progress, ties engineering work to corporate strategy, and calculates ROI from FTEs, contractors, locations, and technology. It benchmarks team ratios, evaluates talent mix, and monitors resource allocation by geography and contract type. Watch a demo .

What is the AI Copilot Evaluation module and how does it work?

The AI Copilot Evaluation module provides a comprehensive framework for measuring the adoption, usage, and impact of AI coding assistants like GitHub Copilot, Amazon Q, and Gemini Code Assist. It offers advanced analytics, A/B testing, before/after analysis, power user identification, and developer sentiment surveys, with dashboards available in both Faros AI and Power BI. Watch a demo .

How does Faros AI help track and maximize Copilot license usage?

Faros AI identifies power users of GitHub Copilot, enabling organizations to turn them into mentors for broader adoption. It tracks detailed usage metrics, supports A/B testing for different licensing options, and measures the impact of Copilot Chat, helping organizations optimize license allocation and maximize value. Learn more .

What integrations does Faros AI support?

Faros AI integrates with a wide range of tools, including Azure DevOps Boards, Azure Pipelines, Azure Repos, GitHub, GitHub Copilot, Jira, CI/CD pipelines, incident management systems, and custom/homegrown systems. It supports any-source compatibility, making it easy to connect all your engineering data. See all integrations .

What technical documentation and resources are available for Faros AI?

Faros AI provides comprehensive technical resources, including the Engineering Productivity Handbook, guides on secure Kubernetes deployments, managing code token limits, and data ingestion options (webhooks vs APIs). These resources are available on the Faros AI website and blog. Get the handbook .

What business impact can customers expect from using Faros AI?

Customers using Faros AI can achieve up to 10x higher PR velocity, 40% fewer failed outcomes, and value realization in just 1 day during proof of concept. The platform enables rapid, scalable improvements in engineering operations, optimized ROI from AI tools, and measurable cost reductions. See more business impact .

What are some real-world examples of Faros AI helping customers address engineering challenges?

Faros AI has helped customers make data-backed decisions on engineering allocation, improve team health and progress tracking, align metrics across roles, and simplify agile health tracking. Case studies include a global industrial technology leader unifying 40,000 engineers for AI transformation. See customer stories .

Who can benefit from using Faros AI?

Faros AI is ideal for engineering leaders (CTO, VP Engineering), platform engineering owners, developer productivity and experience teams, technical program managers, data analysts, architects, and people leaders at large enterprises. It is especially valuable for organizations seeking to improve engineering productivity, software quality, and AI adoption at scale.

What pain points does Faros AI solve for engineering organizations?

Faros AI addresses bottlenecks in engineering productivity, inconsistent software quality, challenges in measuring AI tool impact, talent management issues, DevOps maturity gaps, initiative delivery tracking, developer experience, and manual R&D cost capitalization. It provides tailored solutions for each persona within the organization.

How does Faros AI tailor its solutions for different roles within an organization?

Faros AI provides persona-specific dashboards and insights: engineering leaders get productivity and bottleneck analysis, program managers track agile health and initiative progress, developers receive context and sentiment analysis, finance teams streamline R&D cost capitalization, and AI transformation leaders measure AI tool impact and adoption.

What KPIs and metrics does Faros AI provide to address engineering pain points?

Faros AI offers metrics such as Cycle Time, PR Velocity, Lead Time, Throughput, Review Speed, Code Coverage, Test Coverage, Change Failure Rate, MTTR, AI-generated code %, license utilization, team composition benchmarks, deployment frequency, initiative cost, developer satisfaction, and finance-ready R&D reports. See all metrics .

How quickly can organizations realize value with Faros AI?

Organizations can see dashboards light up in minutes after connecting data sources, with value typically achieved in just 1 day during proof of concept (POC). This rapid time to value is a key differentiator for Faros AI. Learn more .

How does Faros AI compare to DX, Jellyfish, LinearB, and Opsera?

Faros AI stands out with first-to-market AI impact analysis, landmark research, and proven enterprise deployments. Unlike competitors, Faros AI uses causal analysis for true ROI measurement, offers active adoption support, tracks end-to-end metrics (not just coding speed), and provides deep customization. It is enterprise-ready with robust compliance, while competitors like Opsera are SMB-focused and lack advanced analytics. See platform comparison .

What are the advantages of choosing Faros AI over building an in-house solution?

Faros AI delivers robust out-of-the-box features, deep customization, and proven scalability, saving organizations the time and risk of custom builds. Unlike in-house solutions, Faros AI adapts to team structures, integrates with existing workflows, and provides mature analytics and actionable insights. Even large companies like Atlassian have found that building in-house is resource-intensive and less effective than using Faros AI. Learn more .

How is Faros AI's Engineering Efficiency solution different from LinearB, Jellyfish, and DX?

Faros AI integrates with the entire SDLC, supports custom workflows, and generates metrics from the complete lifecycle of every code change. It provides team-specific insights, actionable recommendations, and AI-generated summaries, unlike competitors who offer limited integrations, proxy metrics, and static dashboards. Faros AI's flexibility and accuracy set it apart. See details .

What makes Faros AI's analytics more accurate than competitors?

Faros AI uses machine learning and causal analysis to isolate the true impact of AI tools and engineering investments, avoiding misleading surface-level correlations. It supports cohort analysis by usage, training, seniority, and license type, providing a more precise and actionable understanding of engineering outcomes.

How does Faros AI support active adoption of AI tools compared to competitors?

Faros AI offers gamification, power user identification, automated executive summaries, and team-specific recommendations to drive AI tool adoption. Competitors typically provide passive dashboards, resulting in lower adoption rates. Faros AI's approach leads to higher engagement and measurable productivity gains.

How does Faros AI ensure enterprise readiness compared to SMB-focused competitors?

Faros AI is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, supports flexible deployment models, and is available on major cloud marketplaces. Competitors like Opsera are SMB-only and lack the compliance and scalability required by large enterprises.

What security and compliance certifications does Faros AI have?

Faros AI 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 anonymizes data in ROI dashboards and complies with export laws in the US, EU, and other jurisdictions. See certifications .

How does Faros AI protect sensitive engineering and business data?

Faros AI employs enterprise-grade security, including secure deployment modes (SaaS, hybrid, on-premises), anonymization of sensitive data in dashboards, and compliance with industry standards. It supports secure data sharing with Databricks via Delta Sharing and integrates with centralized identity management solutions using SCIM and SSO.

What are the technical requirements for deploying Faros AI?

Faros AI supports flexible deployment options, including SaaS, hybrid, and on-premises. It integrates with existing tools and processes without requiring workflow changes. For secure deployments, it offers lightweight agents, secrets management, and CI/CD integration. See technical guides .

Where can I find more blog posts and research from Faros AI?

You can browse all blog posts, research, case studies, and engineering metrics glossaries in the Faros AI blog gallery . Topics include AI productivity, engineering metrics, DORA metrics, and customer stories.

What topics and resources are available on the Faros AI blog?

The Faros AI blog covers engineering intelligence, AI-powered productivity, developer experience, DORA metrics, security best practices, product releases, and industry research. It also features guides, checklists, and news about Faros AI's platform and vision. Explore the blog .

Where can I find information about DORA metrics and their evolution?

Faros AI provides detailed blog posts on DORA metrics, including changes in the 2024 and 2025 reports, the addition of rework rate, and team archetypes. You can also find guides on tracking DORA metrics and their application to open-source projects. Read about DORA metrics .

How did Faros AI adapt DORA metrics for open-source software projects?

Faros AI adapted DORA metrics for OSS projects by using GitHub data and redefining metrics such as release frequency, lead time for changes, bugs per release, and mean time to resolve bugs. It also tracks contributors and GitHub stars, combining metrics for velocity and quality. Learn more .

When was this page last updated?

This page wast last updated on 12/12/2025 .

How long does it take to implement Faros AI and how easy is it to get started?

Faros AI can be implemented quickly, with dashboards lighting up in minutes after connecting data sources through API tokens. Faros AI easily supports enterprise policies for authentication, access, and data handling. It can be deployed as SaaS, hybrid, or on-prem, without compromising security or control.

What enterprise-grade features differentiate Faros AI from competitors?

Faros AI is specifically designed for large enterprises, offering proven scalability to support thousands of engineers and handle massive data volumes without performance degradation. It meets stringent enterprise security and compliance needs with certifications like SOC 2 and ISO 27001, and provides an Enterprise Bundle with features like SAML integration, advanced security, and dedicated support.

What resources do customers need to get started with Faros AI?

Faros AI can be deployed as SaaS, hybrid, or on-prem. Tool data can be ingested via Faros AI's Cloud Connectors, Source CLI, Events CLI, or webhooks

Faros AI Doppler Release: New Value Signals and ROI

Communicating the ROI of engineering investments for better business alignment

Engineering is one of the most expensive corporate functions. Yet its leaders often cannot easily articulate its impact on key initiatives, justify its resources, or demonstrate productivity improvements gained from new investments.

That puts a heavy strain on the relationship between the CFO, the CTO, and Heads of Engineering. And at this moment of AI transformation, it’s also hindering the effective application of AI to improve financial outcomes.

The latest release from Faros AI, Doppler, enables engineering leaders to gain valuable insights into the overall effectiveness and productivity of their software teams, as well as the specific impact of AI coding assistants like GitHub Copilot. These insights help transform the partnership between Engineering and Finance to ensure better resource allocation and value realization for the organization.

An image shows the highlights of the Faros AI Doppler release, including Investment Strategy module, AI Copilot Evaluation features, and Performance and admin enhancements.

Christian Doppler (1803–1853) makes a fitting namesake for this release due to its core principle of detecting and analyzing signals over time. With our new Investment Strategy intelligence module and enhanced AI Copilot Evaluation module, Faros AI helps organizations understand the true value and efficiency gains from their teams and technological investments, much like how Doppler radar helps meteorologists predict and respond to changing weather conditions.

A new intelligence module: Investment Strategy

The new Investment Strategy module from Faros AI answers three questions CFOs are constantly asking engineering leaders:

What is Engineering doing? Confirm that key initiatives are progressing.
How does it tie back to corporate strategy and objectives? Demonstrate that Engineering is working on the right things to achieve business outcomes.
Do we have the right resource allocation? Calculate the return on investment from FTEs, contractors, locations, and technology.

Faros AI provides a structured picture to answer these questions in periodic reviews, as well as data to support additional ad-hoc and impromptu analysis by fusing together data that is normally disconnected and reported on separately. Faros AI combines financial data (revenue, costs, etc.), employee HR data (contract type, role, seniority, location, average compensation per role, etc.), and productivity data (primarily from task management and version control systems) to provide these insights.

Infographic made of four quadrants for optimizing investment strategy with Faros A: Efficiency, Composition, Effectiveness and Allocation.

Key benefits of the Investment Strategy module:

Assess the efficiency and financial impact of your engineering initiatives by comparing their costs against revenue contributions, highlighting areas for potential optimization or concern.
Benchmark your engineering overhead and team ratios against industry standards, identifying outliers in team composition that may require attention to optimize efficiency and performance.
Evaluate the effectiveness of your talent mix and offshoring strategy by comparing developer productivity across different contract types and geographic locations, enabling you to balance cost and outcomes in terms of productivity, quality, and security.
Monitor the allocation of resources and time spent on critical company initiatives with a breakdown by geography and contract type, helping you to understand the distribution of innovation versus maintenance work and ensure your most expensive resources are utilized effectively.

To watch a 4-minute demo of the module, click here.

The industry’s most advanced copilot evaluation framework just got better

Engineering leaders are deploying AI coding assistants like GitHub Copilot, Amazon Q, and Gemini Code Assist under the watchful eyes of executives who anticipate significant productivity gains.

The AI Copilot Evaluation intelligence module provides a complete value framework for answering these top of mind questions:

How do I measure and communicate the impact?
How do I ensure we’ve given our teams the tools to adopt it successfully?
What is changing as a result of that adoption in terms of velocity, quality, security, and satisfaction?

The Doppler release introduces several essential new features to help with setting up your evaluation program, increasing adoption and usage, and measuring the impact.

Infographic showing the new AI Copilot Evaluation features in three buckets - Getting Started, Tracking Adoption, and Measuring Impact.

Quickly launch a strategic measurement program

The first step to understanding the impact of GitHub Copilot is getting a measurement program off the ground. Well, now that’s easier than ever. Faros AI has a free app you can download from the GitHub Marketplace.

Faros AI provides analytics that go far beyond the basics available from the GitHub Copilot API, including:

Adoption metrics per developer and team (DAU, WAU, MAU)
Full Copilot usage data history
Team and Power User filters
A/B testing and Before/After analysis
GitHub data correlated with task, deployment, quality, incident, security, and sentiment data from 100+ tools
Out-of-the-box dashboards for tracking adoption, impact, risk, and value

Plus, we have good news for Power BI users. The AI Copilot Evaluation dashboards are now available natively in Power BI, where you’ll get the exact same experience as in Faros AI's built-in BI layer.

Track Copilot adoption and maximize license usage

Turn your best users into mentors with Power User identification. No one is better positioned to train, mentor, and coach your teams on how to use copilots effectively than your power users. That’s why we’re helping you figure out who they are! Faros AI identifies your power users, so you can partner with them to enable team members. Power users have the credibility, context, and insider knowledge to help drive high-quality usage and minimize unused licenses.Understanding usage of chat.

Understanding usage of chat. Curious how your developers are benefiting from GitHub Copilot as its functionality evolves beyond code completion? You should be! In addition to lines of code and acceptance rate, Faros AI is now measuring usage of GitHub Copilot Chat, a conversational AI tool within the IDE.

GitHub just announced mixed licensing, where companies can now select Business or Enterprise plans at the organization level (instead of at the Enterprise level). That's a great use case for our A/B testing feature. Compare the impact of the different licensing options and make a data-driven choice.

Measure the impact and ROI of coding assistants

Benchmark how your results compare. Are you an outlier or on par with peers? What kind of results are others seeing? As you roll out any new technology, it's natural to seek this type of insight from peers. Leveraging our experience accompanying rollouts for the better part of a year, we now provide benchmarks for a range of metrics like PR Merge Rate, Cycle Time, Test Coverage, Code Smells, and more.

Capture the voice of the developer. Developer surveys complement system telemetry to help further understand your teams’ experience with coding assistants. Two new survey dashboards are now available to analyze and trend their feedback over time, supporting surveys run on a cadence or within the flow of work.

Faros AI also provides templated surveys to understand which tasks are being augmented by AI, quantify the time savings, understand how the time savings were reinvested, and gauge overall satisfaction with the coding assistant.

To watch a demo video of the complete AI Copilot Evaluation module, click here.

Platform enhancements for easier administration and better performance

The Faros AI platform is designed for the enterprise, handling data sets from tens of thousands of engineers. Every release, we make sure to include features that ease administration and improve platform performance.

Infographic of five new platform features in the Faros AI Doppler release: Improved connector performance, faster dashboard loading, streamlined administration, centralized account provisioning and Databricks Delta Sharing.

Here are the new features available in Doppler:

Improved performance for our most popular connectors. Jira and GitHub are the most popular data sources for engineering productivity insights. We’ve made it a whole lot easier to ingest large amounts of data, pull even more historical data, and select the specific data sets you want to backfill. Faros AI also supports webhooks for both these sources.
Faster dashboard load times. Users will now experience exponentially faster load times, now that we’ve completed the migration of our PostgreSQL analytics database to DuckDB. DuckDB is an embedded OLAP database, often referred to as the "SQLite for analytics".
Simplify user management with SCIM provisioning. As organizations grow and adopt more cloud-based applications, manual account management becomes increasingly complex and time-consuming. Many companies adopt centralized identity management solutions, like Okta, Microsoft Azure Active Directory, or Ping Identity. In addition to previously supported single sign-on through these services, Faros AI supports SCIM, short for System for Cross-domain Identity Management, for user account provisioning, deletion, and suspension.
Streamlined administration and empowered teams. We've overhauled our admin pages to make managing your organization's resources a breeze, including improved search, streamlined workflows for assigning teams and updating asset status, and a brand new team quick view with all your team's info at a glance. Plus, new permissions allow teams to create and manage themselves, boosting their autonomy and efficiency.
Now in Alpha: Securely share Faros AI data with Databricks data using Delta Sharing. Do you want to view Faros AI reporting data within Databricks to analyze it alongside other business data stores? That’s now possible with Databricks Delta Sharing, the industry’s first open protocol for secure data sharing. Contact us to learn more.

To learn more about these capabilities or speak to sales, reach out to our team.

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