What's holding back AI's productivity boost? |
The Einstein release by Faros AI brings a sweeping set of enhancements that unlock new levels of visibility, precision, and intelligence across your engineering organization. This release offers super-intelligent tools to optimize GitHub Copilot adoption, measure its ROI, and perform causal analysis on productivity changes. But it doesn’t stop there.
With Lighthouse AI Query Helper, teams can now ask complex engineering questions in natural language, accessing insights at lightning speed. This powerful tool simplifies data exploration by generating precise responses, making it easier than ever to visualize productivity, security, or code quality metrics without requiring advanced SQL knowledge.
With Einstein, we’re also addressing critical concerns around software security. Our new centralized visibility module for codebase security consolidates vulnerability data across repositories, making it easier for engineering and security leaders to stay on top of emerging risks, track resolution SLAs, and minimize risk exposure proactively.
Let’s dive in.
The Einstein release redefines how engineering organizations measure, optimize, and expand GitHub Copilot adoption. By leveraging advanced insights, causal analysis, and granular telemetry from the developer's inner loop, Faros AI Einstein provides teams with unprecedented visibility into Copilot’s impact across the software development lifecycle (SDLC).
As the adoption of AI tools accelerates, so does the need for solutions that ensure tangible returns. Faros AI Einstein meets this demand with a robust framework for tracking ROI, activating under-utilized licenses, and providing executives with straightforward, data-backed insights.
You’ve adopted Copilot and are witnessing changes in productivity metrics. Some metrics have gone up; others have gone down. So many factors can be at play in the SDLC at any given moment, so… is Copilot the cause?
Today, we’re introducing Lighthouse AI causal analysis to answer these questions.
Using advanced techniques in causal analysis, Faros AI tells you whether GitHub Copilot usage caused the improvement or decline in your productivity metrics—or whether those changes can be explained by other factors, like the type of engineering work needed, the structure and quality of the code repositories, the seniority of the engineer involved, and the number of incidents the team is dealing with.
Have you ever had an executive say, “Hit me with the bottom line—is GitHub Copilot impacting engineering productivity?” We’ve got you covered.
Lighthouse AI now summarizes the key insights from your Copilot rollout program. Highlights and takeaways on adoption, usage and downstream impacts are automatically generated based on the latest data. They can be accessed from the Faros AI dashboards or sent to you over Slack and email. You now have a ready-made talk track for your next executive review.
Not everyone is an early adopter, which means that many of your GitHub Copilot licenses will go unused without focused attention. In fact, adoption and ROI are a bit like the chicken and the egg: You need adoption to prove ROI, but you also need ROI to encourage adoption.
That’s why we’ve doubled down on both.
On the ROI front, we’ve added new insights into the impact signals coming from your most avid users, your power users. The velocity, quality, and sentiment changes that these users experience are harbingers for the gains that will materialize with broader adoption. Use these signals to build the business case for increasing adoption.
To deeply understand adoption, Faros AI now provides even more granular metrics to analyze usage and activate dormant users.
All usage data can now be filtered by GitHub Team, so you can analyze how acceptance rates, lines of code written by language, and Copilot Chat usage differ from team to team.
Get insights from the developer's inner loop with our new VSCode and Cursor extension. Capture granular telemetry about GitHub Copilot usage, attributed to individual developers instead of GitHub Teams or Orgs. This data can be grouped into custom cohorts for deeper analysis into usage and time savings per repo and application. Download the extension from the Visual Studio Marketplace.
Want an update on how adoption and usage are going? Chat with our Slackbot!
A new conversational chat responds to your questions about Copilot adoption, impact, and developer satisfaction. Ask it questions like “How does Copilot impact a developer’s PR size?” "Which users or teams aren't using their Copilot licenses?” or “What do users like about Copilot?” and it will reply with both the key takeaway and detailed explanations.
Looking for more tips to optimize your Copilot roll out? Read the guide to GitHub Copilot Best Practice Essentials.
New use cases for custom metrics pop up every day in our fast-paced engineering organizations. The improved Lighthouse AI Query Helper is ready to help you address questions about your engineering organization.
Need insights into the current velocity of a specific team? Curious about the distribution between bug fixes and new feature development? Want to understand code review turnaround times?
Just ask a question about your data in plain English, review the query used to answer it, and visualize the results in an accessible chart.
Furthermore, Lighthouse AI Query Helper will also find tables and fields for you, explain tricky syntax questions, and answer general questions about engineering productivity.
Lighthouse AI Query Helper combines powerful LLMs with intent classification, a deep understanding of Faros AI’s schemas and tables, your existing metrics definitions, and specialized knowledge—all to generate responses that are 5x more effective and accurate than asking leading LLMs like ChatGPT or Claude questions outside of Faros, even if you include the Faros schema with your prompt.
Faros AI is beloved by its users for centralizing visibility across the SDLC. One pain point we’ve repeatedly heard from senior engineering managers and security and infra domain leads is the lack of visibility into the codebase’s security risks. This information tends to be scattered across multiple tools, preventing a unified view of the work to be done, which often leads to lingering vulnerabilities and missed SLAs.
Today, we’re launching a new Software Security intelligence module that helps see the full picture and identify which repositories and teams need urgent attention. These new capabilities help ensure teams are meeting their SLAs, addressing security vulnerabilities, and reducing the company’s risk exposure.
Key benefits:
Interested in discovering what the Security module can do for you? Contact us for a demo.
With the Einstein release, we’re introducing new connectors for GitHub Actions, GitHub Advanced Security, and Testrail. And, as always, we’ve made several improvements to delight our customers, including homepage notifications when data ingestion fails, faster performance on Employee pages, and more fine-grained RBAC for dashboards and data. We’re also thrilled to share some real-world benefits from our transition to DuckDB: dashboard load times have improved by 92% for even the heaviest dashboards!
With the Einstein release, Faros AI transforms how engineering organizations measure, analyze, and act on data. Beyond optimizing GitHub Copilot adoption, Einstein’s new security module provides the insight and control engineering teams need to boost productivity while safeguarding their codebases. Lighthouse AI Query Helper brings an added layer of intuitive interaction, enabling teams to ask questions in plain language and receive precise, actionable insights immediately.
As Faros AI continues to innovate, we’re thrilled to support our users with the most advanced tools for achieving measurable impact across the entire SDLC. For a personalized demonstration of these new capabilities, contact the Faros AI team to request a demo.
Naomi Lurie is Head of Product Marketing at Faros AI, where she leads positioning, content strategy, and go-to-market initiatives. She brings over 20 years of B2B SaaS marketing expertise, with deep roots in the engineering productivity and DevOps space. Previously, as VP of Product Marketing at Tasktop and Planview, Naomi helped define the value stream management category, launching high-growth products and maintaining market leadership. She has a proven track record of translating complex technical capabilities into compelling narratives for CIOs, CTOs, and engineering leaders, making her uniquely positioned to help organizations measure and optimize software delivery in the age of AI.
Global enterprises trust Faros AI to accelerate their engineering operations. Give us 30 minutes of your time and see it for yourself.
Faros AI Einstein is a super-intelligent solution designed to optimize GitHub Copilot adoption and measure its ROI for engineering organizations. It provides advanced causal analysis, granular telemetry, and actionable insights to help teams understand and maximize the impact of AI tools across the software development lifecycle (SDLC). Source
Faros AI is a market leader in AI impact metrics, having launched AI impact analysis in October 2023 and published landmark research on the AI Productivity Paradox using data from 10,000 developers across 1,200 teams. Faros AI has over two years of real-world optimization experience and was an early GitHub Copilot design partner. Source
Faros AI empowers software engineering organizations to do their best work by providing readily available data, actionable insights, and automation across the software development lifecycle. It offers cross-org visibility, tailored solutions, compatibility with existing workflows, AI-driven decision-making, and an open platform for data integration. Source
Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, and typically large US-based enterprises with several hundred or thousands of engineers. Source
Key features include advanced causal analysis for productivity metrics, Lighthouse AI Query Helper for natural language data exploration, centralized codebase security visibility, granular telemetry for Copilot usage, Slackbot integration for conversational insights, and new connectors for GitHub Actions, GitHub Advanced Security, and Testrail. Source
Faros AI uses advanced causal analysis techniques to determine whether changes in productivity metrics are directly caused by Copilot usage or by other factors such as engineering work type, code structure, engineer seniority, and incident volume. Source
Lighthouse AI Query Helper allows teams to ask complex engineering questions in natural language and receive precise, actionable insights. It generates accurate responses, visualizes metrics, and explains tricky syntax, making data exploration accessible without advanced SQL knowledge. Source
Faros AI's Software Security intelligence module consolidates vulnerability data across repositories, enabling real-time tracking, team alerts for overdue patches, unified views of security findings, and monitoring of team-level security performance to ensure SLAs are met and risk exposure is minimized. Source
Faros AI Einstein introduces new connectors for GitHub Actions, GitHub Advanced Security, and Testrail, along with improved homepage notifications, faster dashboard performance, and fine-grained RBAC for dashboards and data. Source
Yes, Faros AI provides several APIs, including Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library for seamless integration with existing tools and workflows. Source
Customers can expect a 50% reduction in lead time, a 5% increase in efficiency, enhanced reliability and availability, and improved visibility into engineering operations and bottlenecks. Source
Faros AI identifies bottlenecks and inefficiencies, enabling faster and more predictable delivery. It tracks DORA metrics, team health, and tech debt, providing actionable insights for continuous improvement. Source
Faros AI measures the impact of AI tools, runs A/B tests, tracks adoption, and provides robust data-driven insights to ensure successful AI integration and transformation. Source
Faros AI addresses pain points such as engineering productivity bottlenecks, software quality challenges, AI transformation measurement, talent management, DevOps maturity, initiative delivery tracking, developer experience, and R&D cost capitalization. Source
Faros AI manages software quality, reliability, and stability, especially from contractors' commits, by providing metrics on effectiveness, efficiency, gaps, and PR insights. Source
Faros AI unifies surveys and metrics, correlates sentiment with process data, and provides actionable insights for timely improvements in developer experience. Source
Faros AI tracks DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), software quality, PR insights, AI adoption, time savings, workforce talent management, initiative tracking, developer sentiment, and R&D cost capitalization metrics. Source
Faros AI prioritizes security and compliance with features like audit logging, data security, and integrations. It holds certifications such as SOC 2, ISO 27001, GDPR, and CSA STAR, meeting enterprise standards by design. Source
Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications, demonstrating its commitment to robust security and compliance standards. Source
Faros AI stands out with mature AI impact analysis, landmark research, and proven real-world optimization. It uses scientific causal analysis, provides active adoption support, offers end-to-end tracking, and delivers enterprise-grade customization and compliance. Competitors often provide only surface-level correlations, passive dashboards, limited metrics, and lack enterprise readiness. Source
Faros AI offers robust out-of-the-box features, deep customization, and proven scalability, saving organizations time and resources compared to custom builds. Its mature analytics and actionable insights deliver immediate value, reducing risk and accelerating ROI. Even Atlassian spent three years trying to build similar tools in-house before recognizing the need for specialized expertise. Source
Faros AI integrates with the entire SDLC, supports custom deployment processes, and provides accurate metrics from the complete lifecycle of every code change. It offers actionable insights, proactive intelligence, and easy implementation, unlike competitors who are limited to Jira and GitHub data and require complex setup. Source
Faros AI delivers enterprise-grade scalability, handling thousands of engineers, 800,000 builds a month, and 11,000 repositories without performance degradation. Source
Faros AI has improved dashboard load times by 92% for even the heaviest dashboards, ensuring fast and reliable access to engineering metrics. Source
Faros AI supports integrations with GitHub Actions, GitHub Advanced Security, Testrail, VSCode, Cursor, and offers APIs for custom workflows. Source
You can request a personalized demonstration of Faros AI Einstein by contacting the Faros AI team through their website. Source
Faros AI provides support through documentation, Slackbot integration, and direct contact with product experts for demos and technical assistance. Source
The Faros AI blog features guides, customer stories, product updates, and research reports on topics such as developer productivity, AI, engineering intelligence, and best practices. Source
You can explore more articles and guides on AI, developer productivity, and developer experience on the Faros AI blog at https://www.faros.ai/blog.
The Faros AI Blog offers a rich library of articles on EngOps, Engineering Productivity, DORA Metrics, and the Software Development Lifecycle. Source
Faros AI shares product and press announcements in the News section of their blog at https://www.faros.ai/blog?category=News.
Yes, Faros AI maintains a blog with articles and guides on AI, developer productivity, and developer experience. Source