Faros AI Einstein Release: Super-Intelligence for AI Copilot Adoption

Unlocking the Power of AI in Software Development with Faros AI Einstein

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.

Super-Intelligence for AI Copilot Adoption

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.

Productivity Causal Analysis — Did GitHub Copilot Impact this Metric?

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.

Faros AI summarizes GitHub Copilot's impact on engineering productivity with advanced causal analysis

AI Insights/Summary — The Talk Track for Exec Reviews

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.

Granular Analytics to Boost Adoption

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.

The power user filter zooms into the early ROI signals from early adopters

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.

New Slack Chatbot for Copilot Adoption and Impact

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.

Ask natural language questions about Copilot adoption and impact with our Slackbot

Looking for more tips to optimize your Copilot roll out? Read the guide to GitHub Copilot Best Practice Essentials.

A Big AI Boost (5x!) to Custom Metrics

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.

Centralized Visibility for Codebase Security

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:

• Resolve vulnerabilities within your SLAs with real-time tracking and team alerts for pending or overdue patches.
• Identify the most vulnerable parts of your codebase with a single unified view of security findings and measure the ROI of security activities over time
• Monitor team-level security performance with vulnerability resolution performance. Identify which teams require more support or education on security best practices.

Security - Vulnerability Detection Intelligence on Faros AI

Security - Vulnerability Remediation Summary Faros AI

Interested in discovering what the Security module can do for you? Contact us for a demo.

New Connectors and Delightful Admin Improvements

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!

Einstein Release: Driving Impact Across Productivity, Security, and Insights

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.