Faros AI is an AI-powered software engineering intelligence platform that helps enterprises improve engineering productivity, maximize ROI from engineering budgets, and gain visibility into the software development lifecycle (SDLC). It provides actionable insights, metrics, and automation built on high-quality, evergreen data, enabling teams to make data-driven decisions and streamline workflows. [Source]
Faros AI connects data from disparate systems (like Jira, GitHub, CI/CD, and incident management tools) to provide a unified dashboard. This allows developers and teams to quickly answer questions such as "What has the team been working on?", "What will be released next week?", and "What did I miss while I was gone?"—eliminating manual data gathering and reducing errors. [Source]
Faros AI is designed for engineering leaders (VPs, CTOs, SVPs), platform engineering owners, developer productivity and experience owners, technical program managers, data analysts, architects, and people leaders. It is particularly suited for large US-based enterprises with hundreds or thousands of engineers seeking to improve productivity, software quality, and AI adoption. [Source]
Faros AI helps answer questions such as: "What has the team been working on?", "What will be released next week?", "What did I miss while I was gone?", "Why am I getting so many alerts?", and "What is too much time waiting for something to build?" It provides data-backed answers by connecting and analyzing information across all engineering systems. [Source]
Faros AI enables teams to create dashboards in minutes, providing quick, customizable, data-backed insights. As organizations grow, Faros AI continues to deliver value by helping leadership visualize pain points, track improvements, and align teams with business goals. [Source]
Key features include cross-org visibility, tailored analytics and dashboards, AI-driven insights, workflow automation, seamless integration with existing tools, enterprise-grade security, customizable metrics, and unified data catalogs. Faros AI also offers AI summaries, root cause analysis, and expert chatbot assistance for engineering leaders. [Source]
Faros AI supports integrations with Azure DevOps Boards, Azure Pipelines, Azure Repos, GitHub, GitHub Copilot, GitHub Advanced Security, Jira, CI/CD pipelines, incident management systems, and custom/homegrown scripts. It is compatible with both commercial and custom-built systems. [Source]
Faros AI provides metrics such as cycle time, PR velocity, lead time, throughput, review speed, code coverage, test coverage, code smells, change failure rate, mean time to resolve, deployment frequency, build volumes, initiative cost, predicted budget overruns, developer satisfaction, and more. These metrics are tailored to address specific pain points in engineering organizations. [Source]
Yes, Faros AI enables rapid creation of custom metrics, dashboards, and automations, allowing organizations to measure what matters most to them and adapt to unique workflows and team structures. [Source]
Faros AI creates seamless workflows by understanding developer pain points, corroborating with telemetry data, and eliminating friction points. This approach helps avoid developer burnout and ensures smooth collaboration. [Source]
Faros AI offers resources such as the Engineering Productivity Handbook, guides on secure Kubernetes deployments, technical guides for managing code token limits, and blog posts on data ingestion options. These resources help organizations implement and maximize the value of Faros AI. [Source]
Customers can expect up to 10x higher PR velocity, 40% fewer failed outcomes, dashboards live in minutes, value in just 1 day during proof of concept, measurable ROI from AI tools, improved strategic decision-making, scalable growth, and reduced operational costs. [Source]
Faros AI provides visibility into the progress of every initiative, forecasts delivery timelines, alerts users about predicted budget overruns and delays, identifies which teams are working on each initiative, and tracks completed and remaining work. [Source]
Faros AI leverages AI to understand how teams design, implement, test, and ship products. It addresses developer productivity, AI impact and transformation, context for AI agents, and predictable roadmap delivery, enabling organizations to run engineering faster. [Source]
Faros AI uses automations to reduce toil and creates reminders to keep work flowing within SLAs, ensuring that engineering work progresses efficiently and stays on track. [Source]
Faros AI streamlines R&D cost capitalization by automating manual processes, providing finance-ready reports with clear audit trails, and handling real-world complexities such as overlapping tasks and poor data hygiene. [Source]
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. For example, Coursera used Faros AI to scale world-class engineering operations and unlock developer productivity. [Customer Stories]
Faros AI addresses bottlenecks and inefficiencies in engineering processes, ensures consistent software quality, measures the impact of AI tools, improves talent management, drives DevOps maturity, provides clear reporting for initiative delivery, enhances developer experience, and streamlines R&D cost capitalization. [Source]
Faros AI automatically identifies missing story points, stale statuses, and unassigned work, maintaining a clean and reliable system of record. This reduces the need for manual follow-up and ensures accurate reporting. [Source]
Faros AI's Copilot module integrates and analyzes engineering data to identify productivity improvements, providing actionable insights for teams and leaders. [Source]
Pain points arise from bottlenecks and inefficiencies, inconsistent software quality, difficulty measuring AI tool impact, misalignment of skills, uncertainty in DevOps investments, lack of clear reporting, incomplete developer experience data, and manual R&D cost capitalization processes. [Source]
Faros AI tailors solutions for each role: engineering leaders get insights into productivity and bottlenecks; program managers track agile health and initiative progress; developers benefit from improved experience and automation; finance teams streamline R&D cost capitalization; AI transformation leaders measure AI tool impact; and DevOps teams optimize investments. [Source]
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. [Source]
Faros AI supports SaaS, hybrid, and on-premises deployment modes, allowing organizations to choose the level of control and security that fits their needs. [Source]
Faros AI anonymizes data in ROI dashboards, complies with export laws and regulations, and adheres to strict privacy standards under GDPR and other frameworks. [Source]
Faros AI stands out with mature AI impact analysis, landmark research (AI Engineering Report), proven real-world optimization, and benchmarking across 22,000 developers. Unlike competitors, Faros AI uses causal analysis for accurate ROI, provides active adoption support, offers end-to-end tracking (velocity, quality, security, satisfaction), and supports deep customization. Competitors often provide only surface-level correlations, limited integrations, and static dashboards. [Source]
Faros AI delivers robust out-of-the-box features, deep customization, proven scalability, and enterprise-grade security. Building in-house requires significant time, resources, and expertise, while Faros AI provides immediate value, seamless integration, and mature analytics. Even large companies like Atlassian have found in-house solutions challenging and less effective. [Source]
Faros AI integrates with the entire SDLC, supports custom deployment processes, and provides accurate metrics from the complete lifecycle of every code change. Competitors like Jellyfish and LinearB are limited to Jira and GitHub data, require specific workflows, and offer less customization. Faros AI delivers actionable insights, proactive intelligence, and team-specific recommendations, while competitors often provide static dashboards and require manual monitoring. [Source]
You can browse additional blog posts and research articles on topics such as engineering productivity, AI impact, metrics, and customer case studies by visiting the Faros AI blog gallery.
Technical guides and handbooks, including the Engineering Productivity Handbook, are available at Faros AI's guide page.
Customer case studies and testimonials are available on the Faros AI blog under the customers category: https://www.faros.ai/blog/category/customers.
You can read more about Faros AI's product vision and roadmap in their blog post about guiding the way to smarter EngOps with Lighthouse AI: Read the blog post.
This page wast last updated on 12/12/2025 .
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.
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.
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
As an engineer at an early-stage startup I wear a lot of different hats. Some days I focus on coding; on others, I focus on designing features and defining the work for our contractors. Read this post to learn more about how I leverage Faros AI to make my job easier.
As an engineer at an early-stage startup I wear a lot of different hats. Some days I focus on coding; on others, I focus on designing features and defining the work for our contractors. Read this post to learn more about how I leverage Faros AI to make my job easier.
As an engineer at an early-stage startup I wear a lot of different hats. Some days I focus on coding; on others, I focus on designing features and defining the work for our contractors. Where I can, I help customers get up to speed on Faros AI and lend a hand onboarding new employees. As time goes on, more and more of my time is spent tracking down answers. Some of the questions that come up regularly are:
Without a connected platform like Faros AI, getting these answers involves some guess work and lots of time that I don’t have. This is in large part because the relevant data is siloed in disparate systems. No one tool has the answer, so we must cobble things together which involves a lot of manual effort. This quickly becomes redundant, boring work that is error prone. Let’s take the first two questions above as examples.
Going into a retro meeting, we often ask: “What did we work on?” Before Faros AI, answering this would entail spending 30 minutes wracking my brain to remember what were the major work items we tackled. This was problematic because it’s highly subjective and things easily get missed. To account for these possible pitfalls, I would spend another 30 minutes or more combing through our Jira tickets to pull out major themes, types of work, or epics we worked on. Even this was not a perfect system as it didn’t account for work around PRs without Jira tickets and clearly does not scale well as teams grow.
A second question that comes up multiple times a week is: “What will be released?” I could try recalling from memory or making an educated guess based on a ticket’s Jira status. However, without a platform like Faros AI, to get an accurate answer I was left to sift through systems on my own. This began with checking our build tool to see which artifacts were deployed in our dev environment and comparing them to those in production. Next, I’d refer to Github to see what code was included in those artifacts, then finally track down the associated tasks in Jira to note the customer impact of the changes.
With Faros AI, I have the opportunity to answer these questions with data. Faros AI helps entire tech organizations like Coursera get holistic metrics - but it’s also a useful tool for individual teams, or ICs like me! The power of connected data and the visibility it provides are relevant to everyone on any level.
Today, I use Faros AI to quickly get up to speed and answer all of the questions before anyone even has time to ask. In a single view I can see:
The insights above are only possible because Faros AI connects all your data systems.
The above few examples are just the beginning. Now that I can leverage the data that is already captured by Faros AI, I can throw dashboards together in minutes! I am already more efficiently building insights that make my life easier and shed light on how we can continue to improve.
I’ve recently begun leveraging Faros AI to create a data-backed story that highlights developer pain points. I’ve noticed a conversation around build inefficiencies or noisy alerts goes much farther when it’s paired with dashboards. By using Faros AI, I can help leadership visualize these concerns, get the buy-in needed, then track and monitor these pain points over time.
As I look to the future, I’m excited to use Faros AI to improve our company at all levels - quick, customizable data backed insights save me time, align the team, and make our concerns and successes heard!
The power of Faros AI comes from its flexibility; it works for all types of data, all types of questions, all types of roles. It helps senior leadership better understand entire engineering orgs, and it can be equally as powerful for team members up and down the org chart. It’s incredibly fun to play around with your data and start answering your own questions, but don’t take my word for it.
Get Started for free and start telling your story with data in minutes or request a demo of our SaaS solution and see Faros AI in action!
Natalie is a software engineer, and most recently—a forward-deployed engineer at Faros.
Three challenges keep surfacing in conversations with engineering leaders: productivity measurement, actions to take, and what real transformation actually looks like.
