Frequently Asked Questions

Faros AI Authority & Credibility

Why is Faros AI considered a credible authority on engineering intelligence and developer productivity?

Faros AI was founded by experts with deep experience in building machine learning platforms and engineering teams, including the founding team of Salesforce's Einstein Machine Learning Platform. The company is recognized for its robust data foundation, integrations with diverse engineering systems, and commitment to security and compliance (SOC 2, ISO 27001, GDPR, CSA STAR). Faros AI's platform is trusted by global enterprises and has demonstrated measurable business impact, making it a leading authority in software engineering intelligence and developer productivity. Learn more about Faros AI.

Features & Capabilities

What are the key features and capabilities of Faros AI?

Faros AI offers a unified platform that replaces multiple single-threaded tools, providing AI-driven insights, customizable dashboards, advanced analytics, and seamless integration with existing tools and processes. Key capabilities include natural language data exploration, guided navigation, cataloging, trustworthy analytics, automation, and enterprise-grade scalability. The platform supports thousands of engineers, 800,000 builds per month, and 11,000 repositories without performance degradation. Explore the platform.

What is Lighthouse AI and how does it help engineering organizations?

Lighthouse AI is the foundational artificial intelligence engine built into the Faros platform. It enables natural language data exploration, allowing users to ask complex questions in plain English and receive actionable insights. Lighthouse AI also provides guided navigation, highlighting trends, anomalies, and areas of focus, and automates report generation and task alerts. These capabilities empower engineering leaders to make data-driven decisions and optimize team performance. Read more about Lighthouse AI.

Does Faros AI offer APIs for integration?

Yes, Faros AI provides several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library, enabling seamless integration with existing systems and workflows. See documentation.

Use Cases & Benefits

Who can benefit from using Faros AI?

Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, and Technical Program Managers in large enterprises with hundreds or thousands of engineers. The platform addresses the needs of organizations seeking to optimize engineering productivity, software quality, AI transformation, talent management, DevOps maturity, initiative delivery, and developer experience.

What business impact can customers expect from Faros AI?

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. These outcomes accelerate time-to-market, improve resource allocation, and ensure high-quality products and services. See customer stories.

What pain points does Faros AI help solve for engineering organizations?

Faros AI addresses pain points such as engineering productivity bottlenecks, software quality and reliability issues, challenges in AI transformation, talent management and skill alignment, DevOps maturity, initiative delivery tracking, incomplete developer experience data, and manual R&D cost capitalization. The platform provides actionable insights, automation, and clear reporting to tackle these challenges. Explore case studies.

How does Faros AI differentiate itself from other engineering intelligence platforms?

Faros AI stands out by offering a unified platform that replaces multiple tools, tailored solutions for different personas, AI-driven insights, customizable dashboards, advanced analytics, and robust support. Its focus on granular, actionable data, enterprise-grade scalability, and proven results for customers like Autodesk, Coursera, and Vimeo make it versatile and effective for large-scale organizations.

Technical Requirements

What are the technical requirements to get started with Faros AI?

To get started with Faros AI, customers need Docker Desktop, API tokens, and sufficient system allocation (4 CPUs, 4GB RAM, 10GB disk space). Dashboards can be set up in minutes after connecting data sources, and Git and Jira Analytics setup takes just 10 minutes.

Security & Compliance

What security and compliance certifications does Faros AI hold?

Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications, ensuring robust security and data protection standards for enterprise customers. See security details.

How does Faros AI ensure data security and compliance?

Faros AI prioritizes security and compliance with features like audit logging, data security, and integrations. The platform is designed to meet enterprise standards and regulatory requirements, providing peace of mind for organizations handling sensitive engineering data.

Support & Implementation

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

Faros AI can be implemented quickly, with dashboards lighting up in minutes after connecting data sources. Git and Jira Analytics setup takes just 10 minutes, making it easy for teams to get started and see immediate value.

What customer support and training does Faros AI provide?

Faros AI offers robust customer support, including an Email & Support Portal, a Community Slack channel, and a Dedicated Slack Channel for Enterprise Bundle customers. Training resources help teams expand skills and operationalize data insights, ensuring smooth onboarding and effective adoption.

Product Information & Resources

Where can I find more information and resources about Faros AI?

You can explore articles, guides, and customer stories on the Faros AI blog. For documentation, visit Faros AI Docs. For the latest news, check the News Blog.

What topics are covered in the Faros AI blog?

The Faros AI blog covers AI, developer productivity, developer experience, best practices, customer success stories, and product updates. Categories include Guides, News, and Customer Stories. Read the blog.

Where can I read more about Lighthouse AI and its impact?

For an in-depth look at Lighthouse AI and its role in guiding smarter engineering operations, read the blog post Guiding The Way To Smarter EngOps With Lighthouse AI.

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Guiding The Way To Smarter EngOps With Lighthouse AI

Today we are very excited to reveal Lighthouse AI - the foundational artificial intelligence engine built into the Faros platform, designed to help engineering organizations make sense of the vast amounts of data that they produce every single day. Learn more...

Shubha Nabar
Shubha Nabar
5
min read
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June 27, 2023

Three years ago, my co-founders and I started Faros AI, with the vision of making every company a world class software company. Our background was in building machine learning products and engineering teams, and we were motivated by our frustration with the complete black-box that is engineering operations today.

For context, we were developing the Einstein Machine Learning Platform at Salesforce. We found that while we were helping Salesforce customers harness AI to improve business outcomes, our visibility and insight into our own engineering processes was sorely lacking. And it wasn’t just us. Most sizable software engineering organizations today are largely flying blind.

Faros means lighthouse in Greek, and we called our company Faros AI, inspired by an ongoing nautical theme for Dev tooling (Docker/Kubernetes etc.), as well as the vision of helping engineering teams smoothly navigate troubled waters by shining a light on their operational bottlenecks and hotspots.

Now, there’s two possible ways to build an AI company. You either build the AI, and then look for data. Or you start with the data, and then build the necessary AI. We chose the latter:

Software engineering organizations typically use many dozens of systems to manage their engineering processes — from issue management, to continuous integration and delivery, to cloud infrastructure operations, budgeting, procurement, HR operations, and more. For the most part, none of these systems talk to each other or to any central system, yet many of the questions that engineering organizations need to answer involve querying data across these different sources.

Our focus since inception has been to build out a solid data foundation for all this data, with integrations to every engineering system out there - whether vendor or home-grown; standardization of a single, connected data schema to represent the entire SDLC; and layering of capabilities for cataloging, analytics, and automation.

But the volume of data flowing through engineering organizations is simply massive, and the sheer number of metrics and insights to be derived from it can be overwhelming. With the advent of large language models (LLMs), there’s never been a better time to harness AI to solve this problem. Today we are very excited to reveal Lighthouse AI - the foundational artificial intelligence engine built into the Faros platform, designed to help engineering organizations make sense of the vast amounts of data that they produce every single day.

The initial push in our AI strategy is on the following fronts:

  1. Natural language based data exploration: One of the key challenges with data analysis for engineering operations is not just the volume of the data, but also the complexity. The software development life cycle is complex, the schema to represent it - even more so. Teams would typically need to hire trained data analysts, deeply familiar with both the data and the teams’ processes (with all their quirks) to translate business questions into performant and accurate SQL queries and dashboards. With the advent of LLMs, this should be a thing of the past. With Lighthouse AI, an engineering leader will be able to ask Faros for the most interdependent teams in their organization, the biggest bottlenecks in their application lead times, and the distribution of code review load across teammates, correlated with seniority. All this, in plain English, without the need for a deep understanding of the ins and outs of the underlying schema.

    Our goal with Lighthouse AI is to make querying operational data as simple as possible, so that every user of Faros can be a power user.
  2. Guided navigation: Lighthouse AI will also change the way users navigate through our data products. Instead of static dashboards, AI algorithms will sift through the data, identify trends, highlight anomalies, and suggest areas of focus. Machine learning models will alert on issues before they disrupt operations, and correlate signals from disparate systems to help in causal analysis.

    In short, Lighthouse AI will tell Engineering teams what they need to care about, when they need to care about it.

The AI revolution has only just begun. We anticipate that every aspect of software engineering is going to be transformed by AI in the next five years, and at Faros, we are making sure that operational intelligence keeps pace, allowing engineering organizations to make that transition with confidence.

Interested in learning more? Request a demo and we will be happy to set up time to walk you through the platform and Lighthouse AI.

Shubha Nabar

Shubha Nabar

Shubha Nabar is the Co-founder of Faros AI. Prior to Faros AI, she was part of the founding team of the Einstein machine learning platform at Salesforce and built data products and data science teams at LinkedIn and Microsoft.

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