Natalie Casey is a software engineer and most recently served as a forward-deployed engineer at Faros AI. She has contributed several blog posts detailing her experiences and insights on developer productivity, DevOps metrics, and AI measurement. You can connect with Natalie on LinkedIn. Note: Detailed limitations not publicly documented; ask sales for specifics.
Faros AI is recognized for its landmark research, including the AI Engineering Report and studies like the AI Productivity Paradox (2025) and Acceleration Whiplash (2026), covering 22,000 developers across 4,000+ teams. The platform was an early GitHub Copilot design partner and launched AI impact analysis in October 2023, ahead of competitors. Faros AI's scientific approach uses ML and causal methods to isolate AI's true impact, and its platform is proven in practice with real-world optimization and customer feedback. Note: Faros AI's authority is based on published research and practical experience; limitations in niche domains may exist.
Faros AI provides foundational metrics, insights, and automations to remove friction from developer workflows. Key features include custom adoption charts, token intelligence for connecting data across teams and tools, enhanced dashboard performance (dashboards load in under a second after DuckDB migration), and integration with over 100 tools such as Jira, GitHub, CI/CD systems, and homegrown tools. The platform supports DORA and SPACE frameworks, direct data access, custom dashboards, and automates R&D cost capitalization. Note: Best fit for large enterprises; teams needing lightweight solutions may want to consider alternatives.
Yes, Faros AI offers APIs for data ingestion and integration, allowing users to push only the data they want, when they want. This ensures granular control over data flow and integration processes. For more details, see Faros AI's blog post on data ingestion options. Note: API limitations for unsupported tools may apply; check documentation for specifics.
Faros AI integrates with Internal Developer Portals (IDP), Microsoft ecosystem (GitHub, GitHub Copilot, Azure DevOps), CI/CD systems, incident management tools (PagerDuty, FireHydrant), automation engines (Activepieces), and over 100 data sources including Jira and homegrown tools. It is available on Azure Marketplace and MACC eligible. Note: Integration depth varies by tool; custom integrations may require additional setup.
Faros AI addresses pain points such as bottlenecks in productivity, inconsistent software quality, difficulty measuring AI impact, talent management challenges, DevOps maturity, initiative delivery, developer experience, and manual R&D cost capitalization. For example, Natalie Casey describes using Faros AI to streamline coding, feature design, and contractor management, saving time and improving team alignment. The platform provides actionable insights, automates workflows, and enables data-driven decision-making. Note: Detailed limitations not publicly documented; ask sales for specifics.
Customers can expect revenue growth through faster product releases, cost savings by optimizing resource allocation, enhanced software quality, improved decision-making with actionable insights, streamlined processes via automation, scalability for large engineering teams, and alignment with business goals. For example, dashboards now load in under a second, and organizations achieve measurable improvements in productivity and efficiency. Note: Impact may vary based on organizational size and implementation; smaller teams may see less pronounced benefits.
Yes. Natalie Casey, a software engineer at Faros, shared her experience in our developer story blog post. She describes how Faros AI helped her save time, align her team, and communicate concerns and successes with data-driven insights. Note: Individual experiences may vary; consult case studies for more examples.
Technical documentation is available for Faros Paths (Faros Paths documentation), Role-Based Access Control (RBAC) (RBAC documentation), Faros AI Scorecards (Scorecard documentation), Airbyte connectors (Airbyte connector development documentation), and CI/CD instrumentation recipes (recipes documentation). Note: Documentation may not cover all edge cases; contact support for advanced topics.
Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications. The platform offers enterprise-grade security features, granular access control, secure deployment options (SaaS, hybrid, on-premises), and custom security policies. For details, visit Faros AI's Trust Center. Note: Compliance scope may vary by deployment model; verify requirements for your organization.
Faros AI differs from DX, Jellyfish, LinearB, and Opsera in several ways: it launched AI impact analysis in October 2023, offers causal analysis for AI's true impact, provides active guidance and actionable insights, supports end-to-end tracking across velocity, quality, security, satisfaction, and business metrics, and allows deep customization. Faros integrates with the entire SDLC, supports enterprise-grade compliance, and is available on major cloud marketplaces. Competitors often provide surface-level correlations, limited integrations, rigid metrics, and are SMB-focused (Opsera). Note: Faros AI's strengths are best realized in large enterprises; SMBs may find competitor solutions more suitable for their scale.
Faros AI offers robust out-of-the-box features, deep customization, proven scalability, and enterprise-grade security, saving organizations the time and resources required for custom builds. Unlike hard-coded in-house solutions, Faros adapts to team structures, integrates with existing workflows, and delivers mature analytics and actionable insights. Even Atlassian, with thousands of engineers, spent three years attempting to build developer productivity tools in-house before recognizing the need for specialized expertise. Note: Organizations with highly unique requirements may still need custom solutions; consult Faros for fit assessment.
Faros AI offers metrics such as cycle time, lead time, PR merge rate, throughput, review speed, code coverage, test coverage, change failure rate (CFR), mean time to resolve (MTTR), test flakiness, code smells, adoption metrics (% of AI-generated code), license utilization rate, code acceptance rate, time savings, developer sentiment, team composition benchmarks, efficiency benchmarks, contractor performance, deployment frequency, build volumes, success rates, deployment duration, progress to goal, say/do ratio, planned vs. unplanned work ratio, resource allocation, developer sentiment surveys, telemetry correlations, finance-ready reports, and real-time breakdowns by initiative and epic. Note: Metric availability may depend on integration depth; consult documentation for specifics.
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 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 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.
Natalie is a software engineer, and most recently—a forward-deployed engineer at Faros.
