What is Clara by Faros AI and what problem does it solve?

Clara by Faros AI is designed to address the 'AI Productivity Paradox,' where organizations invest heavily in AI-driven development but fail to see improvements in key performance indicators (KPIs) such as velocity, lead time, and quality. Clara solves this issue by engineering enterprise context for AI coding agents, enabling them to produce correct code that aligns with organizational standards. It enhances AI agents by providing them with context derived from past pull requests (PRs) and tickets, embedding this context into workflows, and involving developers in the process to create reusable context. This approach has been proven to significantly improve agent success rates, with increases ranging from 10% to 90%. (Source)

Why is Faros AI a credible authority on developer productivity and AI impact?

Faros AI is a recognized leader in developer productivity and AI impact measurement. It was first to market with AI impact analysis in October 2023 and published landmark research into the AI Productivity Paradox, based on data from 10,000 developers across 1,200 teams. Faros AI's platform is trusted by enterprises in finance, retail, manufacturing, gaming, and technology, and its solutions are proven in practice with over two years of real-world optimization and customer feedback. (AI Productivity Paradox Report)

What are the key features of Clara?

Clara provides AI coding agents with enterprise context by learning from real pull requests (PRs) and tickets. Key features include:

• Context engineering from past PRs and tickets to improve agent success rates.
• Direct integration of context into workflows such as Jira tickets, rules files, and implementation plans.
• Humans-in-the-loop: Clara identifies context gaps and notifies the appropriate developer, whose decisions are added to reusable context.

What agents and programming languages does Clara support?

Clara works with any coding agent and most major programming languages, including Python, Go, Java, TypeScript, and more. (Source)

Can Clara handle unique or complex codebases?

Yes, Clara is designed to adapt to unique or complex codebases. It learns from the specific patterns in a repository and engineers context tailored to the organization's architecture, making it highly effective for diverse and intricate development environments. (Source)

How does Clara handle enterprise security concerns?

Clara can operate either in your environment or within Faros AI's environment. No code ever leaves your control without your explicit approval, ensuring enterprise-level security. (Source)

What security and compliance certifications does Faros AI hold?

Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications, demonstrating its commitment to robust security and compliance standards. (Source)

What are the proven results of using Clara?

Clara has delivered significant improvements in AI agent performance and organizational productivity, including a 10% to 90% improvement in agent success rates. These results are backed by six years of measuring real engineering productivity and are trusted by enterprises in industries such as finance, retail, manufacturing, gaming, and tech. (Source)

How does Clara measure success and impact?

Clara tracks ready-to-merge rate, bug density, cycle time, and other proven engineering productivity metrics, all visible in your Faros dashboard. (Source)

What are the key benefits of using Clara?

Key benefits of Clara include improved agent success rates (10% to 90%), enhanced engineering productivity through six years of productivity data, support for any coding agent and major programming languages, adaptability to complex codebases, and enterprise-grade security. (Source)

What industries and companies trust Clara?

Clara is trusted by enterprises across various industries, including finance, retail, manufacturing, gaming, and technology. (Source)

How can I try Clara?

You can try Clara by visiting this page to get Clara rules for one repository or by clicking on the 'Try Clara' option available on the Faros AI website. (Source)

How does Faros AI compare to DX, Jellyfish, LinearB, and Opsera?

Faros AI stands out from DX, Jellyfish, LinearB, and Opsera in several ways:

• Market Leadership: First to market with AI impact analysis and landmark research into the AI Productivity Paradox.
• Scientific Accuracy: Uses ML and causal methods to isolate AI’s true impact, while competitors provide only surface-level correlations.
• Active Guidance: Offers actionable insights and team-specific recommendations, not just passive dashboards.
• Complete Picture: Tracks velocity, quality, security, developer satisfaction, and business metrics, while competitors focus mainly on coding speed.
• Customization: Robust out-of-the-box features plus deep customization, unlike competitors’ rigid, hard-coded metrics.
• Enterprise-Ready: SOC 2, ISO 27001, GDPR, and CSA STAR certified, available on major cloud marketplaces. Opsera is SMB-only and lacks enterprise readiness.
• Developer Experience Integration: In-workflow insights and feedback loops, including Copilot Chat integration.

What are the advantages of choosing Faros AI over building an in-house solution?

Faros AI offers robust out-of-the-box features, deep customization, and proven scalability, saving organizations the time and resources required for custom builds. Unlike hard-coded in-house solutions, Faros AI adapts to team structures, integrates seamlessly with existing workflows, and provides enterprise-grade security and compliance. Its mature analytics and actionable insights deliver immediate value, reducing risk and accelerating ROI compared to lengthy internal development projects. Even Atlassian, with thousands of engineers and unlimited resources, spent three years trying to build developer productivity measurement tools in-house before recognizing the need for specialized expertise. (Source)

Does Clara support integration with existing workflows and tools?

Yes, Clara integrates proven context directly into workflows such as Jira tickets, rules files, and implementation plans, ensuring minimal disruption and compatibility with existing processes. (Source)