With Augment’s context-aware suggestions and multi-language support, Newfront improved developer efficiency. The team could focus on building AI-driven insurance models and production-level AI integrations, rather than routine coding tasks.

The Challenge  

Newfront is transforming the delivery of risk management, employee experience, insurance, and retirement solutions by building the modern insurance platform. They required a scalable solution to manage their evolving codebase. Their full-stack engineering team works across multiple technologies, including React, Next.js, TypeScript, and Python. Newfront needed a way to ensure consistency and efficiency, especially when onboarding new developers.

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Insurance is a data-heavy and traditionally slow-moving industry. Modernizing their software stack was critical to Newfront's mission of improving insurance access and utilization. 

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As Joe Luo, Director of Engineering, explained, “We work in a very tech-heavy and language-heavy industry, and managing a complex codebase across multiple languages was a real challenge. We needed a solution that could help streamline our development process and ensure our engineers were focusing on critical tasks, not just routine coding.”

Why Augment?  

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• Multi-Language Codebase Understanding: Augment’s ability to support Newfront’s complex tech stack was critical. This included Python for AI projects and TypeScript for front-end development."We needed a tool that could help engineers work across different repos with different languages seamlessly." 

• Large Context Window: Augment’s deep understanding of multi-file context sets it apart from its competitors. "It’s a game-changer to have an AI assistant that understands dependencies and the entire codebase, not just one file."

• Increased Productivity and Developer Confidence: With Augment’s low-latency suggestions and deep understanding of context, Newfront’s engineers could trust the code completions and refactoring suggestions. This increased confidence in the tool and sped up development processes. "Developers can ask Augment questions without feeling self-conscious, helping them understand the codebase more quickly."

The Results  

Newfront has seen clear improvements in developer efficiency since adopting Augment. Code reviews are faster, and refactoring is more streamlined. With Augment, the team could focus on complex engineering challenges rather than routine tasks. 

“We’ve seen improvements in our DORA metrics, particularly around pull request completion speed and code merge times” Joe shared. “It’s also been extremely helpful in onboarding new engineers, making it easier for them to get up to speed on our codebase.”

Key Benefits  

• Accelerated onboarding: Augment helped new engineers get up to speed quickly by providing code suggestions and context-based guidance. “New engineers can ask Augment questions and get immediate, accurate responses, helping them onboard much faster.”

• Improved collaboration: Augment created a shared understanding of the codebase, bridging the gap between software engineers and data scientists. “Augment allowed our data scientists and engineers to collaborate more seamlessly on production-level AI deployments.”

• Increased code quality: Augment’s code completions led to cleaner, more maintainable code, reducing errors and improving long-term stability. “We're producing more reliable code with better structure, which is crucial for our AI-driven applications.” 

About Newfront  

Newfront is reshaping the insurance industry by using technology to enhance business insurance services. As a tech-enabled brokerage, they integrate AI and modern software to empower their team and clients, providing streamlined access to essential insurance resources.

Transform Your Development Process  

Ready to optimize your development workflows like Newfront? Join our growing waitlist today and experience the power of Augment.

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Industry: Insurance

Location: San Francisco, California

Team: 50

Languages, Frameworks, Libraries: Typescript, Python, React, 

IDE: VSCode, IntelliJ