How Litmos Accelerated Third-Party Integration Development with DevClarity

Litmos is an AI-powered learning platform built for speed, scale, and measurable impact, supporting millions of learners across a global workforce and customer ecosystem. As a modern, cloud-based platform, Litmos enables organizations to deliver employee, partner, and customer training at scale – while maintaining the flexibility required to integrate with a growing ecosystem of third-party systems.

Litmos supports over 25 million users across 150 countries, working with thousands of organizations worldwide. As customer, partner, and external training use cases expanded, third-party integrations became a critical growth driver.

Building integrations at scale introduces inherent complexity. Each integration requires deep API analysis, precise field mapping between systems, and comprehensive testing across multiple layers of the platform. Without standardized patterns, integration teams often re-create similar workflows for each new system, slowing development velocity and limiting how quickly new integrations can be delivered.

Litmos needed a structured, AI-native approach to integration development that would standardize workflows, preserve quality, and increase speed – enabling the engineering team to scale integrations intentionally without compromising reliability.

DevClarity embedded with the Litmos engineering team over the course of several months to work alongside the team to refine and formalize a comprehensive, AI-native integration playbook. The engagement focused on standardizing both integration development and testing through reusable workflows, shared context, and documented patterns.

The approach included:

• • AI-native coding foundations: The engineering team aligned on shared AI-coding principles and workflows tailored to Litmos' architecture, establishing a consistent foundation for AI-assisted development.
• • Standardized integration development workflows: The team formalized an integration-writer workflow with structured steps for data identification, field mapping, and API implementation. Engineers standardized how critical context, such as API specifications and framework documentation, was incorporated into development.
• • Repeatable integration testing patterns: The team standardized test creation using Claude Code slash commands and adopted Arrange-Act-Assert testing patterns across the entire integration testing harness. Workflows covered API calls for test data setup, Playwright page objects and tests, and database queries, ensuring consistent coverage and reliability across integrations.

By the end of the engagement, Litmos engineers were combining comprehensive context files with focused prompts, breaking large initiatives into smaller, reviewable units, and storing all workflows and artifacts directly in the repository for ongoing use.

The impact was immediate – both in performance metrics and day-to-day workflows. 58% velocity increase on repetitive tasks such as boilerplate code and field mapping with a 30% increase in overall weekly output across the engineering team.

More importantly, the integration workflows themselves were transformed:

• • The team operates with repeatable, documented patterns for third-party integration development and testing
• • Developers use AI intentionally to identify API endpoints, create field mappings, and perform transformations
• • Test writers use standardized workflows across the entire integration testing harness, with reusable slash commands and Arrange-Act-Assert patterns ensuring consistent test coverage
• • Integration prompts, context files, and testing commands are versioned and maintained in the team repository for ongoing and future use

Litmos emerged with a systematic, AI-native integration development process, including standardized workflows, shared context files, testing patterns, and reusable artifacts – fully owned by the engineering team.

The result: faster development, higher output, and a scalable integration playbook that supports Litmos' continued growth across its global ecosystem.