---
title: "Andela Case Study: Althea Adopts Claude Code Companywide"
description: "Andela says insurtech Althea reached 100% Claude Code adoption with an AI-native engineering workflow, speeding QA loops and improving code quality."
canonical_url: "https://liners.com/news/andela-althea-claude-code-ai-native-engineering-workflow"
markdown_url: "https://liners.com/news/andela-althea-claude-code-ai-native-engineering-workflow.md"
type: "article"
language: "en"
published_at: "2026-09-26T18:33:41.362Z"
updated_at: "2026-09-26T18:33:45.675Z"
---

# Andela Case Study: Althea Adopts Claude Code Companywide

Andela says insurtech Althea reached 100% Claude Code adoption with an AI-native engineering workflow, speeding QA loops and improving code quality.

## Breadcrumbs

- [News](/news)
- [Andela Case Study: Althea Adopts Claude Code Companywide](/news/andela-althea-claude-code-ai-native-engineering-workflow)

## Content

## In Short
Andela published a case study on September 25, 2026, about insurtech Althea building an AI-native engineering workflow using Claude Code. Andela says Althea reached 100% adoption of Claude Code across the company. The case study also reports faster QA cycles and stronger code review and security checks.

## What Happened
The case study says Althea worked with [Andela](/andela) AI Engineers and Anthropic’s Claude Code to redesign how its engineering team builds software.

AI-native engineering means AI tools are embedded across the full software lifecycle, not just used for one-off tasks. In this setup, Claude Code supports planning, implementation, code review, and quality assurance. Quality assurance, or QA, is the step where teams test software and report bugs before release.

Andela’s write-up highlights a common problem with AI-assisted development, context. Context is the background information a developer or AI tool needs to make good decisions, like product requirements, architecture choices, and prior discussions. Althea found that context was spread across pull requests, meeting notes, tickets, and documentation, which can cause AI tools to work with incomplete or outdated assumptions.

To address this, the workflow connects technical and product context so engineers, and the AI helping them, can make decisions with a clearer view of requirements and architecture.

Andela reports early results that include shorter QA feedback loops, less back-and-forth during testing, and better continuity between product decisions and engineering work. The company also claims improved velocity, meaning faster delivery of software, and better quality through AI-assisted code review and security checks.

## Why It Matters
For African startups and distributed teams, faster engineering cycles can translate into quicker releases and lower operating costs.

The bigger takeaway is about process design, not just tools. As more teams adopt coding copilots, the hardest part may be keeping “what we decided” consistent across docs, tickets, and code. Workflows that package and reuse that context could become a competitive advantage, especially for regulated sectors like insurance where audit trails and security reviews matter.

## Sources and products

- [andela.com](https://www.andela.com/newsroom/how-althea-is-building-an-ai-native-engineering-model-with-andela-anthropic)
- [Andela](/andela)

## Related pages

- [Expansion & Partnerships](/news)

## Access and citation

- [Canonical HTML page](https://liners.com/news/andela-althea-claude-code-ai-native-engineering-workflow)
- [Markdown route index](/sitemap.md)
- [Agent access guide](/llms.txt)