Cognition Deploys Devin for Enterprise Scale Database and Monolith Migrations

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Cognition released case studies from AngelList and Nubank showing its Devin AI agent completing large-scale architectural migrations up to 12x faster than human teams. By running dozens of agents in parallel and using fine-tuning to optimize for specific codebases, the autonomous engineer is moving beyond simple bug fixes to handle massive technical debt.

Cognition, an applied AI lab building software agents, released data from enterprise deployments of Devin, its autonomous AI engineer. At AngelList, Devin migrated 14,000 dashboards between Redshift and Snowflake 5.2x faster than projected. Nubank used the agent to refactor a 6-million-line monolith, achieving a 12x efficiency gain.
AngelList migration speed
5.2x faster
Nubank efficiency gain
12x engineering hours
Nubank cost savings
20x reduction
Parallel agent capacity
20 agents
Fine-tuning speed gain
4x improvement
Fine-tuning completion gain
2x improvement

These results validate the shift toward Devin's parallel agent coordination for high-stakes infrastructure projects. By demonstrating that agents can propose improved architectures and Devin's legacy modernization capabilities at a 20x lower cost, Cognition is positioning autonomous engineering as a solution for structural technical debt.

You can now deploy Devin to handle repetitive refactoring tasks that were previously too complex to script. The Nubank study highlights that fine-tuning (adapting a model to a specific domain) doubled Devin's task completion scores and quadrupled its speed. Devin is available via web or terminal with enterprise-grade security features.

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Beau Rothrock had been at @AngelList for two months when he walked into a Redshift-to-Snowflake migration in deep trouble, already two months behind schedule. He had a 5-week window to migrate all 14,000 dashboards and reports AngelList runs on. He could've asked for three more engineers and four more months. Instead, he turned to Devin.

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Still wondering? A few quick answers below.

Devin is an autonomous AI software engineer that can plan and execute complex engineering tasks using its own browser, terminal, and code editor. It functions as a teammate that can navigate entire repositories, build its own scripts, and coordinate multiple agents in parallel to complete large-scale projects like database migrations or monolith refactoring.

AngelList used Devin to migrate 14,000 Metabase dashboards from Redshift to Snowflake. The agent proposed a modular architecture of five specialized tools rather than a single monolithic script. By running 20 Devin agents in parallel, the team completed the migration 5.2x faster than projected, finishing the project in just three weeks instead of several months.

Nubank deployed Devin to refactor a monolithic repository—a single, massive codebase—containing over 6 million lines of code into smaller sub-modules. By delegating repetitive refactoring tasks to the agent, the company achieved a 12x improvement in engineering hour efficiency and a 20x reduction in costs. The project was completed in weeks.

Yes, Devin supports fine-tuning on domain-specific data to improve performance on specialized tasks. In the Nubank deployment, fine-tuning the agent on examples of previous manual migrations doubled its task completion scores and quadrupled its speed. This allowed the agent to handle complex variations and edge cases more reliably than a generic model.

Devin is available through Cognition for both individual developers and large organizations. The Devin Enterprise tier provides additional security, control, and management features specifically designed for engineering teams. Users can sign up for the standard version or contact the company directly to integrate Devin into enterprise-scale workflows and high-stakes infrastructure projects.

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