List of Cognition Devin Customers
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Since 2010, our global team of researchers has been studying Cognition Devin customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Cognition Devin for Generative AI Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Cognition Devin for Generative AI Platforms include: Goldman Sachs, a United States based Banking and Financial Services organisation with 48300 employees and revenues of $53.51 billion, Nubank, a Brazil based Banking and Financial Services organisation with 8716 employees and revenues of $8.27 billion, Crossmint, a United States based Professional Services organisation with 75 employees and revenues of $8.0 million and many others.
Contact us if you need a completed and verified list of companies using Cognition Devin, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Crossmint | Professional Services | 75 | $8M | United States | Cognition AI | Cognition Devin | Generative AI Platforms | 2025 | n/a |
In 2025, Crossmint ran a January 2025 trial of Cognition Devin as part of Cognition AI’s Open Source Initiative to scale contributions to its GOAT SDK. Cognition Devin is a Generative AI Platforms implementation used in the developer and engineering area within the United States, focused on automation of code contribution workflows and repository-level development tasks. The engagement targeted open-source contribution pipelines rather than broader enterprise IT systems.
During the trial Cognition Devin operated as an engineering automation contributor and open-source contributor to the GOAT SDK, producing automated pull request activity and merge actions that aligned with developer workflows. Cognition Devin created and submitted change sets that were reviewed through the project’s existing code review process, and it became the top contributor by PR count in the trial. The record from the initial run shows Cognition Devin merged 8 PRs, demonstrating the application’s role in automating repetitive engineering tasks and accelerating contribution throughput.
The implementation emphasized repository-level integration and developer workflow orchestration, with Devin merging multiple integrations into the GOAT SDK codebase and interacting with source control and pull request procedures used by Crossmint engineers. Governance for the trial remained within the open-source initiative framework, preserving standard review and merge controls while instrumenting an automated contributor role. Measured outcomes from the case study include Devin’s 8 merged PRs and its status as the top contributor by PR count, which the customer presented as evidence of developer productivity gains from using Cognition Devin.
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Goldman Sachs | Banking and Financial Services | 48300 | $53.5B | United States | Cognition AI | Cognition Devin | Generative AI Platforms | 2025 | n/a |
In 2025, Goldman Sachs piloted Cognition Devin, a Generative AI Platforms application, to augment its software engineering organization. The pilot used the Cognition Devin AI agent to automate code migrations and routine developer tasks within the firm’s finance and IT teams in the United States.
The deployment emphasis was on agent-driven automation for code migration and repetitive developer workflows, leveraging generative coding capabilities such as code synthesis, automated refactoring, test scaffolding, and scripted task orchestration that are typical of Generative AI Platforms. Configuration work focused on embedding the agent into developer workflows and enabling supervised execution of migration tasks to preserve engineer oversight.
Operational coverage during the pilot was confined to software engineering in the finance and IT areas in the United States, with plans to scale to hundreds and potentially thousands of instances across teams. The rollout model described is instance-based, provisioning multiple supervised AI agents to run alongside human engineers and interact with existing development toolchains and automation pipelines.
Governance emphasized human-in-the-loop supervision, with human engineers retained as controllers of final code decisions and review. The pilot expected a productivity uplift of roughly 3–4x for developers while maintaining supervised operations, and planned scaling implies staged rollouts and oversight processes to manage adoption and risk.
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Nubank | Banking and Financial Services | 8716 | $8.3B | Brazil | Cognition AI | Cognition Devin | Generative AI Platforms | 2024 | n/a |
In 2024, Nubank deployed Cognition Devin to accelerate a large ETL codebase migration. Cognition Devin is a Generative AI Platforms application used to orchestrate agent based code refactoring and automation within data engineering workflows.
Implementation centered on delegating repetitive refactor subtasks to the Devin agent, allowing programmatic transformation of ETL scripts and pipeline code as part of custom ETL migration workflows described in Cognition Devin customer materials. The project architecture positioned Cognition Devin as an autonomous code assistant that prepared and applied refactor patches while human engineers retained review and approval authority. Work was focused on a large, centralized ETL codebase managed by the data engineering team.
Operational scope was a data engineering migration project in Brazil, with human engineers supervising and reviewing all Cognition Devin generated changes before merge. The engagement model combined automated agent outputs with human supervised code review, enabling parallelization of repetitive migration subtasks without altering core governance roles. No external system integrations were specified in the customer description beyond embedding Cognition Devin into ETL migration workflows.
The engagement began after Devin became available in 2024 and reported engineering time efficiency gains of 8 to 12 times and approximately 20 times cost savings, with migrations delivered in weeks instead of months. Outcomes and metrics were reported by Nubank and described in Cognition Devin customer materials, while risk controls relied on human review gates and supervised rollout of agent changes. This implementation illustrates Cognition Devin in the Generative AI Platforms category applied to data engineering, ETL refactor automation, and engineering workflow acceleration.
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