Quality engineering and cloud architecture for regulated industries
Keeggo is a São Paulo-based consulting firm founded in 1993 with deep expertise in quality assurance, security, and observability across cloud platforms (AWS, Azure, GCP). The tech stack reveals a mature testing and automation culture—Selenium, Cypress, Playwright, REST Assured, Cucumber—paired with modern data infrastructure (Databricks, Spark, Kafka). Current hiring is heavily senior-focused (7 of 8 open roles) in engineering, suggesting either a scaling push for complex client work or backfill for specialized domain expertise; notably, the firm is adopting agentic AI frameworks (LangChain, CrewAI, AutoGen) while tackling government accessibility compliance and trade finance regulatory challenges.
Keeggo provides technology consulting for mid-to-large enterprises navigating quality assurance, security, privacy, and observability requirements. The firm operates across three main service lines: Engenharia de Qualidade (quality engineering and automation), Segurança & Privacidade (security and LGPD compliance), and Ambiente & Observabilidade (cloud infrastructure and observability). With over 30 years in market, Keeggo serves clients in highly regulated sectors—government, financial services, trade finance—where compliance, performance, and release reliability are non-negotiable. The workforce spans 501–1,000 employees, primarily based in Brazil.
Keeggo uses Jira, Postman, Python, Java (Spring Boot, Quarkus), AWS/Azure/GCP, Kafka, Docker, GitLab CI/CD, and testing frameworks: Selenium, Cucumber, Cypress, Playwright, Appium, REST Assured. The firm is adopting LangChain, CrewAI, and AutoGen for AI-driven automation.
Primary active project is a government digital accessibility initiative. Core pain points include accessibility compliance, trade finance regulatory requirements, quality-to-DevOps integration, test automation, and reducing release lead times and performance bottlenecks.
Keeggo's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.