Enterprise AI and data modernization services for Fortune 500 companies
Kanerika delivers custom AI agents and data platform migrations for large enterprises, with a production-ready agent portfolio (FLIP, Karl, KlarityIQ, Klara) that addresses migration, analytics, document intelligence, and compliance. The stack shows heavy investment in modern cloud platforms—Databricks, Snowflake, Microsoft Fabric, Power BI—while actively replacing legacy tools like Cognos; combined with an engineering-heavy hiring mix (18 of 44 open roles) and ongoing projects around Cognos-to-Power BI migrations and RAG-based chatbots, this signals a company scaling both delivery capacity and internal tooling maturity.
Kanerika is a 201–500-person enterprise services firm headquartered in Austin with delivery centers across the US, India, and Singapore. Founded in 2015, the company focuses on three service lines: custom enterprise AI (agents, generative AI, machine learning), data modernization and migration (moving legacy stacks like Alteryx, Informatica, and Oracle onto Databricks, Snowflake, Microsoft Fabric, and Power BI), and end-to-end data lifecycle services including analytics, integration, governance, and RPA. The firm holds Microsoft Top 1% Fabric Partner status and Databricks partnership credentials, and maintains a 95% client retention rate backed by ISO 9001, 27001, 27701 certifications and SOC 2 compliance.
Kanerika's primary stack spans Databricks, Snowflake, Microsoft Fabric, Power BI, Python, Java, Scala, Kafka, Azure Event Hubs, and Delta Lake for data engineering and analytics, with Jira, Confluence, and Azure DevOps for delivery. The team also uses Tableau, SQL Server Reporting Services, Cognos, and VB.NET for legacy integration work.
Enterprise AI (custom agents, generative AI, ML solutions), data modernization and migration (legacy tool offboarding to Databricks, Snowflake, Fabric, Power BI), and data lifecycle services (analytics, integration, governance, RPA).
Kanerika Inc'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 →
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