Decimal AI raised $4 million in seed funding to build what it calls a Customer Engineering Platform - an AI support engineer that investigates code, logs, and production data to resolve technical issues directly. The round, co-led by Khosla Ventures and Kearny Jackson, signals growing investor appetite for tools that reduce the escalating cost of software support by giving customer-facing teams engineering-level diagnostic capabilities.
The platform surfaced from a problem founders Sanjeet Hajarnis and Kevin Raji Cherian saw repeatedly: support escalations rely on secondhand descriptions of what a product is doing, rather than evidence from the product itself. Their system pulls data from the actual codebase and production environment to answer questions during live support conversations, cutting out the back-and-forth between support agents and engineering teams.
Who built it and where the money is coming from
Hajarnis previously led AI at Eightfold AI and worked on Facebook's News Feed ranking systems. Raji Cherian built Databricks' vector search product and led infrastructure at Eightfold, where the two met. The seed round includes Atlassian Ventures, Weekend Fund, and individual backers like former Stripe COO Claire Johnson.
Vinod Khosla said the platform addresses "the rising hidden costs of software support by combining AI with deep code and customer context." Kearny Jackson's Sriram Krishnan went further, predicting that customer engineering would emerge as its own technology category - a distinct space separate from traditional helpdesk software or internal developer tools.
Early traction points to faster resolution times
Since launching in March 2025, Decimal has been adopted by companies including Granola, Resilinc, Tealium, BuildOps, and Lucidworks. Support interaction volume on the platform grew 15-fold between early 2026 and the announcement. Resilinc reported a 62% reduction in resolution times, while Granola said it now handles double its previous ticket volume without adding headcount.
For technical support teams, the shift matters because it changes the economics of escalation. When an AI support engineer can check logs and code during the first customer interaction, fewer tickets need to reach engineering. That means faster answers for customers and less context-switching for developers who would otherwise be pulled into support queues.
Why this matters for customer support professionals
Decimal's approach points toward a support role that requires less triage coordination and more direct technical investigation. If the platform works as described, support professionals will spend less time describing problems to engineering and more time resolving them - using evidence pulled straight from the product's behavior. For teams evaluating AI for Customer Support, the funding round and early customer results suggest that tools bridging support and engineering workflows are attracting serious investment. Professionals looking to build skills in this area can explore the AI Learning Path for Technical Support Specialists, which covers AI helpdesk automation and diagnostic techniques relevant to this evolving role.
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