Decimal AI raised a $4 million seed round to expand its platform that gives technical support teams direct access to the code, logs, and production data needed to resolve complex customer issues. The funding, co-led by Khosla Ventures and Kearny Jackson with participation from Atlassian Ventures and Weekend Fund, signals growing investment in tools that reduce the costly handoffs between support and engineering.
The company also introduced its Customer Engineering Platform, which embeds an AI Support Engineer directly into the support workflow. The system investigates problems using the full technical context of a customer's environment and can even propose bug fixes for engineering review.
Moving past chatbot deflection to technical investigation
Most AI support tools focus on deflecting common questions by searching knowledge bases. Decimal targets the harder cases where the answer depends on what actually happened inside the software. That means examining production logs, account configurations, code behavior, and historical interactions - work that traditionally requires escalation to a developer.
"The answer to customer questions is usually sitting in what the product actually did," said Sanjeet Hajarnis, co-founder and CEO of Decimal AI. "We built Decimal to surface that evidence the moment a ticket arrives, not after a customer has waited through many escalation cycles."
The platform has gained early traction since its March 2025 launch. Customers include Granola, Resilinc, Tealium, BuildOps, Lucidworks, and an unnamed Fortune 5 technology company. Since the beginning of 2026, the volume of support interactions resolved by Decimal has grown 15-fold.
Measurable impact on resolution times
Supply-chain risk management firm Resilinc cut its mean time to resolution from 6.5 days to 2.5 days, a 62% reduction. Granola doubled its ticket-handling capacity while resolving roughly 70% of common questions through chat before they become tickets. The company also uses Decimal to automate investigations and generate pull requests that improve documentation.
These results reflect a broader shift in enterprise support. As AI enables software teams to ship faster, the volume and complexity of customer interactions climb. Support organizations need more than scripted responses - they need tools that can read the product itself.
Vinod Khosla, founder of Khosla Ventures, framed the economics directly: "Support is becoming one of software's biggest hidden costs. Decimal changes those economics entirely with an AI platform that understands the code and customer context as well as the best support engineers."
Founder expertise from Facebook, Uber, and Databricks
Hajarnis and CTO Kevin Raji Cherian previously worked together at Eightfold AI. Hajarnis led AI initiatives as Eightfold grew past $100 million in revenue. Earlier, he worked as an early engineer on Facebook's News Feed ranking systems and built pricing technology at Uber. Raji Cherian built Databricks' vector search product and led infrastructure at Eightfold supporting matching across more than 1 billion candidate profiles.
That background in large-scale AI and infrastructure shaped Decimal's approach. Rather than requiring support reps to manually summarize problems for developers, the platform investigates the underlying system and presents evidence of what occurred.
Sriram Krishnan, co-founder and general partner at Kearny Jackson, compared the opportunity to a previous category shift: "Every software company with a technical problem is going to need this. We think Customer Engineering becomes its own category, the way GTM Engineering did."
Why this matters for customer support professionals
Decimal's model changes the support role from triage and escalation to investigation and resolution. When an AI system can examine logs and code the moment a ticket arrives, support teams spend less time describing problems to engineers and more time solving them. For professionals building skills in AI for Customer Support, the shift points toward a future where technical fluency with AI investigation tools becomes a core competency, not a specialization.
The platform does not replace engineering judgment - proposed fixes still go through code review. But it removes the bottleneck where support teams lack the system access to diagnose issues independently. As software complexity grows, the ability to work alongside an AI that understands the product's internals could separate teams that resolve issues in hours from those that take days.
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