Autoheal raises $7.9M seed for self-improving AI software factory
Autoheal has raised $7.9 million in seed funding to expand its platform for deploying, governing and improving AI agents across software engineering workflows. The round was led by Innovation Endeavors, with Harpinder Singh joining Autoheal's board.
Emergent Ventures, U&I Ventures, Darkmode Ventures, Batch Ventures' CTO Fund and Param Hansa Values also participated. Angel investors Shawn Kung, Sumeet Arora, Anshu Sharma, Savin Goyal and Srikant Gokulnatha joined the round. The company did not disclose its valuation.
Autoheal describes its platform as a self-improving software factory that connects coding agents with code repositories, CI/CD pipelines, observability tools, cloud runtimes and issue trackers.
These systems feed into an engineering context graph, giving agents shared information about an organisation's software environment.
Two specialised agents, the Evaluator and the Healer, sit at the core of the system. The Evaluator scores worker-agent performance using signals such as pull requests, review comments, CI failures and production incidents.
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The Healer can then propose changes to prompts, tools, skills or model selection. Every change is tested against historical runs, version-controlled in Git and requires engineer approval before deployment.
The startup grew out of its founders' experience at Harness, Microsoft Azure, ThoughtSpot and AppDynamics. Nomura Bank and AvidXchange are already using the technology for alert triage and production incident response.
Sid Choudhury, co-founder and CEO of Autoheal, said scaling AI agents consistently across the enterprise SDLC is the real challenge. "Our experience taught us that while building the first version of an AI agent is easy, scaling it consistently across the enterprise SDLC is the real challenge," he said.
Sameer Jain, CIO, Wholesale at Nomura Bank, reported that Autoheal had reduced investigation timelines "from hours to minutes" while operating within the bank's own cloud and controls.
The company plans to build enterprise-specific small language models trained on each client's private software development lifecycle data. Over the longer term, it intends to extend its architecture beyond software engineering into data and security engineering.
For global partners and investors watching India's enterprise AI space, Autoheal signals a shift from single-point coding copilots toward orchestration layers that govern multiple agents across an entire engineering organisation.
The early traction with a regulated financial institution like Nomura suggests the architecture can meet stringent compliance requirements, a threshold that narrows the competitive field considerably.
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