Autoheal raises $7.9M to build the self-improving software factory for enterprises
AI coding agents accelerated feature delivery, shifting the bottleneck to keeping software reliable, secure, cost-efficient, and supportable after the commit. Autoheal is a self-improving software factory for removing that bottleneck: one platform where enterprise engineering teams build, run, govern and continuously improve worker agents for incident response, vulnerability remediation and AI coding cost efficiency. Every worker also enhances existing coding agents’ context for inner-loop tasks.
Today, we are announcing our $7.9M seed round, led by Harpinder Singh at Innovation Endeavors, with participation from Emergent Ventures, U&I Ventures, Darkmode Ventures, Batch Ventures (CTO Fund) and Param Hansa Values. Joining them as angel investors are industry leaders Shawn Kung, Founder of GIT100; Sumeet Arora, Chief Product Officer of Teradata; Anshu Sharma, Co-Founder & CEO of Skyflow; Savin Goyal, Co-Founder & CTO of Outerbounds; and Srikant Gokulnatha, former SVP at ThoughtSpot. We are grateful to our investors for backing our team and vision so early.
Our team previously built and scaled the DevOps platform at Harness. That experience shaped our approach to extensibility, reliability, ecosystem and enterprise control.
Trusted by Nomura Bank and AvidXchange
Incident response and alert triage were the first agents we built on the factory. At Nomura Bank, they gather evidence from multiple systems and work within strict access controls.
“Our production operations teams spend valuable time triaging alerts and managing incidents, while also pulling engineers away from their software development activities. Autoheal gives us a platform that takes investigation timelines down from hours to minutes. The fact that it runs entirely within our own cloud, in compliance with our controls, made it a natural fit for how we operate.”
— Sameer Jain, CIO, Wholesale at Nomura Bank
At AvidXchange, Autoheal took time to root cause down to minutes, and the team is now expanding it across the software lifecycle:
“In production incident response, Autoheal took our time to root cause to minutes, with evidence our engineers trust. That's time our developers stay focused on feature work. Next, we're shifting it left into other critical parts of our SDLC, because every engineering hour we get back goes into shipping faster for our customers.”
— Krish Shetty, CTO & SVP at AvidXchange
The problem
Today, engineers bridge the gap by adding a few skills to a coding agent and running it from their local laptop when needed. That works until the engineer is unavailable, doesn't know what to ask, or the skills go stale as systems change. Coding agents depend on who prompts them and on the frontier model behind them. That is the right design for novel feature work and the wrong one for repetitive operational work that has to produce consistent outputs every time.
Enterprises need to turn individual expertise into a shared, continuously improving capability, so every engineer benefits from the practices of the strongest ones and agents work 24x7 in a secure, governed environment.
What is a software factory
A software factory turns ad-hoc agent workflows into a shared cloud-based system that runs in the background autonomously instead of on individual laptops. The figure below highlights the key components of Autoheal’s factory.
Specifically, it has two planes:
Context plane: what every agent reads before it acts, and what Autoheal improves. A context graph links your repositories, services, tickets and releases. It also includes a skill and memory registry that holds the skills, AGENTS.md files and memories agents read for each task.
Control plane: how every agent runs. Agent definitions as code, budgets, model selection, identity, roles and approval policy live in one governance layer. A tool gateway registers, scopes and audits every integration once, for every agent. Agents escalate to DevEx, DevOps or SRE teams for input or approval when a step calls for it.
Autoheal doesn't manage or replace the coding agents your developers already use. What it changes is the context they read, so an improvement made for one agent reaches every agent, coding agents included.
Getting the first agent running
Connect Autoheal to your code repositories, CI/CD pipelines, observability tools, cloud runtimes, issue trackers and coding agents, and the factory builds the context graph every agent works from.
Next, pick the post-coding workflow with the most manual toil, usually a customer-impacting one like incident response or vulnerability remediation, and deploy an Autoheal agent to automate it. With integrations in place, the first agent runs in minutes and can be rolled out to a team in hours. Broader rollout follows your enterprise's access and deployment requirements.
The factory's self-improvement loop
Deploying an agent is only step one. As codebases and production architecture shift, APIs evolve and new tools arrive, agents drift. Keeping them accurate and cost-efficient takes continuous evaluation and adaptation, and two agents do that work.
The Evaluator scores every run. In the SDLC, one agent's outcome is another's input, so every triage or remediation can be scored by what happened downstream: review comments, CI re-runs, fired alerts, the incident's actual root cause. The Evaluator scores each run's trajectory and outcome against rubrics generated from those signals, continuously, on your own data inside your own security boundary.
The Healer fixes what the Evaluator finds. It turns weak scores into specific context changes, such as an updated skill, a correction to an AGENTS.md file, a new memory, a narrower tool scope or a different model, and opens a pull request for each. Every change is back-tested against your run history and promoted only if it clears a blast-radius threshold.
Engineers stay in control. Every change is version-controlled in git and needs an engineer's approval to go live. You review it like any other pull request.
Because the context is shared, the improvements compound: the next coding agent that reads it produces software that is more reliable, more secure and cheaper to run. The goal is higher accuracy, faster execution and lower cost per successful task. As agents prove reliable, engineers can expand their autonomy.
Built for regulated environments
Enterprises control where agents run, what they can access and what they can change.
Your boundary. Deploy in your own cloud (BYOC) or fully airgapped, with data sovereignty controls.
Least privilege by default. Read-only access by default, fine-grained policies and an audit trail of every tool call.
Private evaluations. Agents are scored on your own runs and outcomes, inside your boundary. No other customer's data shapes them.
Model intelligence. Use frontier models for complex reasoning, and lower-cost or open-weight models where evaluations show they perform well.
What's next
Our long-term vision is for engineering teams to set outcome targets for quality, security, reliability and cost, then govern factory agents that work 24x7 toward those targets. Continuous evaluation and healing keep agents effective as the software evolves, with engineers approving governance policy exceptions.
To get there, we are building the foundation of sovereign engineering intelligence: enterprise-specific small language models trained with reinforcement learning on each company's private SDLC data, within its approved security boundary. We believe this is how AI becomes omnipresent across engineering at an acceptable cost while meeting enterprise requirements for data security and governance.
Two ways to start
Start with your sharpest operational pain. Alert triage and incident response, or vulnerability remediation: up to 80% lower MTTR and 3x faster vulnerability burndown.
Start with AI coding cost and context. An Autoheal agent owned by your DevEx team puts the spend and context your coding agents consume under budget and governance, without changing which coding agents developers use: up to 40% lower AI coding cost.
Either way, we run a three-week proof of value in your environment, with the workflow and success measures agreed at the start, so you see measured impact, not just a running agent. We measure against your baseline: time to a root cause supported by evidence, effort to produce a validated vulnerability fix, and cost per successful coding task.
Book a demo to choose your starting workflow.
