The sudden proliferation of AI-generated code has created a new class of problem for software companies: managing the volume and quality of code flowing through their systems. Reports have shown that AI-generated code introduces significant bugs and quality issues that must be fixed by senior engineers before shipping to production — a costly bottleneck that Gitar is now attempting to solve.
Gitar, founded by Ali-Reza Adl-Tabatabai (a veteran of Intel Labs, Google, and Uber), emerged from stealth Wednesday with a $9 million Series A led by Venrock, with participation from Sierra Ventures. The two-year-old San Mateo company operates a subscription platform that deploys AI agents to handle code-quality operations across the development lifecycle.
The platform automates code reviews, manages continuous integration workflows (the automated process of regularly merging and testing code changes), and enables teams to create custom agents for security and maintenance operations. According to Adl-Tabatabai, the problem is straightforward: AI-generated code means "more code to review, more tests to write, more CI failures to diagnose."
Gitar's approach centers on what the company calls "code validation" — ensuring that code generated by AI (or humans) is trustworthy and ready for production. "Generation produces code; validation makes it trustworthy. Gitar is the workflow agent that owns that process, orchestrating reviews, tests, and diagnostics end to end," Adl-Tabatabai told TechCrunch.
The company's longer-term vision is significant: Adl-Tabatabai sees human code review becoming a minimal part of the software development process. He describes Gitar's validation agent as capable of "automatically ensuring that your code is safe to ship, and involves humans only in exception cases." Human oversight would remain in the process initially — Adl-Tabatabai acknowledged there are "good reasons" for human review — but the goal is to minimize that involvement.
Gitar faces established competitors in the automated code-review space, but Adl-Tabatabai argues for a key distinction in focus. "Most of the market chased [code] generation. We didn't," he said. "Gitar is built around what happens after code is written." This positioning suggests the startup sees an underserved market among companies drowning in code quality issues rather than seeking to build code generation tools themselves.
The new funding will support hiring across engineering and product teams as Gitar works to scale its systems to serve enterprise customers at larger volumes.