As a cloud project tracking software pir.comAs the engineering organization scaled past 500 developers, the team began to feel the strain of its success. Product lines were multiplying, microservices were proliferating, and code was flowing faster than human reviewers could keep up. The company needed a way to review thousands of pull requests each month without inundating developers with tedium.
That’s when Guy Reggio, VP of R&D and head of Nimo and Peer’s lead teams, began experimenting with a new AI tool. Qodoan Israeli startup focused on developer agents. What started as a lightweight test soon became a critical part of Peer.com’s software delivery infrastructure, as a new case study released today by both QDO and Peer.com shows.
“Quodo doesn’t feel like just another tool – it’s like adding a new developer to the team who actually learns how we work," Reggio told VentureBeat in a recent video call interview, adding that he has "Prevented more than 800 issues from reaching production every month – many of them could cause serious security risks."
Unlike code generation tools like GitHub CoPilot or Cursor, Quodo isn’t trying to write new code. Instead, it specializes in evaluating it. Context engineering To understand not only what changed in the bridge application, but why, how it aligns with business logic, and whether it follows internal best practices.
"You can call CloudCode or Cursor and get 1,000 lines of code in five minutes," In the same video call interview with Regio, Quodo co-founder and CEO, Atar Friedman said. "You have 40 minutes, and you can’t review it. So you need a Qodo to actually review it.
For Peer.com, this capability wasn’t just helpful — it was transformative.
Code review, at scale
At any given time, Peer.com developers are sending updates to hundreds of repositories and services. The engineering org works in tightly integrated teams, each aligned with specific areas of the product: marketing, CRM, dev tools, internal platforms and more.
At that place came Qudu. The company’s platform uses AI not only to check for obvious bugs or style violations, but to evaluate whether a bridge application follows the team’s specific conventions, architectural guidelines, and historical patterns.
It does this by learning from your own codebase – previous PRs, comments, integrations, and even training Slack threads to understand how your team works.
"The comments that Qodo gives are not generic – they reflect our values, our libraries, even our standards for things like feature flags and privacy." Reggio said. "It is context-aware in a way that traditional tools are not."
What does “context engineering” actually mean?
Kudo has what he calls his secret sauce Context engineering -System-level approach to handle all aspects of the model when making decisions.
This of course includes the PR code difference, but also previous discussions, documentation, files related to the REPO, even test results and configuration data.
The idea is that language models don’t really “think” – they predict the next token they have based on the input. Hence the quality of their output depends almost entirely on the quality and composition of their inputs.
As Dana Fine, Qodo’s Community Manager, puts it in one Blog post: “You’re not just writing notation. You’re designing structural input under a fixed token limit. Each token is a design decision.”
This is not just theory. In the case of Peer.com, this meant that Qodo could catch not only obvious bugs, but subtle ones that would normally slip past human reviewers—hard-coded variables, missing fallbacks, or violations of cross-team architecture conventions.
An example came up. In a recent PR, Kudo flagged a line that inadvertently exposed a staging environment variable – something that no human reviewer caught. If it were to be merged, it could cause production problems.
"The hours we will spend fixing this security leak and the legal issues it will bring will far outweigh the hours we save from the pull request." Reggio said.
Integration into the pipeline
Today, Qodo is deeply integrated into Peer.com’s development workflow, analyzing pull requests and surfacing context-aware recommendations based on previous reviews of the team’s code.
“It doesn’t feel like just another tool … it feels like another teammate who joins the system — who learns how we work," Reggio noted.
Developers receive suggestions during the review process and control the final decisions. This is a human loop model that was important to adopt.
Because QoDO integrated directly into GitHub via pull request actions and comments, Peer.com’s infrastructure team didn’t face a steep learning curve.
“It’s just a GitHub action,” Reggio said. “It generates a PR with tests. It’s not like a separate tool that we had to learn.”
“The goal is to actually get the developer to learn the code, take ownership, give each other feedback, and learn from it and set standards and set standards," Added Friedman.
Results: Saves time, prevents bugs
After rolling out QODO more broadly, Peer.com has seen measurable improvements across multiple teams.
Internal analysis shows that developers save about an hour on average per pull request. Multiply that by thousands of PRs per month, and the savings quickly add up to thousands of developer hours annually.
These aren’t just cosmetic issues—many are related to business logic, security, or runtime stability. And because Quodo’s suggestions reflect Pir.com’s original conventions, developers are more likely to follow them.
The validity of the system lies in its data-first design. Quodo trains on each company’s private codebase and historical data, which accommodates different team styles and practices. It does not rely on one-size-fits-all rules or external datasets. According to everything.
From internal tool to product vision
Reggio’s team was so impressed with Qodo’s impact that they began planning a deep integration between Qodo and PeerDev, a developer-oriented product line Peer.com is ready for.
The vision is to create a workflow where business context – tasks, tickets, customer feedback – flows directly into the code review layer. That way, reviewers can’t judge whether the code “works”, but whether it solves the right problem.
“Before, we had linters, risk rules, static analysis … rule-based … you need to configure all the rules," Reggio said. "But it doesn’t know what you don’t know…quodo…feels like it’s learning from our engineers.
This aligns closely with Qodo’s own roadmap. The company doesn’t just review code. It is building a complete platform of developer agents—including Quodo General for context-aware code generation, Quodo Integration for automated PR analysis, and a regression testing agent, Quodo Core, that uses runtime validation to ensure test coverage.
All of this is driven by Qodo’s own infrastructure, including its new open-source embedding model, Embed-1-1.5b from Qodo, which leverages offerings on the code retrieval standard from OpenAIA and Salesforce.
What’s next?
Qodo is now offering its platform under a freemium model—free for individuals, discounted for startups through Google Cloud’s Perks program, and enterprise-grade for companies that need SSO, over-the-air deployments, or advanced controls.
The company is already working with teams at NVIDIA, INTUIT, and other Fortune 500 companies. And thanks to a recent partnership with Google Cloud, Quodo’s models are available directly within Vertex AI’s Model Garden, making it easy to integrate into enterprise pipelines.
"Contextual engines will be the big story of 2026," Friedman said. "Every enterprise will need to create its own second brain if they want AI that actually supports and supports them."
As AI systems become more embedded in software development, tools like Qodo are showing how the right context — provided at the right moment — is how teams build, ship, and scale code across the enterprise.