Introduction
In the fast‑moving world of cloud‑based project tracking, monday.com has grown from a niche tool to a platform that powers the workflows of thousands of teams worldwide. As the engineering organization expanded past five hundred developers, the company faced a paradox that many scaling tech firms confront: the very success that fuels growth can become a bottleneck. Product lines multiplied, microservices proliferated, and the velocity of code changes outpaced the capacity of human reviewers. The result was a mounting backlog of pull requests, a risk of slipping bugs into production, and a developer experience that felt increasingly tedious.
Enter Qodo, an Israeli startup that has carved a niche around “developer agents” – AI systems that learn the habits, conventions, and context of a specific engineering team. By integrating Qodo’s context‑engineering capabilities into its continuous integration pipeline, monday.com discovered a way to keep pace with its own growth without sacrificing quality. The partnership, now documented in a joint case study, demonstrates how a focused AI tool can become a virtual teammate, catching subtle errors that would otherwise slip through human eyes and saving developers an average of an hour per pull request.
The story is more than a success metric; it is a blueprint for any organization that wants to scale code reviews, reduce technical debt, and maintain a high standard of security and reliability. By examining the mechanics of context engineering, the integration strategy, and the measurable outcomes, we can distill practical lessons that apply across industries.
Main Content
Code Review at Scale
At any given moment, monday.com’s developers are shipping updates across hundreds of repositories and services. The engineering organization is split into tightly coordinated squads, each responsible for distinct product areas such as marketing, CRM, developer tools, and internal platforms. This structure creates a complex web of interdependencies: a change in one microservice can ripple through dozens of downstream components.
Traditional static analysis tools and linters can flag syntax errors or enforce style guidelines, but they lack the nuanced understanding of a team’s architectural conventions or the business logic that underpins a feature. Qodo addresses this gap by training on the company’s own historical data – past pull requests, review comments, merge decisions, and even Slack conversations. This data‑driven approach allows the AI to learn the “language” of monday.com’s codebase, including preferred libraries, patterns for feature flags, and privacy safeguards.
When a new pull request arrives, Qodo parses the diff and consults the contextual knowledge it has built. It then generates a review that is not a generic checklist but a tailored set of observations that mirror the team’s own standards. For example, if a developer inadvertently hardcodes a staging environment variable, Qodo will flag it as a security risk, a nuance that a generic linter would miss. By surfacing such issues early, the tool prevents costly post‑deployment fixes and protects the company’s compliance posture.
What Context Engineering Means
The term “context engineering” refers to a systematic method of preparing the input that a language model receives before it makes a decision. Language models, at their core, predict the next token in a sequence; they do not possess intrinsic reasoning. Therefore, the quality of their output hinges entirely on the quality and structure of the input data.
Qodo’s approach involves assembling a rich, structured context that includes the code diff, relevant documentation, historical review comments, test results, and configuration files. This composite context is then fed to the model within a fixed token budget, ensuring that every token contributes meaningfully to the final recommendation. The result is a review that feels as if it were written by a senior engineer who has spent months on the project.
Dana Fine, Qodo’s community manager, emphasizes that designing this input is an art: “You’re not just writing prompts; you’re designing structured input under a fixed token limit. Every token is a design decision.” This meticulous curation is what differentiates Qodo from generic code‑generation tools like GitHub Copilot or Cursor, which focus on writing new code rather than understanding the existing codebase.
Integration into the Pipeline
One of the most compelling aspects of Qodo’s success at monday.com is the simplicity of its integration. Rather than requiring a separate dashboard or a steep learning curve, Qodo operates as a GitHub action that comments directly on pull requests. When a developer pushes a change, the action triggers Qodo’s analysis, and the AI’s feedback appears as inline comments or a summary in the pull request thread.
This seamless workflow preserves the human‑in‑the‑loop model that is essential for developer trust. While Qodo can identify potential issues and suggest improvements, the final decision remains in the hands of the engineering team. The AI’s role is to augment, not replace, human judgment. Developers can accept or reject suggestions, and the system learns from these interactions, continually refining its recommendations.
The integration also extends beyond code review. Qodo’s roadmap includes a suite of developer agents – Qodo Gen for context‑aware code generation, Qodo Merge for automated pull request analysis, and Qodo Cover for regression testing. These tools are built on Qodo’s proprietary embedding model, Qodo‑Embed‑1‑1.5B, which outperforms leading open‑source models on code retrieval benchmarks. By offering a unified platform, Qodo positions itself as a comprehensive solution for modern software development pipelines.
The Results
Since rolling out Qodo more broadly, monday.com has observed tangible improvements across multiple dimensions. Internal metrics indicate that developers save roughly an hour per pull request on average. When multiplied across thousands of pull requests each month, the cumulative savings reach several thousand developer hours annually – a significant cost reduction for a company of this scale.
Beyond time savings, the quality impact is profound. Qodo has prevented over 800 issues per month from reaching production, including potential security vulnerabilities that could have caused severe breaches. The AI’s context‑aware suggestions align closely with the team’s conventions, leading to higher acceptance rates compared to generic linting tools. This alignment also accelerates knowledge transfer: newer developers can learn the team’s coding style through the AI’s feedback, reducing onboarding time.
The success story at monday.com illustrates that context engineering is not a theoretical concept but a practical enabler of scalable, high‑quality software delivery. By embedding the AI within the existing workflow and training it on proprietary data, the company achieved a level of automation that feels natural to its engineers.
From Internal Tool to Product Vision
The positive outcomes have spurred monday.com’s engineering leadership to envision deeper collaboration with Qodo. The goal is to create a workflow where business context – tasks, tickets, customer feedback – flows directly into the code review layer. In such a system, reviewers would assess not only whether the code works but whether it addresses the right problem.
This vision aligns with Qodo’s broader mission to build a full platform of developer agents. The company’s roadmap includes features that integrate business context into code reviews, enabling a holistic view of the development lifecycle. By moving beyond rule‑based static analysis to a learning‑based, context‑aware approach, Qodo and monday.com aim to reduce the friction that often hampers rapid iteration.
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 organizations that require SSO, air‑gapped deployment, or advanced controls. The company is already collaborating with teams at NVIDIA, Intuit, and other Fortune 500 firms. A recent partnership with Google Cloud has made Qodo’s models available inside Vertex AI’s Model Garden, simplifying integration into enterprise pipelines.
According to Itamar Friedman, Qodo’s CEO, “Context engines will be the big story of 2026. Every enterprise will need to build its own second brain if it wants AI that actually understands and helps them.” As AI systems become more embedded in software development, tools like Qodo demonstrate how the right context, delivered at the right moment, can transform how teams build, ship, and scale code.
Conclusion
The partnership between monday.com and Qodo offers a compelling case study in how context‑engineering AI can resolve a classic scaling problem: keeping code quality high while accelerating delivery. By training on a company’s own historical data and integrating seamlessly into the pull‑request workflow, Qodo transforms the review process from a manual bottleneck into an intelligent assistant. The measurable outcomes – thousands of developer hours saved, hundreds of potential security issues prevented, and a higher rate of adoption among engineers – underscore the value of a tailored, context‑aware approach.
More broadly, the success of Qodo illustrates a shift in how enterprises view AI in software development. Rather than relying on generic tools that apply one‑size‑fits‑all rules, organizations are increasingly investing in systems that learn the unique language of their codebases. This shift promises not only faster delivery but also deeper alignment between technical implementation and business objectives.
Call to Action
If your organization is grappling with a growing volume of code reviews, technical debt, or security concerns, consider exploring context‑engineering solutions like Qodo. Start by assessing the volume of pull requests that your team currently reviews and identify the most common types of issues that slip through. Reach out to Qodo or similar vendors to understand how their models can be trained on your proprietary data. By investing in a context‑aware AI assistant, you can free your developers to focus on higher‑value work, reduce the risk of costly post‑deployment fixes, and create a culture of continuous improvement that scales with your product.