As cloud venture monitoring software program monday.com’s engineering group scaled previous 500 builders, the group started to really feel the pressure of its personal success. Product strains have been multiplying, microservices proliferating, and code was flowing quicker than human reviewers might sustain. The corporate wanted a option to assessment hundreds of pull requests every month with out drowning builders in tedium — or letting high quality slip.
That’s when Man Regev, VP of R&D and head of the Development and monday Dev groups, began experimenting with a brand new AI instrument from Qodo, an Israeli startup targeted on developer brokers. What started as a light-weight take a look at quickly grew to become a vital a part of monday.com’s software program supply infrastructure, as a new case study launched by each Qodo and monday.com at the moment reveals.
“Qodo doesn’t really feel like simply one other instrument—it’s like including a brand new developer to the group who truly learns how we work,” Regev advised VentureBeat in a current video name interview, including that it has “prevented over 800 points monthly from reaching manufacturing—a few of them might have induced critical safety vulnerabilities.”
In contrast to code technology instruments like GitHub Copilot or Cursor, Qodo isn’t attempting to put in writing new code. As a substitute, it makes a speciality of reviewing it — utilizing what it calls context engineering to grasp not simply what modified in a pull request, however why, the way it aligns with enterprise logic, and whether or not it follows inside finest practices.
“You’ll be able to name Claude Code or Cursor and in 5 minutes get 1,000 strains of code,” mentioned Itamar Friedman, co-founder and CEO of Qodo, in the identical video name interview as with Regev. “You may have 40 minutes, and you may’t assessment that. So that you want Qodo to really assessment it.”
For monday.com, this functionality wasn’t simply useful — it was transformative.
Code Assessment, at Scale
At any given time, monday.com’s builders are transport updates throughout a whole bunch of repositories and companies. The engineering org works in tightly coordinated groups, every aligned with particular elements of the product: advertising, CRM, dev instruments, inside platforms, and extra.
That’s the place Qodo got here in. The corporate’s platform makes use of AI not simply to verify for apparent bugs or fashion violations, however to judge whether or not a pull request follows team-specific conventions, architectural tips, and historic patterns.
It does this by studying from your personal codebase — coaching on earlier PRs, feedback, merges, and even Slack threads to grasp how your group works.
“The feedback Qodo provides aren’t generic—they mirror our values, our libraries, even our requirements for issues like characteristic flags and privateness,” Regev mentioned. “It’s context-aware in a manner conventional instruments aren’t.”
What “Context Engineering” Truly Means
Qodo calls its secret sauce context engineering — a system-level method to managing every part the mannequin sees when making a call.
This contains the PR code diff, after all, but additionally prior discussions, documentation, related recordsdata from the repo, even take a look at outcomes and configuration knowledge.
The thought is that language fashions don’t actually “suppose” — they predict the subsequent token based mostly on the inputs they’re given. So the standard of their output relies upon nearly fully on the standard and construction of their inputs.
As Dana Superb, Qodo’s neighborhood supervisor, put it in a blog post: “You’re not simply writing prompts; you’re designing structured enter below a hard and fast token restrict. Each token is a design resolution.”
This isn’t simply principle. In monday.com’s case, it meant Qodo might catch not solely the plain bugs, however the refined ones that usually slip previous human reviewers — hardcoded variables, lacking fallbacks, or violations of cross-team structure conventions.
One instance stood out. In a current PR, Qodo flagged a line that inadvertently uncovered a staging setting variable — one thing no human reviewer caught. Had it been merged, it might need induced issues in manufacturing.
“The hours we’d spend on fixing this safety leak and the authorized concern that it will convey can be rather more than the hours that we scale back from a pull-request,” mentioned Regev.
Integration into the Pipeline
At this time, Qodo is deeply built-in into monday.com’s improvement workflow, analyzing pull requests and surfacing context-aware suggestions based mostly on prior group code evaluations.
“It doesn’t really feel like simply one other instrument… It seems like one other teammate that joined the system — one who learns how we work,” Regev famous.
Builders obtain solutions throughout the assessment course of and stay in charge of closing choices — a human-in-the-loop mannequin that was vital for adoption.
As a result of Qodo built-in immediately into GitHub by way of pull request actions and feedback, Monday.com’s infrastructure group didn’t face a steep studying curve.
“It’s only a GitHub motion,” mentioned Regev. “It creates a PR with the assessments. It’s not like a separate instrument we needed to study.”
“The aim is to really assist the developer study the code, take possession, give suggestions to one another, and study from that and set up the requirements,” added Friedman.
The Outcomes: Time Saved, Bugs Prevented
Since rolling out Qodo extra broadly, monday.com has seen measurable enhancements throughout a number of groups.
Inside evaluation reveals that builders save roughly an hour per pull request on common. Multiply that throughout hundreds of PRs monthly, and the financial savings rapidly attain hundreds of developer hours yearly.
These aren’t simply beauty points — many relate to enterprise logic, safety, or runtime stability. And since Qodo’s solutions mirror monday.com’s precise conventions, builders usually tend to act on them.
The system’s accuracy is rooted in its data-first design. Qodo trains on every firm’s personal codebase and historic knowledge, adapting to totally different group types and practices. It doesn’t depend on one-size-fits-all guidelines or exterior datasets. Every thing is tailor-made.
From Inside Device to Product Imaginative and prescient
Regev’s group was so impressed with Qodo’s affect that they’ve began planning deeper integrations between Qodo and Monday Dev, the developer-focused product line monday.com is constructing.
The imaginative and prescient is to create a workflow the place enterprise context — duties, tickets, buyer suggestions — flows immediately into the code assessment layer. That manner, reviewers can assess not simply whether or not the code “works,” however whether or not it solves the precise downside.
“Earlier than, we had linters, hazard guidelines, static evaluation… rule-based… it’s essential configure all the principles,” Regev mentioned. “But it surely doesn’t know what you don’t know… Qodo… feels prefer it’s studying from our engineers.”
This aligns intently with Qodo’s personal roadmap. The corporate doesn’t simply assessment code. It’s constructing a full platform of developer brokers — together with Qodo Gen for context-aware code technology, Qodo Merge for automated PR evaluation, and Qodo Cowl, a regression-testing agent that makes use of runtime validation to make sure take a look at protection.
All of that is powered by Qodo’s personal infrastructure, together with its new open-source embedding mannequin, Qodo-Embed-1-1.5B, which outperformed choices from OpenAI and Salesforce on code retrieval benchmarks.
What’s Subsequent?
Qodo is now providing its platform below a freemium mannequin — free for people, discounted for startups via Google Cloud’s Perks program, and enterprise-grade for firms that want SSO, air-gapped deployment, or superior controls.
The corporate is already working with groups at NVIDIA, Intuit, and different Fortune 500 firms. And due to a current partnership with Google Cloud, Qodo’s fashions can be found immediately inside Vertex AI’s Mannequin Backyard, making it simpler to combine into enterprise pipelines.
“Context engines would be the large story of 2026,” Friedman mentioned. “Each enterprise might want to construct their very own second mind if they need AI that truly understands and helps them.”
As AI techniques turn into extra embedded in software program improvement, instruments like Qodo are exhibiting how the precise context — delivered on the proper second — can remodel how groups construct, ship, and scale code throughout the enterprise.
