As cloud undertaking monitoring software program monday.com’s engineering group scaled previous 500 builders, the workforce started to really feel the pressure of its personal success. Product traces have been multiplying, microservices proliferating, and code was flowing sooner than human reviewers may sustain. The corporate wanted a method to evaluation 1000’s 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 Progress 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 check quickly grew to become a crucial a part of monday.com’s software program supply infrastructure, as a brand new case research launched by each Qodo and monday.com at present reveals.
“Qodo doesn’t really feel like simply one other instrument—it’s like including a brand new developer to the workforce who really learns how we work," Regev instructed VentureBeat in a latest video name interview, including that it has "prevented over 800 points monthly from reaching manufacturing—a few of them may have triggered critical safety vulnerabilities."
In contrast to code technology instruments like GitHub Copilot or Cursor, Qodo isn’t making an attempt to write down new code. As a substitute, it makes a speciality of reviewing it — utilizing what it calls context engineering to know not simply what modified in a pull request, however why, the way it aligns with enterprise logic, and whether or not it follows inside greatest practices.
"You possibly can name Claude Code or Cursor and in 5 minutes get 1,000 traces of code," stated Itamar Friedman, co-founder and CEO of Qodo, in the identical video name interview as with Regev. "You’ve got 40 minutes, and you may't evaluation that. So that you want Qodo to truly evaluation 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 tons of of repositories and providers. 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 examine for apparent bugs or model violations, however to guage whether or not a pull request follows team-specific conventions, architectural pointers, and historic patterns.
It does this by studying from your personal codebase — coaching on earlier PRs, feedback, merges, and even Slack threads to know how your workforce works.
"The feedback Qodo provides aren’t generic—they replicate our values, our libraries, even our requirements for issues like function flags and privateness," Regev stated. "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 strategy to managing every thing the mannequin sees when making a choice.
This contains the PR code diff, in fact, but in addition prior discussions, documentation, related information from the repo, even check outcomes and configuration information.
The thought is that language fashions don’t actually “assume” — they predict the following token primarily based on the inputs they’re given. So the standard of their output relies upon nearly completely on the standard and construction of their inputs.
As Dana Effective, Qodo’s neighborhood supervisor, put it in a blog post: “You’re not simply writing prompts; you’re designing structured enter below a set token restrict. Each token is a design resolution.”
This isn’t simply concept. In monday.com’s case, it meant Qodo may catch not solely the apparent 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 latest PR, Qodo flagged a line that inadvertently uncovered a staging atmosphere variable — one thing no human reviewer caught. Had it been merged, it might need triggered issues in manufacturing.
"The hours we might spend on fixing this safety leak and the authorized challenge that it could deliver could be way more than the hours that we cut back from a pull-request," stated Regev.
Integration into the Pipeline
Immediately, Qodo is deeply built-in into monday.com’s growth workflow, analyzing pull requests and surfacing context-aware suggestions primarily based on prior workforce code critiques.
“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 recommendations throughout the evaluation course of and stay accountable for ultimate choices — a human-in-the-loop mannequin that was crucial for adoption.
As a result of Qodo built-in immediately into GitHub by way of pull request actions and feedback, Monday.com’s infrastructure workforce didn’t face a steep studying curve.
“It’s only a GitHub motion,” stated Regev. “It creates a PR with the checks. It’s not like a separate instrument we needed to be taught.”
“The aim is to truly assist the developer be taught the code, take possession, give suggestions to one another, and be taught 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 exhibits that builders save roughly an hour per pull request on common. Multiply that throughout 1000’s of PRs monthly, and the financial savings shortly attain 1000’s of developer hours yearly.
These aren’t simply beauty points — many relate to enterprise logic, safety, or runtime stability. And since Qodo’s recommendations replicate 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 information, adapting to completely different workforce types and practices. It doesn’t depend on one-size-fits-all guidelines or exterior datasets. Every part is tailor-made.
From Inside Device to Product Imaginative and prescient
Regev’s workforce was so impressed with Qodo’s influence 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 evaluation layer. That manner, reviewers can assess not simply whether or not the code “works,” however whether or not it solves the correct drawback.
“Earlier than, we had linters, hazard guidelines, static evaluation… rule-based… it’s worthwhile to configure all the principles," Regev stated. "Nevertheless it 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 evaluation 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 check 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 by way of Google Cloud’s Perks program, and enterprise-grade for corporations that want SSO, air-gapped deployment, or superior controls.
The corporate is already working with groups at NVIDIA, Intuit, and different Fortune 500 corporations. And because of a latest 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 huge story of 2026," Friedman stated. "Each enterprise might want to construct their very own second mind if they need AI that really understands and helps them."
As AI programs grow to be extra embedded in software program growth, instruments like Qodo are displaying how the correct context — delivered on the proper second — can rework how groups construct, ship, and scale code throughout the enterprise.
