Co-presented the uReview talk at AI Engineer World's Fair 2026 with Will Bond, covering uReview's observability, evaluation and team customization; the talk page lists him at Uber and gives no title.
Why their word counts: Describes the evaluation and rollout of a code-review system his team built and runs, so he is close to the numbers; Uber is promoting its own work.
Profile from this source , checked 2026-11-01 .
“ [uReview] does … around 25,000 comments a week, and we get 10% of them actually get some feedback … We also saw that the overall address rate was around 67%, and almost three quarters of the high severity issues were usually addressed by the developers” Ameya Ketkar Speaker, AI Engineer World's Fair 2026 (job title not stated) · Practitioner ,
Context. Ameya Ketkar is reporting results for uReview, Uber's code-review system. The address rate is how often a developer acts on a comment, as he defined it earlier in the talk.
Talk: Building uReview, Uber's Multi-Agent Code Review Engine (talk, AI Engineer World's Fair 2026) , AI Engineer (YouTube) , August 28, 2026, at 10:13 .
[ Grade B ] Said in the last year Uber: $52 billion revenue, FY2025 Passage page
“ we saw that against like a very naive implementation, our costs were down by 60%, and our quality and our accuracy was up by around 70%.” Ameya Ketkar Speaker, AI Engineer World's Fair 2026 (job title not stated) · Practitioner ,
Context. Ameya Ketkar is summarizing the effect of the observability and evaluation work on uReview, Uber's code-review system.
Talk: Building uReview, Uber's Multi-Agent Code Review Engine (talk, AI Engineer World's Fair 2026) , AI Engineer (YouTube) , August 28, 2026, at 10:50 .
[ Grade B ] Said in the last year Uber: $52 billion revenue, FY2025 Passage page
“ One of the biggest learnings in this process was like the model doesn't know that it's wrong. It always confidently says 100% sure that, yeah, this is the review for your code. Go ahead. But we saw that, no, it actually needs a lot of guidance from the teams, because each team has its own style guide, its own patterns or … anti-patterns that they want to look for.” Ameya Ketkar Speaker, AI Engineer World's Fair 2026 (job title not stated) · Practitioner ,
Context. Ameya Ketkar is describing what Uber's team learned while adding observability and evaluation to its uReview code-review agent.
Talk: Building uReview, Uber's Multi-Agent Code Review Engine (talk, AI Engineer World's Fair 2026) , AI Engineer (YouTube) , August 28, 2026, at 06:35 .
[ Grade B ] Said in the last year Uber: $52 billion revenue, FY2025 Passage page
“ One thing that we learned is that actually writing the skill was very easy, like teams just very quickly wrote a skill by asking Claude to write one, go over my previous PR reviews and write me a skill, but the hard part was how to run these skills at scale with consistent quality and low cost, and that required a lot of iterations, not only from the [uReview] team side, but also … for each team who was trying to write these rules.” Ameya Ketkar Speaker, AI Engineer World's Fair 2026 (job title not stated) · Practitioner ,
Context. Ameya Ketkar is describing how Uber's teams wrote their own code-review skills for uReview.
Talk: Building uReview, Uber's Multi-Agent Code Review Engine (talk, AI Engineer World's Fair 2026) , AI Engineer (YouTube) , August 28, 2026, at 09:44 .
[ Grade B ] Said in the last year Uber: $52 billion revenue, FY2025 Passage page
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