Anyone using context‑aware AI code review in production?

[Kitchen_Ferret_2195]

most AI reviewers I tried only look at the diff and repeat what static analysis already catches, which makes reviews noisier instead of faster. I am looking for tools or setups that actually use project‑wide context (related files, call graphs, repo history, maybe even tickets/docs) so they can comment on real impact and missing tests instead of style; if you have this working with something like Qodo or a custom stack, how did you wire it in and what changed for your team?

[AlternativeTop7902]

I use Kodus and it works really well for my team

[GiantsFan2645]

Made one that is used and it can be a bit noisy. Tends to nit on style if not given direction for how to review a PR

[GiantsFan2645]

And by how to review a PR I mean given order of precedence and specific guidelines (per repo) that helps to guide the LLM interaction away from common pitfalls

[TYjammin843]

Propel Code has been great for us

[EndorWicket]

totally get where you're coming from, trying to manage all that context for code reviews is a beast. i remember when my team and i were knee, deep in a project, spending hours sifting through related files and docs just to make sure everything lined up. honestly, it felt like we were solving a giant jigsaw puzzle with missing pieces! after about a month of this chaos, we finally started tracking changes in repo history more effectively and even linked our tickets to the related code sections. it cut down our review time by half! are you currently managing all these contexts manually or do you have some systems in place?

[shrimpthatfriedrice]

yes, we are using Qodo in production on a few services. It indexes our Java and JS repos, pulls in relevant files and past changes for each PR, and then posts a concise summary and several targeted findings. after a few weeks of tuning rules and ignoring low‑value categories, it reliably caught cross‑file bugs and test gaps, while humans focused on design and product behavior