PRFlow helps teams do the opposite.

It s an AI agent that analyzes GitHub pull requests
so human reviewers start from a cleaner, clearer place.

PRFlow doesn t just comment on a PR.
It explains why something matters.

It s an AI agent that reviews GitHub pull requests
and lets you chat with it about the logic behind each suggestion.

That turns review from a checklist
into a conversation about the code.

We built it so teams can move faster
without losing understanding.

Authors bring intent.
Reviewers bring perspective.

When either side disengages,
quality drops quietly.

Strong reviews feel like collaboration,
not inspection.

PRFlow is designed to support that balance,
not replace it.

Same expectations.
Same cleanliness.
Same level of readiness.

When that baseline shifts between PRs,
reviewers subconsciously lower the bar.

I recently noticed a disturbing pattern with one of our most promising Intern/Junior Engineers.

• Month 1: They were shipping features daily. Fast. Hungry.

• Month 2: The velocity slowed down.

• Month 3: Silence began..

They hadn't opened a Pull Request in 2/3 days, even though their tasks were marked 'In Progress'.

I assumed the worst: Burnout? Disengagement? Maybe they are overemployed?

When teams are aligned,
reviews are calm, focused and thoughtful.

When teams are rushed or unclear,
reviews become noisy, reactive or silent.

Meetings.
Incidents.
Messages.
Context switching.

When reviews arrive noisy,
they get rushed.

Senior Reviews: Architecture Help or 'Premium Nagging'?

Yesterday, I wrote about the "Friday Merge" ghost story the pain of waiting days just to get code shipped.
(Thanks for the great discussion on that)

Seeing the same feedback again and again slows learning.
Giving it again slows teams down.

PRFlow handles repeatable checks automatically,
so human reviewers spend time on things that actually change how code evolves.

Most code doesn t last.
But the reasoning behind it should.

Good code review captures intent,
why something exists,
what problem it was meant to solve,
and what tradeoffs were accepted at the time.