How Artemis Ops Cut Review Time by 20% with a Lean Team Using Optibot

Artemis Ops builds software for the construction industry—automating compliance paperwork and workflow tracking for operators who don’t have time for friction. The product was scaling; the engineering team was small. The bottleneck wasn’t ideas. It was reviews.

PRs often ballooned to thousands of lines. Reviews were distracting, slow, and dependent on a second engineer being available. “We’d actually read through every single change,” says Sam Lee, CEO & Co-founder. “It took a really long time and it was frustrating.”

Artemis Ops needed a way to review massive diffs quickly, surface real risks automatically, and keep tickets moving without adding headcount or burning out the team.

The Problem

Big PRs, context switching, and reviewer availability slowed everything down

Artemis Ops tracked work in Notion and paired tickets with PRs. When code was “ready,” it moved to review—where velocity stalled.

• Large, high-context PRs demanded deep, line-by-line attention.
• Context switching pulled engineers off their own tickets.
• Reviewer availability created gaps; when the second engineer wasn’t free, progress froze.
• Manual summaries of changes and rationale lived across PRs and tickets, creating extra clicks and review fatigue.

“Some PRs were thousands of lines. We’d read through every single change. It took forever and pulled people off their own work.”
— Sam Lee, CEO & Co-founder, Artemis Ops

The result: slower ticket turnaround, inconsistent review quality, and a team spending more time inspecting code than shipping improvements.

The Solution

Optibot: AI-powered reviews, auto-summaries, and cross-tool sync (GitHub ↔ Notion)

Artemis Ops adopted Optibot, Optimal AI’s code-review agent, to front-load signal and reduce human drag.

• Auto review on every PR: Optibot scans diffs immediately and flags the highest-risk changes first.
• Critical issue highlighting: Big, structural concerns are surfaced in seconds—so humans skim smarter, not harder.
• Auto-summaries of changes: Clear overviews of what changed and why reduce “where do I start?” time.
• Cross-tool sync: Optibot’s PR comments flow into Notion tickets—no extra tab-hopping to copy context.
• Slack-based onboarding: Setup took minutes, and the team was reviewing with AI on day one.

“Optibot identifies big changes that could be a problem faster than we would. We still skim, but we rely on it to highlight the big issues or inefficiencies.”
— Sam Lee

Beyond speed, Optibot teaches: feedback on redundancy, verbosity, and inefficient patterns made every engineer stronger.

“It’s making us better coders. We’re getting feedback when things are redundant or overly verbose.”
— Sam Lee

The Results

20% faster overall process, faster tickets, and fewer review interruptions

With Optibot handling first-pass review and pushing context into Notion, Artemis Ops saw measurable impact:

• 20% faster overall process (review + ticket flow)
• Reduced review time on large PRs through targeted issue highlighting
• Fewer pull-offs: engineers are pulled away less to review others’ work
• Faster ticket turnaround thanks to PR comments syncing into Notion
• Consistent coverage when a second engineer isn’t available

“The really big PRs have been great. It just highlights the six things we need to change.”
— Sam Lee

“Having a system that can auto review was crucial for a small team. The onboarding was so smooth—done over Slack—and everyone said, ‘Wait, that’s so cool.’”
— Sam Lee

Scaling faster, smarter, and with less drag

Artemis Ops turned code review from a blocker into a force multiplier: AI does the heavy lifting, humans apply judgment, and tickets move with less friction. Reviews are faster, quality is higher, and the workflow works across the team’s actual tools.

With AI handling first-pass reviews and context stitching, this small construction-tech team ships like a bigger one—without sacrificing quality or burning cycles on busywork.