MARCH 2026

relativity.com Engineering AI Productivity Report

A focused summary of AI adoption, productivity lift, and code quality for the relativity.com engineering team.

See how AI-active teams rank this week on the Exceeds Leaderboards.

The relativity.com engineering team reports 88.7% AI adoption, 1.80× productivity lift, and 20.0% code quality across recent work.

These metrics track how AI integrates into delivery pipelines, how throughput changes when assistance is used, and the health of AI-supported code review outcomes.

What this report measures

We analyze commits and diffs to estimate AI adoption, productivity lift, and code quality for your engineering organization.

How to interpret these metrics

Use these signals to understand how AI assistance fits into day-to-day development, where enablement efforts drive throughput, and how review practices keep quality steady.

AI Adoption Rate

HIGH

88.7%

AI assistance is present in 88.7% of recent commits for relativity.com.

AI Productivity Lift

HIGH

1.80×

AI-enabled workflows deliver an estimated 80% lift in throughput.

AI Code Quality

LOW

20.0%

Review insights show 20.0% overall code health on AI-supported changes.

How is the relativity.com team performing with AI?

The relativity.com engineering team reports 88.7% AI adoption, translating into 1.80× productivity lift while sustaining 20.0% code quality. These outcomes suggest AI-supported reviews are embedded in day-to-day delivery without trading off reliability.

Manager Questions Answered

Real questions engineering leaders ask about AI productivity, with live benchmarks and company-specific data.

What's a good company AI adoption rate?

relativity.com is at 88.7%. This is 45.0pp above the community median (43.7%)..

88.7%

↑45.0pp above43.7% Community Median

Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.

Does AI actually make developers faster?

relativity.com operates at 1.80×. This is 0.67× above the community median (1.13×)..

1.80×

↑0.67× above1.13× Community Median

Double down on automation around QA and release prep to compound the gains already in flight.

How does AI affect code quality?

relativity.com holds AI-assisted quality at 20.0%. This is 3.3pp below the community median (23.3%)..

20.0%

Roughly in line23.3% Community Median

Invest in AI-specific test checklists and shadow reviews to keep quality slightly ahead of peers.

How evenly is AI use distributed across our team?

AI impact is concentrated—88.9% of AI commits come from a few experts, raising enablement risk.

88.9%

Run prompt-sharing sessions, codify AI review checklists, and incentivize broad participation.

How can I prove AI ROI to executives?

relativity.com combines strong adoption, lift, and quality control—making the ROI story executive-ready.

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Link these metrics to deployment frequency and incident cost to convert engineering wins into business KPIs.

See how your full organization compares

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How Your Company Ranks

See how top engineering organizations compare across AI adoption, productivity lift, and code quality.

AI Adoption

% of commits with AI assistance

Companies in this quartile:

idesie.com

(2904.2%)

inngest.com

(1429.6%)

fmease.dev

(87.5%)

dimagi.com

(87.5%)

Top 25% of teams adopt AI in 65-75% of their commits.

Productivity Lift

Cycle-time improvement vs baseline

Companies in this quartile:

inngest.com

(4.82×)

u.nus.edu

(2.87×)

acad.pucrs.br

(1.12×)

mcornholio.ru

(1.12×)

Top performers sustain 1.5× cycle-time improvements over six months when embedding AI into workflows.

Code Quality

Post-merge defect rate

Companies in this quartile:

gzgz.dev

(20.0%)

gwu.edu

(20.0%)

gzgz.dev

(20.0%)

gwu.edu

(20.0%)

Top 25% maintain quality above 92% while expanding AI usage, pairing automation with rigorous guardrails.

Rankings based on aggregated Exceeds AI dataset of 1.2M commits across open-source and enterprise engineering teams (Q4 2025).

Top contributors

Top contributors combine high AI adoption and quality output. Encourage internal sharing of best practices.

Contributor

Commits

AI Usage

Productivity Lift

Code Quality

chaitan masannagari

Commits103

AI Usage92.0%

Productivity Lift2.00x

Code Quality20.0%

Divya.venkataraju

Commits64

AI Usage92.0%

Productivity Lift1.90x

Code Quality20.0%

Deepankar Kalita

Commits89

AI Usage92.0%

Productivity Lift1.51x

Code Quality20.0%

Neil Maartens

Commits5

AI Usage82.0%

Productivity Lift1.10x

Code Quality20.0%

Amith Shet

Commits16

AI Usage78.0%

Productivity Lift1.10x

Code Quality20.0%

Encourage knowledge transfer from top AI users to others through internal mentoring or recorded "AI coding walkthroughs." Balanced adoption across the team typically improves overall performance by 12-15%.

Cross-Organization Network