A Global Wellness Brand Lifted PR Velocity 40% in 6 Weeks

The engineering team went from near-zero AI adoption to shipping 40% more pull requests in six weeks, and recovered enough capacity to offboard thirteen outside contractors.

+40%

Lifted PR velocity in six weeks

4–8 weeks

Implementation Time

Aanikh Kler

Lazer Technologies | Co-Founder Surf (acquired) | Canada's Young Entrepreneur of the Year 2023

Lazer Technologies

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The Challenge

A leading global health and wellness brand had no meaningful AI tool adoption across its engineering organization. Leadership knew the team was falling behind but had no definition of what good looked like and no credible path to get there. The risk was the standard enterprise failure mode: buy licenses, announce a rollout, watch usage flatline.

What They Built

Lazer embedded three engineers to run enablement as an engineering engagement rather than a training program: diagnose, build, measure, repeat. The team audited existing tooling, workflows, and team appetite, selected a stack centered on GitHub Copilot and coding agents, and drove adoption in deliberate waves: the six-engineer core frontend team first, then the eleven-person dev team, then the full 38-engineer organization. Hands-on pairing, live PR examples, and adoption metrics baked directly into team OKRs replaced the usual launch-and-hope approach.

Results were measured before and after: PR velocity, deployment frequency, tickets shipped, and the share of tasks completed by non-developers. The engagement is now extending into a software-factory build: an autonomous system that picks up low-complexity tasks end to end, freeing developers entirely, while PMs and UX deliver lightweight work with developer touchpoints only at review.

Lazer embedded three engineers and treated the rollout as an engineering engagement rather than a training program, following a diagnose-build-measure-repeat loop. They began by auditing the client's existing tooling, workflows, and the team's appetite for change, then selected a stack centered on GitHub Copilot and AI coding agents. Rather than announcing a company-wide launch, they drove adoption in deliberate waves: the six-engineer core frontend team first, then the eleven-person dev team, and finally the full thirty-eight-engineer organization. Each wave leaned on hands-on pairing and live pull-request examples instead of slide-based training, and adoption metrics were wired directly into team OKRs so usage became a tracked commitment rather than a suggestion. Progress was captured with before-and-after measurement across PR velocity, deployment frequency, tickets shipped, and the share of tasks completed by non-developers, giving leadership CFO-grade evidence. The engagement is now extending into a software-factory build: an autonomous system that picks up low-complexity tasks end to end, with product and UX contributors handing off lightweight work and developers involved only at review.

AI Role

AI coding agents and GitHub Copilot were rolled out as production workflow, not experiments: all frontend developers now work with coding agents, two non-developers ship production code through Copilot, and an autonomous software-factory system is being built to complete low-complexity tasks without developer involvement.

AI Model

GitHub Copilot

Infrastructure

Client's existing development stack • AI workflows integrated into team OKRs

Integration Points

GitHub workflows and PR review processes • Team OKR and metrics tracking

Impact

+40% PR Velocity in 6 Weeks

Team pull-request velocity across the engineering org rose 40% within six weeks of the rollout, measured against a pre-engagement baseline.

13 External Contractors Offboarded

Recovered internal capacity allowed the brand to fully offboard thirteen external contractors while two non-developers began shipping production changes.

~$300K/Year Recovered Per 10-Engineer Cohort

Measured productivity gains translate to roughly $300K in annual recovered capacity for every ten engineers, before the software-factory phase compounds it.

Technology Utilized

Implementation Complexity

Time Savings

Cost Reduction

Best Fit For

Mid-size engineering organizations (20 to 200 engineers) whose leadership knows AI tooling matters but has no internal playbook for making adoption real and measurable.

Aanikh Kler

Lazer Technologies | Co-Founder Surf (acquired) | Canada's Young Entrepreneur of the Year 2023

Lazer Technologies

Award-winning entrepreneur and tech leader helping startups and global brands like Netflix and Amazon drive growth by ethically harnessing consumer data and AI-driven innovation.

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Industry:

Consumer Goods & CPG

Healthcare & Life Sciences

Business Function:

Product & Engineering

Company Size:

251-1,000

Project Cost:

Cost not disclosed

Ownership:

Organization Type:

Private Company

AI Pattern:

AI Workforce Enablement

AI-Accelerated Custom Software

Value Type:

Time Savings

Cost Reduction

AI Model:

Frequently Asked Questions

How did a wellness brand increase pull-request velocity with AI coding tools?

Lazer embedded engineers to drive adoption of GitHub Copilot and AI coding agents in deliberate waves, pairing live on real pull requests and wiring adoption metrics into team OKRs. Pull-request velocity rose 40% within six weeks.

What AI tools did the engineering team use for coding?

The team standardized on GitHub Copilot and AI coding agents as production workflow. All frontend developers now work with coding agents, and two non-developers ship production code through Copilot.

What results did the AI coding adoption program deliver?

Pull-request velocity rose 40% in six weeks, and the brand offboarded thirteen external contractors as internal capacity recovered. Measured gains translate to roughly $300K in annual recovered capacity for every ten engineers.

How long did the AI coding adoption program take to show results?

The headline 40% gain in pull-request velocity was measured within six weeks of the rollout, with the broader engagement landing results in the four-to-eight-week range.

Who is AI coding adoption best suited for?

It fits mid-size engineering organizations of roughly 20 to 200 engineers whose leadership knows AI tooling matters but lacks an internal playbook for making adoption real and measurable.