Case study
The team was using AI. The codebase was not.
EaziCore builds asset management natively on Salesforce. The engineering team was already “using AI” — by pasting code into ChatGPT and pasting the answer back. We put agents in the repo, a reviewer in front of merge, and a number on the owner’s desk.
- Client
- EaziCore
- Product
- Asset management on Salesforce
- Engagement
- Audit, sprint, measure
- Website
- eazicore.com
EaziCore is a Salesforce-native platform for enterprise assets: work orders, preventive maintenance, the asset lifecycle, inventory and GIS. The product is built to close a maintenance loop, so a finished work order makes the next one better informed.
The loop around the code did not work that way. There was no agent infrastructure, no shared context or skills in the repo, no CI guardrail in front of a change, and no architecture for how AI was allowed to touch the product. Use of AI stopped at a browser tab.
Copy, paste, hope
- 01A feature starts
- 02The relevant code is pasted into ChatGPT
- 03The answer is copied back by hand
- 04A person reviews it later, with no earlier check
The work stays in the repo
- 01Context and skills already live in the repo
- 02A coding agent picks up the change
- 03A reviewer agent checks the draft
- 04A person signs it off
Audit, then a sprint, then the same numbers again
We baselined the team before changing anything. AI use was manual copy-paste. Nothing governed it. On the efficiency reading we took at the start, the team was at 48%.
The sprint then landed in four places, not one. Developers got agents that can see the codebase. Senior engineers got a review path that runs before a human opens the pull request. Engineering managers got the weekly tracking and video work off their plate. Leadership got a single view of adoption, output and cost.
- 01
Audit
A baseline of how engineering actually ran: tools, review, and the 48% efficiency figure, taken before any rollout.
- 02
Sprint
Agents, review, manager workflows and an owner dashboard. Each one replaced a manual loop, rather than sitting beside it.
- 03
Measure
The same readings again: efficiency, delivery cost, and the quality of what shipped.
What changed at each level
Developers stopped beginning a feature in a chat window. Agents sit in the codebase, with the project’s context, conventions and reusable skills already wired in.
Senior engineers kept the sign-off, and lost the part where every basic mistake waited for them. A reviewer agent reads the draft first. What still needs a person is what reaches the person.
Engineering managers had been spending the week on social tracking and on cutting release videos. Dashboards took the tracking. Release videos, narration included, are produced through Remotion.
Owners had a feeling the team was “using AI” and no way to tell if it was paying for itself. One dashboard now shows adoption, output and cost across the team.
- 01AI drafts the change
- 02Reviewer agent checks it
- 03A person signs it off
A logic miss is returned to the draft
A day in each role
Composite scenes from the engagement, not transcripts.
Developer
Before
Every feature started by finding the relevant code, pasting it into ChatGPT, writing the prompt, and copying the output back by hand.
After
A codebase-aware agent picks up the work with the project’s context, conventions and skills already in place.
Senior engineer
Before
Every pull request landed in the same manual queue, whether or not a machine had written it. Nothing caught a flawed path before a person looked.
After
Reviewer agent first, then a person. Basic and mid-level logic issues are caught before merge. Review dropped from about 80% of the cycle to about 30%. That share is an estimate.
Engineering manager
Before
Social tracking and release videos took hours of manual work every week, on top of the actual delivery.
After
Dashboards track the social performance. Release videos with synced narration are generated through Remotion.
Owner
Before
A sense that the team was using AI, and no reading that showed whether it was paying off.
After
One dashboard shows adoption, output and cost. “It is working” has a number under it.
What moved
Three of these are estimates, marked as such. The others were re-read after the rollout. Release cadence did not change: deploys stay on Salesforce’s schedule. What changed is how many of those deploys failed.
48% → 80–88%
Engineering efficiency
Same baseline, read again after the rollout
−20%
Delivery cost
About a fifth lower
80% → 30%
Review and QA time
Estimate. Share of the cycle
2 yr → 8 mo
Planned work delivered
Estimate. Two years of planned work in eight months
≈ 0
Failed deployments
Cadence unchanged. Failures dropped to effectively zero
Near zero
Defect rate
Estimate. Draft, reviewer agent, then a person
In their words
From a business perspective, adopting AI at EaziCore has been about more than improving development speed. We wanted to build a more efficient organization where technology, people and processes work together.
The AI enablement initiative has given us better visibility into team productivity, delivery costs and overall output. Automating repetitive workflows and introducing AI-driven engineering processes has helped us make better use of our resources while maintaining product quality.
What matters most is that AI is now contributing to measurable business outcomes, not just experimentation. It has changed how we think about scaling the product and running the business.
One of the biggest improvements I’ve noticed at EaziCore is how AI has become part of our everyday Salesforce workflows rather than just another tool we occasionally use.
From debugging and configuring Salesforce to working on Flows, automation and platform maintenance, AI has helped reduce repetitive effort and speed up problem-solving. The introduction of reusable skills, shared project context and automated checks has also made our development process more consistent.
As a Salesforce Developer and Administrator, I see the real value in how these improvements help the team work more efficiently while maintaining the reliability of the platform.