Sold by: Easycoder
Your engineers are already using AI. Different tools, personal accounts, private prompts, no shared standards. Gartner found 69 percent of organisations suspect or have evidence of employees using prohibited public GenAI tools. This accelerator brings that sprawl into one governed, shared way of working, built live with your team on your own use cases rather than taught from slides.
Overview
Everyone is using it and nobody owns it
Walk through most engineering teams and you will find AI everywhere. One engineer on Claude Code, another on Kiro, a third pasting into a browser tab on a personal account. It is not a policy failure. People reached for the tools because the tools help.
The cost shows up in three places.
It is siloed. The engineer who worked out how to make the tool genuinely useful on your codebase cannot pass that on. Everyone solves the same problems privately and the organisation learns nothing. You are paying for the same discovery over and over.
It is inconsistent. Output quality tracks whoever is driving, so review load goes up rather than down. Reviewers spend their time deciding whether generated code matches standards that were never written down.
It carries risk you cannot see. Gartner's survey of 302 cybersecurity leaders found 69 percent of organisations suspect or have evidence that employees are using prohibited public GenAI tools. Source code and customer data move through personal accounts and free tiers with no audit trail. In New Zealand, Xero found data privacy and security is the single most common reason businesses hold back on AI, named by 40 percent.
So the organisation pays three times, in licences, in review time and in exposure, and still cannot answer whether any of it is working.
What the accelerator does
It takes the AI practice your team already has and makes it one thing: shared, governed, and running on your own systems.
This is a live engagement, not a course. Your engineers are at the keyboard on real work from your backlog throughout. Nothing is demonstrated on a sample application.
How it runs
Discovery. What is actually being used, by whom, through which accounts, on what data. Usually the first time anyone has the real picture. This is also where the use cases are chosen, because the work has to be genuinely useful or the team will not carry it forward.
Live build sessions. Working sessions with your engineers on the selected use cases, building agents that run against your repositories, your ticketing, your pipelines and your data. Cross-functional rather than developers alone, because the handoffs between product, engineering, QA and operations are where most of the time goes.
The context layer. The part that stops the silo re-forming. Your standards, your architectural patterns, your review criteria and your guardrails, written as artefacts and checked into your repository so every engineer and every agent works from the same instructions. This is what makes the gain survive the people who were in the room.
Governance. Which accounts AI runs under, what it is allowed to see, what is logged and what is prohibited. Written alongside the engineers who have to live with it, which is why it holds.
Handover. The team runs it. The accelerator exists to leave, not to create a dependency.
What changes
Faster turnaround on routine work, because the agents handle the parts that are repetitive and well specified and your engineers stop re-deriving the same prompts. Less time in review, because generated work arrives already matching the standards. And a practice the next engineer inherits instead of rebuilding.
We do not publish a percentage, because the honest number depends on your codebase, your team and where your time currently goes. We measure yours instead. Delivery measurement is available as an add-on: your baseline captured from Jira, Git and your build pipeline before we start, and measured again afterwards across deployment frequency, lead time for changes, change failure rate and time to restore. You get your own number rather than somebody else's case study.
Who should be in the room
Engineers who write the code, plus product, QA and operations. An engineering leader who can make decisions without going away to ask. Eight to ten people works well, and two parallel teams works better than one large group.
Prerequisites
A named decision maker, access to a non-production AWS environment, real work from your backlog, and read access to your delivery systems if the measurement add-on is included.
Support
Enquiries are answered within two business days, with a scoping call and a written proposal. After purchase you get a named engagement lead and a facilitation plan before the first session. Support during business hours.
Documentation
Infrastructure costs
AWS services used during the engagement and in anything you keep afterwards are billed by AWS directly to you, separately from this engagement.
Highlights
- The problem is not adoption, it is fragmentation. Every engineer has worked out their own way to use AI and none of it is shared. The same problems get solved privately, output quality varies by who is driving, and review load goes up rather than down. This brings it into one place your whole team works from.
- Live, on your own use cases. Working sessions with your engineers on real work from your backlog, not a sample repository. The team leaves with agents running on their actual systems, and the context, standards and guardrails checked into your repository as artefacts rather than written up in a document nobody opens.
- Governance that is real rather than a policy. Where AI runs, which accounts it uses, what it is allowed to see, and what gets logged. Gartner puts prohibited public tool use at 69 percent of organisations, and 40 percent of New Zealand businesses say data privacy and security is the main reason they hold back.
Details
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Pricing
Custom pricing options
Pricing is based on your specific requirements and eligibility. To get a custom quote for your needs, request a private offer.
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Support
Vendor support
Email: hi@easycoder.co . Phone: +64 21 675 099. Enquiries through this listing are answered within two business days. After a private offer is accepted, a named engagement lead contacts you within two business days with a facilitation plan. Support during business hours.