An AI harness for software delivery
A backlog and a workflow for AI assisted build, with the work kept transparent to whoever is directing it. The point is not speed on its own, it is knowing what was built and why.
The short version.
A backlog AI works from
Work is expressed as a backlog with enough context to be executed, rather than as prompts typed into a window and lost. What was asked for stays attached to what was produced.
A workflow, not a chat
Build moves through defined stages, so output is reviewed and accepted deliberately. AI accelerates inside a process rather than replacing it.
Transparent to the person directing it
Whoever is accountable for the build can see what is being produced and on what basis. AI applied where nobody can see the working is how a delivery team loses control of its own codebase.
Where this earns its place.
- Delivery teams applying AI to build work who need the output reviewable rather than merely fast.
- Programmes where someone is accountable for what ships and needs to see how it was produced.
Into your estate, not ours.
- Used by Ionyze delivery teams, and deployed into the client's own environment where the client's team is building alongside us.
All of it.
The backlog, the workflow and the record of how the build was produced.
Accelerators, not software to rent. They come with the engagement rather than as a separate licence.
The other accelerators.
AI Exposure Index
Scores how much of a job role AI can accelerate or replace, rolled up across every role. A business case for AI investment, with a number on it.
D365 Usability Analytics
Click path analysis for the ERP. Records how long each process step really takes, in the client's own tenant, and shows where users are struggling.
ServiceOps
Field service for utility networks, installed on the client's own tenant and managed by Ionyze. Built for the work engineers actually do on site.