An IT infrastructure operations platform that started as an internal ticketing requirement and grew into a SaaS proposition — SLA management, escalation workflows, asset inventory, monitoring and operational reporting.
An internal IT team needed a better way to handle tickets. The available options were either enterprise ITSM platforms priced for organisations several times the size, or spreadsheets. The gap between those two is where a lot of mid-sized operations live.
What began as scoping a ticketing tool turned into a broader observation: ticketing is the visible symptom. The real problem is that IT operations runs on knowledge that lives in people's heads — which asset is where, which SLA is about to breach, what is running low, what is currently down.
InfraCue is the attempt to put that in one place.
I am the product owner, not the engineering team, and the distinction matters.
I own the problem definition and the requirements: translating operational pain into specified features, prioritising the roadmap, designing the workflows and the states they move through, directing UI/UX, and taking the commercial decisions — branding, positioning, pricing concepts, hosting, SaaS packaging and go-to-market. I work with development, test the builds, and write the product documentation.
I use AI coding assistants throughout — for prototyping, for working through solution options, and for closing the gap between a requirement I can describe and an implementation I can evaluate. That has changed how fast a specification can become something clickable, which in turn changes how early you find out that the specification was wrong.
Most transformation work is bounded by an existing organisation's systems and appetite. Building a product from zero removes those constraints and exposes everything else: whether you can identify a problem worth solving, define a solution precisely enough for someone to build it, make commercial decisions without a template, and keep scope honest.
It is the same skill as internal transformation work, without anywhere to hide.
A conversational assistant that turns a visitor's plain-language measurement problem into a cited, technically validated recommendation — with a deterministic rule engine, not the language model, holding all recommendation authority.
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