Xablu works alongside product, technology and leadership teams to create direction, remove delivery roadblocks and establish practical ways of working.
The questions we answer
Product strategy and discovery
We validate the riskiest assumption with real users, then set direction and priorities the whole organization can read — including which AI or automation ideas are worth a build and which are not.
Architecture and platform advisory
We assess the platform against how the product has to change, and write the decision down with its trade-offs and cost consequence.
Delivery, quality and release review
We look at how work actually flows — scope, testing, pipelines, team structure — and prioritize the changes that move the release date.
Story mapping and release planning
We map the journey end to end and cut it into releases that stand on their own, each with the outcome it has to produce.
AI opportunity and feasibility
We identify valuable applications, test the critical assumptions and determine what the data, technology, governance and organization need before anything is built.
Engagement formats
We propose the smallest format that can answer the question. Any of them can continue into the next.
One or two workshops with the people who own the decision, ending in a written direction.
An independent read on the product, the architecture or delivery, with prioritized recommendations.
Research, validation and story mapping until the first release can be costed and started.
A named consultant working part-time inside your organization, in your meetings and decisions.
What you are left holding
Which of these apply is agreed before the engagement starts.
Findings on the product, platform or delivery, prioritized by impact.
The journey end to end, including the steps that happen off-screen.
Scope sliced into releases that each stand on their own.
Direction and priorities, with the measures that decide what ships.
The choice, the options rejected and the maintenance consequence.
Prioritized opportunities assessed against user value, business value, feasibility, data availability and risk.
A recommendation covering the use case, required data, architecture, governance, constraints and the smallest useful validation.
A costed first release and the improvement backlog behind it.
We prefer advice we could execute ourselves — so a first conversation ends with a recommended format, including when that is to do nothing yet.
