Work examples without the theatre
These are representative engagement patterns: the kind of messy technical situations Voodoo AI is useful for, and the practical shape the work usually takes.
Supply Chain Forecasting Workflow
Representative AI & data pattern
Relevant if: you need AI or data systems to drive measurable operational improvement rather than isolated experimentation.
Situation
Planning teams had useful data, but forecasts, exceptions, and procurement decisions lived across disconnected tools and manual judgement calls.
Practical approach
Start with data readiness, workflow ownership, and exception handling. Then connect forecasting outputs into the operational systems where decisions are already made.
What improves
Typical tools involved
Cloud Migration Without a Big-Bang Rewrite
Representative cloud modernisation pattern
Relevant if: you are modernising critical services without accepting unnecessary delivery risk or runaway cloud spend.
Situation
A critical estate needed modernising, but downtime, compliance, dependency risk, and unclear ownership made a simple migration plan unrealistic.
Practical approach
Design the target environment, map dependencies, move services in bounded batches, validate rollback paths, and introduce cost governance before spend becomes invisible.
What improves
Typical tools involved
Node.js Platform Recovery
Representative platform architecture pattern
Relevant if: your platform is slowing product delivery, struggling under load, or carrying too much architecture debt.
Situation
A growing platform had become difficult to reason about: slow paths were unclear, releases felt risky, and new features touched too much of the system.
Practical approach
Identify the worst bottlenecks, make boundaries explicit, improve observability, and change the architecture in steps small enough for the team to absorb.
What improves
Typical tools involved
Facing something similar?
If the details are different but the pattern feels familiar, we can help work out the first sensible move.
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