AI Productivity
TensorX — sovereign AI inference for product teams
Sovereign AI is naturally going to be one of the busiest use cases for AI in the coming years. In a company context, it is usually best for everyone to be on a single platform like Claude Code, so context, prompts and review habits stay shared. But in certain cases — where data sovereignty matters, where you want to compare model outputs, or where the context window is less important — I switch to TensorX.
Why a single platform still wins most of the time
For most product work, continuity is the hidden advantage. When your prompts, project files, interview notes and draft briefs all live in one place, the model gets smarter as the engagement progresses and the team does not waste time re-explaining context. That is why I default to Claude Code for the bulk of drafting, synthesis and stakeholder communication.
When I reach for TensorX
There are three situations where I deliberately step outside the main platform:
- Data sovereignty. If the input involves sensitive customer data, commercial terms or anything that should stay inside EU jurisdiction, TensorX runs on EU infrastructure with zero data retention. That alone can remove a whole category of compliance questions.
- Cost-sensitive, high-volume tasks. When the context window is less important — think one-off summaries, translations, reformatting, or quick classification — the cheaper models on TensorX can cut costs without a meaningful drop in quality.
- Model comparison. Running the same prompt through different models is one of the fastest ways to expose blind spots, hallucinations or weak reasoning. TensorX makes it easy to compare outputs side by side before committing to a workflow.
How I use it in practice
I treat TensorX as a sidecar, not a replacement. The team’s canonical work stays on Claude Code; TensorX handles the tasks where EU residency, cost or model comparison matter more than long context. I also use it to sanity-check outputs: if two different models agree on a synthesis, I am more confident passing it on.
Worth a small experiment
If you are curious, put a few euros into TensorX and try it on a real but non-critical task — a batch summary, a competitor rewrite, or a comparison run against your current model. You will quickly see where it saves money and where the context continuity of your main platform still matters more.