How the reasoning layer works.
Zigital AI is not a chatbot bolted onto a lab. It is a pipeline: collect physical evidence, normalize it, reason over it with context, and hand a decision back to the people and agents running the work.
Four stages between an instrument and a decision.
Each stage is inspectable on its own. If reasoning is wrong, you can see which evidence it was given.
Pulse gathers instrument telemetry, logs, and run records from liquid handlers, readers, and movers — vendor-agnostic, without changing the instrument.
The Hub aligns signals from different vendors onto one timeline and one vocabulary, so a run can be compared against the fleet and against its own history.
A language model reads the normalized evidence with product and instrument context, interprets errors, and separates ordinary variation from real drift.
Findings leave as structured output: a verdict, an explanation, and a recommended action an operator or an orchestration agent can consume.
The same reasoning layer, pointed at your problem.
The tool below runs on the ZdefLabs reasoning layer. It receives your description plus the current capability of each product, then returns a typed recommendation: which products apply, why, what to do next, and what to ask us.
- Reasoning is grounded in the evidence supplied — the model is not asked to guess missing measurements.
- Every answer returns as a typed structure, so downstream agents get fields, not prose to parse.
- The model never invents pricing, delivery dates, customer names, or certifications.
- Model calls run server-side only; credentials never reach the browser.
- Output is advisory. A person or a policy decides what happens to a run.
Put the reasoning layer on your fleet
Talk with us about a pilot that connects your instruments to Zigital AI and returns decisions your team and your agents can act on.