CryspIQ® new release brings trusted, governed answers from enterprise data into Microsoft Teams and Copilot

Résumé détaillé

AI is only as good as its data: CryspIQ® v3.3.0 delivers faster setup, cleaner data and answers users can trace at speed and scale.

COMPANY NEWS: AI is only as good as its data: CryspIQ® v3.3.0 delivers faster setup, cleaner data and answers users can trace at speed and scale.

CryspIQ® today announced CryspIQ® v3.3.0, designed to help users get from a source file to a trusted data model faster, catch data quality problems earlier, and trace and secure every answer. For businesses, this means faster, more trusted answers from their own data, in the tools they already use, with every answer traceable and limited to what each person is allowed to see.

The release comes as organisations want to use AI on their own data, but most can't trust the answers, because the data underneath is incomplete, inconsistent or not governed. CryspIQ® v3.3.0 closes that gap.

With v3.3.0, executives can ask CryspIQ® questions directly in Microsoft Teams and Microsoft 365 Copilot, and get answers from governed enterprise data without raising a request or waiting for a report. Every answer runs under the person's own Microsoft sign-in, is limited by their Microsoft Entra ID security groups, and shows the evidence behind it. Copilot reads from CryspIQ®, so there is no separate copy of the data or Copilot data source to set up and keep in sync.

“Everyone wants to use AI on their own data, but the real question is whether you can trust what it tells you. That trust is earned in the data layer, long before anyone types a question. With v3.3.0, we’ve focused on getting those foundations right, so people can act on the answers with confidence,” said Dan Peacock, Co-Founder and CEO at CryspIQ®.

The release makes the data more trustworthy through three core features:

● Guided setup: v3.3.0 automatically maps data into the patented data model from a source file efficiently. Lumen, the CryspIQ® AI assistant, proposes how the file fits the data model, and the user reviews the suggestions and saves, with a preview before saving.

● Data quality caught earlier: Problems are caught when data is mapped, before it reaches the data model.

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