Authentise launches source-available Whisper AI platform at RAPID + TCT 2026

Authentise, the data-driven additive manufacturing software company, launched its Whisper AI platform at the RAPID + TCT conference in Boston. Whisper is described as an "agentic AI backbone" that captures engineering activity and intent from tools like email, Slack, and enterprise systems, then organizes and acts on that data within existing workflows. The platform performs real-time compliance checks, risk detection, and automated updates across ERP, PLM, and QMS systems. CEO Andre Wegner stated the platform "listens, understands, and acts" without requiring engineers to change their working habits. Whisper is released as source-available, with early access programs requiring low upfront commitment and full costs incurred only after value is proven.

This launch addresses a persistent gap in AM digital transformation: the loss of engineering intent that occurs in informal communications outside formal PLM/ERP systems. Authentise positions Whisper against fragmented point solutions from competitors like Link3D (now part of Materialise) and Oqton, which focus on workflow automation but do not capture unstructured communication data. The platform's source-available model is unusual for enterprise AM software and may accelerate adoption among defense and aerospace customers who require code transparency for qualification. The timing aligns with the broader industry push toward AI-augmented manufacturing, where capturing tacit engineering knowledge becomes a competitive differentiator as AM scales into regulated production.

For Authentise, the practical challenge is proving Whisper's value in environments where engineers may resist surveillance of their communications. The company must demonstrate that the platform's compliance and provenance benefits outweigh privacy concerns, particularly in aerospace and defense settings where IP protection is paramount. Early adopters should evaluate Whisper against their existing QMS integration requirements before committing to the agentic workflow model.