AI built for CMC
With models that use structured data within a governed, compliant, and workflow-aligned system, our AI is built to scale in CMC.
Trusted AI applications CMC teams can actually use
Designed to scale
AI is built on a trusted, domain-specific knowledge foundation, making it scalable, governable, and sustainable by design.
Made for CMC
Workflow-aligned applications improve historical data usage and enable better decision-making.
Built for GxP compliance
Our system keeps underlying data static and ensures that all AI outputs are human-reviewed and tracked, making it suitable for use in GxP environments.
Overview
The CMC context layer that AI needs
QbDVision's structured CMC knowledge platform provides the essential basis for credible, scalable AI, focused on converting poorly optimized data into rich knowledge and process intelligence. Built within a governed, compliant, and workflow-tailored system, our models enable context-aware insights and execution.
Take a look under the hood
Governed
Defined credibility thresholds
- Change controlled AI features ensure that the model, prompt, schema, and code are attributable, creating a traceable, 21 CFR part 11-compliant audit trail.
- Defined credibility thresholds for accuracy and quality ensure reliable performance.
- Built-in safeguards, role-based access controls, and required human review further support compliant use in validated enterprise environments.
Sustainable
Efficient by design
- Work from atomized, reusable information, that reduces computation, improves consistency, and lowers operational cost.
- Built for CMC-specific workflows so that you can find accurate CMC information and create a scalable, sustainable, and validated system of record and work.
Structured
Connected, contextualized CMC knowledge
- Easily ingest legacy documents and reorganize their contents into connected, usable data.
- Transform fragmented data into connected, reusable CMC knowledge and bring AI into the entire lifecycle.
Trusted
A trusted foundation
- Built on AWS infrastructure; governed data, traceability, and measurable AI performance create the confidence needed to deploy AI in regulated CMC workflows.
- Maintain compliance, oversight, and scientific integrity within your organization.
Meet Qurio™
Qurio is your CMC teammate built on QbDVision's structured knowledge foundation. It works from your connected process, product, and regulatory data, so every answer is steeped in context. Here’s what Qurio can do.
See what you can do
Insights by Qurio
Retrieve records and data, and get direct links to sources using natural-language questions.
Compare process parameters, quality attributes, and control strategies in a structured table.
Trace relationships between parameters, materials, processes, risks, and quality attributes.
Surface historical parameters, qualified materials, suppliers, and control strategies from previous programs.
Find CQAs, CPPs, risk assessments, and source-cited guidance without leaving QbDVision.
Build with confidence on data you can trust
94.2%
of expected information successfully identified from imported data
92.7%
of provided information evaluated as correct
225+
diverse tests used to measure capability and performance
Your questions, our expertise
No. Customer data is never used to train public or foundational AI models. QbDVision uses AI models through Amazon Bedrock, with contractual protections that prohibit customer data from being used for model training. Customer data remains within QbDVision’s AWS environment.
AI processing occurs within QbDVision’s AWS environment using encrypted and isolated processing. Qurio follows existing role-based permissions, so users can only access data they are authorized to see.
QbDVision AI is designed to support users, not make autonomous GxP decisions. AI-generated outputs remain subject to human review and approval before they become part of a GxP-impacted record. AI capabilities are governed through QbDVision’s validation and credibility framework.
Each AI capability is evaluated against defined CMC tasks and trusted ground-truth data. Metrics can include accuracy, precision, recall, consistency, and other task-specific measures. Evidence is documented through a Credibility Assessment Plan (CAP), within QbDVision’s broader Credibility Assessment Master Plan (CAMP).
QbDVision maintains audit trails for all record interactions. For AI-assisted data creation, the system distinguishes the AI-generated suggestion from the final version reviewed and accepted by the user, providing traceability of the human decision.
AI capabilities require customer authorization before activation. The AI addendum defines how the capabilities are used, relevant restrictions, and the responsibilities associated with AI processing. AI features are not enabled by default and are activated only after the customer confirms readiness.
Model changes are governed through QbDVision’s AI credibility, validation, and change-management processes. Changes are assessed before being introduced to customers, and activation is coordinated through Controlled Capability Activation so customers can align changes with their own validation and change-control requirements.
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