Wisecube AI
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Wisecube: Revolutionizing AI Trustworthiness and Insights for Highly Regulated Industries
Wisecube, founded by AI and data experts, we are a startup specializing in Open and Trustworthy AI for highly regulated industries like finance, pharma, and healthcare. Our mission is to revolutionize AI trustworthiness and insights through open-source semantic data solutions. AI has a trust problem. Hallucinations or factual inaccuracies generated by Large Language Models (LLMs) can lead to:
• Stakeholder Confidence Issues: Frequent hallucinations can erode trust in AI technologies.
• Compliance Violations: LLMs can perpetuate harmful stereotypes and social stigmas, potentially leading to discrimination and compliance issues.
• Errors in Critical Decisions: Hallucinations can lead to erroneous decisions in critical fields like medicine, finance, and policy. The Wisecube Solution
Our solution centers around the semantic modeling of data, decomposing LLM responses into semantic triplets to test the factualness of individual knowledge points. This approach provides more informative and precise insights than traditional methods that analyze paragraphs or sentences. Our Products
An open-source Trustworthy AI platform that offers:
• Simplified knowledge graph construction for contextual insights.
• State-of-the-art open-source AI hallucination detection.
• Interactive prompt engineering and active learning features.
• Centralized model registry and streamlined model deployments.
• Open-source API access and unified governance.
• Private Cloud deployment support. Investment Highlights
• Experienced team with a proven track record in AI and data science.
• Addressing a critical need for trustworthy AI solutions in high-stakes industries.
• Innovative technology with a strong focus on open-source collaboration.
• Successful case studies demonstrating the value and impact of our solutions. Website
http://www.wisecube.ai
08/11/2026
Testing Clinical LLMs for Cognitive Bias - Pacific AI Webinar on continuous testing and monitoring of LLMs in healthcare explores accuracy, fairness, safety, and compliance with Pacific AI governance tools.
08/07/2026
Healthcare organizations are deploying AI faster than they can govern it.
A modern health system can have more than 100 AI systems in flight, many introduced by individual departments long before central governance catches up.
Forms-and-templates governance simply doesn't scale. Frontier models also carry measurable bias and reliability gaps that generic benchmarks miss.
The challenge is no longer writing another policy.
It is building an operating model that continuously governs, tests, and monitors AI across the entire portfolio.
That is the focus of our upcoming hands-on workshop with John Snow Labs.
You'll work through the complete healthcare AI governance lifecycle:
• Register AI systems and vendors in Governor
• Automate AI risk assessments and model cards
• Test models for safety, bias, robustness, and cognitive bias in Gatekeeper
• Monitor production systems for accuracy, bias, safety, and drift in Guardian
• Map 250+ laws, regulations, and industry standards using the AI Policy Suite
The workshop includes hands-on exercises on the CHAI-certified Pacific AI platform, concluding with a certification exam and deployment in your own AWS or Azure tenant.
If your organization is preparing to operationalize healthcare AI governance rather than simply document it, this workshop is designed for you.
Registration details: https://www.eventbrite.com/e/healthcare-ai-governance-testing-and-monitoring-tickets-1996959072342
07/12/2026
A leaderboard rank is not a clinical evaluation.
It shows how a model performed on a curated dataset. It does not show whether that model can draft a defensible discharge summary, support a differential diagnosis, or perform a medication calculation inside your EHR.
The gap matters because procurement decisions depend on evidence that extends beyond leaderboard performance.
Three questions to ask vendors before you sign:
• What was this model evaluated on? Real EHR data or exam-style question banks?
• Which clinical tasks were tested, and how do they map to the workflows you are actually deploying?
• Can the evaluation be reproduced inside your environment, or only on the vendor's claims?
If those questions cannot be answered, the governance evidence is incomplete before the model goes live.
Background on MedHELM and why healthcare AI evaluation extends beyond leaderboard rankings:https://pacific.ai/medhelm-and-the-next-phase-of-open-source-medical-ai-evaluation/
Click here to claim your Sponsored Listing.
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Bothell, WA
98012
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