Modicus Prime Briefing Note
Axendia was briefed by Taylor Chartier, CEO and Mickey Landkof, VP of Revenue at Modicus Prime, on the company’s AI Management System (AIMS). Modicus Prime is a life sciences software company focused on solving one of the pharmaceutical industry’s biggest emerging challenges: scaling AI compliantly across the enterprise.
Founded in 2020, Modicus Prime traces its origins to work performed with a Top 20 Global Pharmaceutical Company, where the founders gained first-hand experience with the operational and regulatory challenges associated with deploying AI in regulated environments. Since then, the company has expanded its relationships across the pharmaceutical industry while contributing to industry guidance as a co-author of both the ISPE GAMP Guide: Artificial Intelligence and the BioPhorum AI Implementation publication. The company has also participated in Johnson & Johnson’s JLABS ecosystem and recently announced a total of $8 million in venture funding. “We’re very much rooted in improving patient outcomes, patient safety, and product quality. That’s really at the heart of our technology and our goal,” said Chartier.
The Emerging Need for AI Management Systems
Organizations today often manage quality systems, manufacturing execution systems, laboratory information management systems, and enterprise applications through well-established management platforms. AI, however, introduces new governance requirements that traditional systems were not designed to address.
According to Landkof, organizations increasingly struggle to maintain visibility across internally developed AI models, commercial AI applications, and foundation model providers while simultaneously satisfying evolving regulatory expectations. The challenge is compounded by the probabilistic behavior of AI, continuous model evolution, supplier dependencies, and increasing regulatory scrutiny. “Maintaining audit readiness, regardless of the origin of where these AI models live…is really the crux of the matter,” noted Chartier.
Rather than replacing existing enterprise applications, AIMS is designed to provide a governance layer across them. The platform is designed to maintain an enterprise inventory of AI systems, support AI lifecycle documentation, integrate with quality and data science tools, manage AI supplier oversight, and provide traceability required for regulatory inspections.
From AI Validation to Continuous Assurance
A central theme of the briefing was that AI requires organizations to build on Computer Software Assurance (CSA) rather than rely solely on traditional software validation approaches. Although the underlying computerized system continues to follow established CSA principles, AI introduces additional considerations including probabilistic behavior, continuous model evolution, and third-party AI services that require ongoing governance and assurance throughout the AI lifecycle.
As life science companies move AI applications from research into GxP operations, they must demonstrate not only that AI systems are fit for their intended use, but that they remain trustworthy throughout their lifecycle. “That requires continuous evidence…from data quality and risk assessments to model monitoring, change management, and ongoing performance evaluation, rather than relying on a one-time validation exercise,” said Chartier.
That philosophy extends equally to internally developed AI models and commercial AI platforms. As organizations increasingly deploy third-party AI technologies, supplier governance becomes a critical component of AI assurance. “We have positioned AIMS as a common governance framework that organizes technical evidence, lifecycle documentation, supplier assessments, and context-of-use evaluations to help organizations demonstrate regulatory readiness across their entire AI ecosystem,” added Landkof.
The company also emphasized that technology alone is not sufficient. As AI adoption accelerates, organizations must build AI literacy across quality, compliance, IT, and operational teams so personnel can appropriately evaluate AI-generated outputs and maintain confidence in regulated AI systems. “To address this need, we are complementing our solution with workshops and advisory services that help organizations establish governance processes while developing internal competency,” explained Landkof.
An Enterprise Layer Rather than Another AI Application
Rather than positioning AIMS as another standalone application, Landkof described it as an enterprise governance layer that complements and connects existing systems.
As AI becomes embedded across enterprise platforms, organizations face a new challenge: maintaining consistent governance, traceability, and audit readiness across AI applications that span multiple systems, functions, and suppliers.
AIMS is designed to address that challenge by integrating with existing quality systems, operational technologies, and data science tools to provide a unified framework for AI governance. Rather than replacing established enterprise applications, the platform centralizes AI inventories, lifecycle documentation, risk assessments, supplier evidence, and governance workflows into what the company describes as a single source of truth for AI assurance. As Landkof observed, “It could also be pretty overarching…because now you have AI in anything and everything…it’s all-encompassing. It needs to cover everything.”
Chartier noted that this architecture also creates opportunities to collaborate with systems integrators and enterprise software providers. “We are engaged with organizations that already provide manufacturing and automation infrastructure but lack a dedicated AI management layer. Rather than competing with those platforms, AIMS is intended to provide the governance capabilities that sit above them, enabling organizations to scale AI across existing enterprise investments while maintaining compliance and inspection readiness.”
In Brief
Axendia’s newly published market research: AI in Life Sciences, What the Industry is Really Saying, revealed that life sciences organizations have moved beyond asking whether AI should be adopted. The more immediate challenge is establishing governance models that allow AI to be deployed responsibly while satisfying increasingly sophisticated regulatory expectations.
Modicus Prime’s approach reflects this evolution by positioning AI governance as a continuous operational discipline rather than a one-time validation exercise. Its emphasis on AI assurance, lifecycle management, supplier oversight, and continuous audit readiness aligns with broader industry efforts to operationalize AI within existing quality management frameworks instead of treating AI as an isolated technology initiative.
As AI adoption accelerates across regulated environments, organizations will increasingly require governance capabilities that connect quality, compliance, IT, data science, and suppliers into a unified framework. Modicus Prime believes the AI Management System represents that missing enterprise layer, helping regulated organizations build confidence in AI while supporting innovation that ultimately benefits patients.
We will continue to provide updates on Modicus Primes was they become available.
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The opinions and analysis expressed in this post reflect the judgment of Axendia at the time of publication and are subject to change without notice. Information contained in this post is current as of publication date. Information cited is not warranted by Axendia but has been obtained through a valid research methodology. This post is not intended to endorse any company or product and should not be attributed as such.