By 2025, the marketing organization was being squeezed between two opposing forces. On one side, a fast-moving competitor was winning share through sheer content velocity, pushing more campaign assets, more frequently, across more channels. On the other, the client’s own content lifecycle was constrained by exactly the controls that made it trustworthy. Every claim, every dealer ad, every product summary required substantiation from an approved claims matrix, then a full Regulatory and Legal review cycle before publication.
Off-the-shelf generative AI tools could not bridge this gap. Generic Copilot-style assistants pulled indiscriminately from any document a user could access. Thus, surfacing unapproved language, expired claims, or competitor-adjacent phrasing that would never survive a regulatory review. The risk was not just inaccuracy. It was regulatory exposure on FDA-regulated medical devices.
The marketing team faced a structural choice. Continue producing content manually and lose the velocity battle to competitors with looser content standards. Or compress drafting cycles by an order of magnitude, without compromising the claim-substantiation discipline that medical device marketing demands. Incremental tooling would not close the gap, nor would onboarding more content talent. The team needed a new content production model.
The client needed a content generation chatbot, designed as a claims-grounded system that was architecturally incapable of producing marketing assets referencing unapproved claims.
That distinction shaped every downstream decision. Rather than fine-tuning a foundation model on client content, which would have introduced hallucination risk and a lengthy retraining cycle for every claim update, the team designed a Retrieval-Augmented Generation architecture anchored on a single source of truth: the enterprise claims matrix held in the regulated quality management system. Every generated asset would be assembled from claims actively approved at the moment of generation, never from model memory.
Zimetrics, working as an AWS Advanced-Tier Partner with deep healthcare and life sciences experience, layered four design principles on top:
The result is an assistant that behaves, in the words of the marketing team, like a fully briefed marketing operator who has never broken a compliance rule.
Selenium-based RPA bots, scheduled through Amazon EventBridge, continuously ingest content from up to fifteen distinct sources: the enterprise claims matrix (approved claims), SharePoint project folders (in-progress claims and brand-approved layout templates), the public client web properties, competitor sites, and shared S3 buckets. Databricks pipelines handle structured enterprise data, while Python-based ETL on AWS Lambda performs cleansing, deduplication, and knowledge distillation — transforming raw scraped material into structured, retrieval-ready chunks.
All distilled knowledge is embedded and stored in Amazon OpenSearch as the vector store, partitioned so that approved claims, in-progress claims, brand templates, and competitive intelligence are retrieved through separate, governed channels. This partitioning is what makes the system structurally claims-safe: a generation request for an externally bound asset can only retrieve from the approved-claims partition.
Content generation workflows are built in Python with LangGraph, with foundation models hosted on Amazon Bedrock and custom model components on Amazon SageMaker, accessed privately via AWS PrivateLink. A typical request triggers a multi-step agent: parse intent, resolve product, query approved claims, fetch matching layout template, assemble draft, run brand-voice checks, and return.
The conversational interface is an Angular front-end served through Amazon CloudFront and Cognito-protected single sign-on, with Spring Boot and FastAPI microservices running on Amazon EKS Fargate behind an Application Load Balancer. Conversation state and metadata are persisted in Amazon DocumentDB; file artifacts in S3 and EFS.
Encryption at rest through AWS KMS using AES-256, and TLS 1.2 or higher in transit. IAM with least-privilege role-based access control. Full audit trails via AWS CloudTrail, X-Ray, and CloudWatch streamed into OpenSearch Dashboards. The architecture is HIPAA– and GDPR-ready, aligned with the client’s enterprise security standards, and reviewed and approved through a formal Architecture Review Board.
We’re a medical device company, so every marketing claim must be substantiated and approved by Regulatory and Legal. Generic AI tools couldn’t operate inside those guardrails. What Zimetrics built behaves like a fully trained marketing operator who has read every approved claim, and it gives us content velocity without ever compromising on compliance.