## Overview Clario is a clinical trials company with over 50 years of experience providing endpoint data solutions for the life sciences industry, having supported over 710 novel drug regulatory approvals across more than 100 countries. This case study describes their implementation of a generative AI solution for automating the review of Clinical Outcome Assessment (COA) interviews, which are critical instruments for evaluating treatment efficacy and safety in clinical trials, particularly in studies of psychosis, anxiety, and mood disorders. These assessments often determine trial success or failure, making data quality and reliability paramount. The core business challenge was that traditional manual review of audio-video recordings was complex, time-consuming, and introduced variability through subjective human assessment. Different expert reviewers would naturally vary in their evaluations, leading to inconsistent quality assessments. The company needed a scalable solution that could standardize reviews across multi-lingual data globally while maintaining regulatory compliance and meeting strict privacy requirements under HIPAA. ## Production Architecture and Infrastructure Clario's production architecture represents a sophisticated, enterprise-grade LLMOps implementation leveraging multiple AWS services in an integrated workflow. The system begins with secure data ingestion where COA interview recordings (audio and video files) are collected on-premises and uploaded via AWS Direct Connect with encryption in transit to Amazon S3, where they are stored with server-side object-level encryption to maintain HIPAA compliance. The orchestration layer is built using a custom AI Orchestration Engine running on Amazon Elastic Kubernetes Service (Amazon EKS), providing the scalability and flexibility needed for complex multi-step workflows. This choice of Kubernetes for orchestration indicates a mature approach to production ML systems, allowing for containerized microservices, horizontal scaling, and robust deployment management. The architecture follows a clear separation of concerns with distinct processing stages. Audio processing leverages a custom speaker diarization model deployed on Amazon SageMaker to identify unique speakers in interviews based on voice characteristics. This is a critical preprocessing step that enables the system to attribute dialogue to specific roles (interviewer versus participant), which is essential for quality assessment. For transcription, Clario selected the Whisper model from the Amazon Bedrock Marketplace, demonstrating a pragmatic approach to model selection by leveraging a proven open-source model adapted for their needs rather than building from scratch. The multi-lingual capabilities of Whisper address their global operations requirement. The transcriptions are annotated with speaker information and timecodes, creating structured data that can be analyzed systematically. ## Vector Database and Semantic Retrieval Strategy A particularly sophisticated aspect of the implementation is the semantic retrieval architecture using Amazon OpenSearch as a vector database. The system addresses a fundamental challenge in LLM operations: context window limitations when processing lengthy interviews. Transcribed interview segments are vectorized using Amazon Titan Text Embeddings v2 model and stored in OpenSearch, enabling semantic search capabilities. The retrieval strategy demonstrates advanced RAG (Retrieval-Augmented Generation) patterns with several thoughtful design decisions: - **Overlapping windows** ensure contextual information is not lost at segment boundaries, addressing the common problem of losing context when chunking long documents - **Targeted semantic searches** identify specific dialogue segments relevant to each assessment criterion, enabling efficient retrieval without processing entire interviews - **Hierarchical approach** preserves both local conversational flow and global interview context through interview-level summaries and speaker role metadata - **Rolling context windows** can be dynamically assembled when evaluating criteria that span multiple segments This architecture allows the system to efficiently handle multiple queries against the same interview data while maintaining contextual relationships throughout conversations. The metadata preservation (timestamps, speaker roles, positional context) enables traceability back to source material, which is critical for regulatory compliance and validation. ## Agentic AI Implementation The core intelligence of the system comes from a graph-based agent system running on Amazon EKS that orchestrates automated COA interview review. This represents a modern agentic AI pattern where the system executes a predefined workflow with decision-making capabilities rather than simple sequential processing. The agent implements a multi-step workflow that: - Retrieves the assessment's structured interview guide from configuration - Loads the corresponding central review checklist criteria - Systematically queries Amazon OpenSearch to extract relevant interview segments - Traverses predefined decision nodes in the graph structure to compare interview responses against standardized assessment criteria - Identifies gaps or inconsistencies - Generates structured findings with supporting evidence citations The use of Anthropic Claude 3.7 Sonnet from Amazon Bedrock for the classification and evaluation tasks shows strategic model selection. Claude is particularly well-suited for tasks requiring nuanced understanding, following complex instructions, and producing structured outputs. The agent uses the LLM to classify speech segments as interviewer or participant and to determine if each interview turn meets quality criteria. The graph-based approach provides several operational advantages. It creates a transparent, auditable workflow where each step's logic is explicit rather than hidden in model weights. This is crucial for regulatory environments where decision-making processes must be explainable. The predefined graph structure also enables version control of the evaluation logic separate from the underlying models, allowing process updates without necessarily retraining or changing models. ## Model Selection and Amazon Bedrock Strategy Clario's choice of Amazon Bedrock as their LLM platform represents a pragmatic approach to LLMOps that prioritizes operational efficiency and compliance over building custom infrastructure. The case study explicitly outlines several advantages this provided: Amazon Bedrock eliminated infrastructure management overhead, allowing Clario to focus on their domain expertise in clinical research rather than ML infrastructure. The serverless architecture enabled rapid prototyping and deployment, which was likely critical for getting a complex system into production. Access to multiple foundation models through a single API allowed comparative evaluation and model selection for specific tasks—evidenced by their use of different models for transcription (Whisper) versus reasoning and classification (Claude). The built-in compliance features, including data governance, audit trails, and native support for regulatory requirements, directly addressed their HIPAA and clinical trial regulatory needs. Enterprise security features like advanced encryption, access controls, and data residency options reduced the complexity of building a compliant system from scratch. The continuous model improvement aspect—automatic access to model updates without migration complexity—provides a sustainable path forward as foundation models continue to evolve. This strategy reflects a mature understanding that for many production use cases, managed services that handle the undifferentiated heavy lifting of model serving, security, and compliance allow teams to focus on the unique value they provide through domain-specific orchestration and evaluation logic. ## Data Flow and Integration The end-to-end data flow demonstrates careful attention to production requirements. After the AI Orchestration Engine compiles the overall review of an interview, results are persisted in Amazon Relational Database Service (Amazon RDS), providing structured storage with ACID guarantees for audit trails and reporting. This choice of a traditional RDBMS alongside the vector database shows pragmatic architecture—using the right tool for each job rather than forcing everything into a single data store. The system exposes results through a REST API using Amazon API Gateway endpoints, enabling integration with existing Clario client applications. This API-first approach facilitates gradual adoption and integration with existing workflows rather than requiring wholesale replacement of existing systems. The architecture maintains encryption in transit and at rest throughout the pipeline, with proper access controls and audit logging capabilities essential for healthcare applications. The use of AWS Direct Connect for on-premises data upload shows attention to network-level security and reliable data transfer for potentially large video files. ## Quality Assurance and Validation One of the most important aspects of this LLMOps implementation is the rigorous approach to quality assurance and validation. The case study explicitly discusses lessons learned around responsible AI development, particularly the discovery that LLMs would occasionally generate plausible-sounding but inaccurate summaries—a phenomenon often called "hallucination." This led Clario to implement a validation system where AI outputs are cross-checked against source documents for factual accuracy before human review. This represents a mature understanding that LLMs in production cannot be trusted blindly, especially in high-stakes domains like clinical trials. The human-in-the-loop validation serves as a quality gate and also provides feedback data that could be used for continuous improvement. Clario adopted a rigorous model evaluation process including comparative studies on custom datasets across multiple models and configurations. This suggests they developed domain-specific evaluation datasets—likely golden standard reviews by expert human reviewers—against which to benchmark model performance. This is essential for validating that automated reviews match or exceed human quality standards. The continuous evaluation approach indicates an operational maturity where model performance is monitored in production, not just validated once during development. This is critical for detecting model drift, identifying edge cases, and ensuring consistent quality as the system processes diverse interview types and languages. ## Operational Impact and Results The results claimed by the solution are substantial, though the case study notes these are from "initial implementation" and show "promise," suggesting appropriate caution about treating these as fully proven outcomes versus early indicators: **Operational Efficiency**: The potential to decrease manual review effort by over 90% represents a dramatic operational improvement. However, readers should note this likely doesn't mean a 90% reduction in human involvement overall—the validation and oversight processes still require human expertise, but focused on higher-value quality assurance rather than initial review. **Quality Improvements**: The system enables up to 100% data coverage through automated review versus human-only review of smaller subsets. This is perhaps the most significant quality benefit—traditional approaches sample recordings for spot-checking due to resource constraints, meaning many interviews never receive detailed review. Automated review of every interview could catch quality issues that would otherwise go undetected. The highly targeted interventions focusing only on raters and sites requiring remediation could improve the efficiency of quality management. **Business Impact**: The potential to shorten turnaround time from weeks to hours represents a fundamental change in operational tempo. Faster feedback enables more rapid interventions during ongoing trials, potentially preventing accumulation of low-quality data. Enhanced data reliability for regulatory submissions and reduced risk of study failure directly address the core business value proposition. Improved scalability of clinical trial operations enables the company to handle more trials or larger trials without proportional increases in review staff. However, it's important to note that these benefits are presented as "potential" outcomes, and the case study appropriately uses qualifying language like "might be enabled" and "up to" rather than claiming guaranteed results. In production LLMOps, actual results often depend on factors like data quality variability, edge cases encountered in real deployments, and the specific interview types and languages processed. ## Regulatory and Compliance Considerations The healthcare and clinical trials context imposes stringent regulatory requirements that significantly influenced the architecture. The system must comply with HIPAA for protecting patient health information, FDA regulations for clinical trial data integrity, and potentially GxP (Good Clinical Practice, Good Laboratory Practice) standards depending on specific use cases. The architecture demonstrates several compliance-oriented design choices: encryption at rest and in transit throughout the data pipeline, audit trail capabilities through persisting results in RDS, access controls and authentication through AWS IAM and API Gateway, data residency controls available through AWS regional deployments, and the ability to maintain data lineage from raw recordings through to final assessments. The fact-checking validation layer addresses regulatory requirements for accuracy and traceability in clinical trial data. The structured output format with evidence citations enables auditors to trace automated assessments back to specific interview segments, maintaining the chain of evidence required for regulatory submissions. ## Scalability and Cloud-Native Design The use of Kubernetes (Amazon EKS) for orchestration, S3 for object storage, serverless APIs through API Gateway, and managed services like Bedrock and OpenSearch creates a cloud-native architecture that can scale horizontally. This is critical given Clario's global operations across over 100 countries with thousands of deployments. The containerized microservices approach on EKS allows independent scaling of different components—transcription, embedding generation, semantic search, and LLM inference can scale based on their specific resource requirements and bottlenecks. The serverless components like API Gateway and potentially Lambda (though not explicitly mentioned) provide automatic scaling for API endpoints. The separation between the vector database (OpenSearch) and relational database (RDS) allows optimizing each for its workload. OpenSearch can scale for semantic search queries while RDS provides transactional consistency for recording results and supporting reporting queries. ## Technical Challenges and Tradeoffs While the case study presents a successful implementation, there are several technical challenges and tradeoffs that readers should consider: **Complexity vs. Capability**: The multi-component architecture provides powerful capabilities but introduces operational complexity. Managing Kubernetes clusters, vector databases, multiple model APIs, and ensuring consistent orchestration requires significant engineering expertise and operational overhead. **Cost Structure**: The case study doesn't discuss costs, but the architecture includes several potentially expensive components. LLM API calls to Bedrock (especially for Claude 3.7 Sonnet on potentially lengthy interviews), vector database operations, continuous Kubernetes cluster operation, and data storage all contribute to operational costs. The claimed 90% reduction in manual review effort should be weighed against these infrastructure costs. **Latency Considerations**: The multi-step workflow—diarization, transcription, embedding, semantic search, multiple LLM calls for classification and evaluation—introduces latency. The case study mentions "near real-time" processing and reducing turnaround from weeks to hours, but this still represents significant processing time compared to sub-second inference. This is likely acceptable for the use case but represents a tradeoff between comprehensive analysis and speed. **Model Dependency Risk**: Relying on third-party models through Bedrock (Whisper, Claude) creates dependency on model availability, pricing changes, and model version updates. While Bedrock provides "continuous model improvements," model updates could potentially change behavior in ways that require revalidation for regulatory purposes. **Multi-Lingual Complexity**: While Whisper provides multi-lingual transcription, the case study doesn't detail how language affects the downstream LLM-based evaluation. Different languages may have different error rates, and cultural differences in interview styles could affect assessment quality. This represents an ongoing evaluation challenge for global deployments. ## Future Directions and Extensibility The case study mentions Clario is "exploring additional use cases in neuroscience studies that rely on clinical interviews for evaluating the safety and efficacy of treatments." This suggests the architecture was designed with extensibility in mind. The configurable interview guides and review checklists, graph-based agent structure, and separation of domain logic from model operations should facilitate adaptation to new assessment types. The modular architecture allows independent improvement of components—better transcription models, more efficient embedding models, or more capable reasoning models can be swapped in without redesigning the entire system. The vector database approach enables efficient reprocessing if evaluation criteria change without re-transcribing audio. ## Overall Assessment This case study represents a sophisticated production LLMOps implementation that appropriately addresses the unique challenges of healthcare and clinical trials contexts. The architecture demonstrates several best practices: using managed services to reduce operational overhead, implementing RAG patterns to overcome context limitations, employing agentic workflows for complex multi-step processes, maintaining audit trails and traceability for compliance, and implementing validation layers to catch model errors. The careful attention to responsible AI practices, including explicit validation against source documents and continuous model evaluation, shows operational maturity. The explicit discussion of discovering hallucination issues and implementing countermeasures demonstrates transparency about real-world LLM challenges. However, readers should maintain appropriate skepticism about the claimed benefits, which are presented as "potential" outcomes from an "initial implementation." The real proof of success will come from sustained operation at scale across diverse interview types, languages, and regulatory jurisdictions. The complexity of the architecture also raises questions about the operational expertise required to maintain and evolve the system. Overall, this case study provides valuable insights into building production LLM systems for regulated industries, demonstrating both the powerful capabilities enabled by modern LLMOps practices and the careful engineering required to deploy them responsibly in high-stakes contexts.