AWS implemented Account Plan Pulse as a comprehensive LLMOps solution to transform their internal sales account planning process. The system addresses the challenges that emerged as AWS scaled globally, including inconsistent plan quality across regions and industries, resource-intensive manual review processes, and knowledge silos that prevented effective cross-team collaboration. The core technical implementation leverages Amazon Bedrock as the foundation model service, enabling AWS to build a production-ready generative AI system that processes account plans at enterprise scale. The solution architecture follows a structured pipeline approach that begins with ingestion from their CRM system into Amazon S3 buckets through scheduled batch processing. This approach ensures continuous analysis of the most current account plan information without overwhelming the system with redundant processing. The preprocessing layer operates at two distinct levels to optimize efficiency and quality. The first layer implements an ETL flow that organizes required files for processing, while the second layer performs input validation just before model calls. A particularly sophisticated aspect of their preprocessing is the use of plan modification timestamps to process only recently updated documents, significantly reducing computational overhead and costs. The system extracts text content from HTML fields and generates structured metadata for each document, transforming everything into a standardized Parquet format stored in S3. The Amazon Bedrock integration performs two primary functions that demonstrate sophisticated LLMOps practices. First, the system evaluates account plans against 10 business-critical categories through 27 specific questions with tailored control prompts, creating what they call an Account Plan Readiness Index. This automated evaluation provides specific improvement recommendations rather than just scoring. Second, the system extracts and synthesizes patterns across plans to identify customer strategic focus and market trends that might otherwise remain isolated in individual documents. A critical aspect of their LLMOps implementation is the use of structured output prompting with schema constraints. This ensures consistent formatting that integrates seamlessly with their reporting tools and downstream systems. The asynchronous batch processing architecture allows evaluation and summarization workloads to operate independently, improving system resilience and scalability. One of the most technically sophisticated aspects of their production implementation is their approach to handling the non-deterministic nature of LLMs. AWS developed a statistical framework using Coefficient of Variation (CoV) analysis across multiple model runs on the same inputs. This framework addresses three critical challenges: output variability from identical inputs, the evolving nature of account plans throughout customer relationships, and different evaluation priorities across AWS teams based on customer industry and business needs. The CoV analysis serves as a correction factor to address data dispersion, calculated at the evaluated question level. This scientific approach allows them to measure and stabilize output variability, establish clear thresholds for selective manual reviews, and detect performance shifts that require system recalibration. Account plans falling within established confidence thresholds proceed automatically through the system, while those outside thresholds are flagged for manual review. Their validation framework demonstrates comprehensive LLMOps practices aligned with security best practices. They implement input and output validations following the OWASP Top 10 for Large Language Model Applications. Input validation includes necessary guardrails and prompt validation, while output validation ensures results are structured and constrained to expected responses. They've also implemented automated quality and compliance checks against established business rules, additional review processes for outputs that don't meet quality thresholds, and feedback mechanisms that improve system accuracy over time. The dynamic threshold weighting system represents another sophisticated LLMOps capability, allowing evaluations to align with organizational priorities by assigning different weights to criteria based on business impact. This enables customized thresholds across different account types, applying different evaluation parameters to enterprise accounts versus mid-market accounts. These business thresholds undergo periodic review with sales leadership and adjustment based on feedback, ensuring the AI evaluations remain relevant while maintaining quality standards. From a technical infrastructure perspective, the solution utilizes Amazon S3 for secure storage of all processed account plans and insights, implements a daily run cadence that refreshes insights and enables progress tracking, and provides interactive dashboards offering both executive summaries and detailed plan views. This infrastructure supports the scale requirements of a global sales organization while maintaining security and compliance standards. The production engineering aspects of their implementation address key challenges in enterprise LLM deployment. They've built reliability into their AI evaluations through statistical frameworks, handled the evolving nature of business documents through dynamic processing capabilities, and created flexibility for different organizational needs through configurable evaluation criteria. Their approach to measuring and controlling output variability through statistical analysis represents a mature approach to LLMOps that goes beyond basic prompt engineering. Looking forward, AWS plans to enhance Pulse's capabilities by connecting strategic planning with sales activities and customer outcomes, analyzing account plan narratives to identify new opportunities, and leveraging new Amazon Bedrock capabilities including flows for workflow orchestration, Guardrails for enhanced safety, agentic frameworks, and Strands Agents with Amazon Bedrock AgentCore for more dynamic processing flows. The case study demonstrates several key LLMOps principles in action: systematic approaches to handling model non-determinism, comprehensive validation frameworks, integration with existing business workflows, statistical methods for quality control, and iterative improvement based on feedback. The reported results of 37% improvement in plan quality and 52% reduction in processing time suggest that their LLMOps implementation has delivered significant business value while maintaining the reliability and consistency required for enterprise sales processes.