OPLOG, a technology-driven fulfillment company powered by AI and robotics, processes millions of items monthly across Türkiye, the United Kingdom, and Germany for major brands and global marketplaces. Operating a customer-agnostic fulfillment model where multiple brands share warehouse infrastructure, workers, and autonomous robots, OPLOG faced a challenge common to many B2B organizations: fragmented business data across systems resulted in delayed insights and manual reporting that consumed hours of productive time daily.

To address this challenge, OPLOG built a production-ready business intelligence (BI) system using AI agents deployed on Amazon Bedrock AgentCore. The solution processes business transactions autonomously, delivering real-time intelligence across sales pipeline management, data quality enforcement, and prospect research. The results demonstrate measurable business impact: 35% reduction in sales cycles, 91% improvement in CRM data completeness, and 98% reduction in manual research time.

In this post, we show you how OPLOG developed three AI agents using the Strands Agents SDK, deployed them to Amazon Bedrock AgentCore, and integrated Amazon Bedrock with Anthropic’s Claude Sonnet and Amazon Bedrock Knowledge Bases for Retrieval(RAG). We describe the architecture, implementation approach, and business outcomes that demonstrate how AI agents can transform BI operations.

OPLOG’s rapid growth created operational complexity that traditional BI systems couldn’t address. The company’s data existed across multiple disconnected systems: Hubspot CRM contained sales pipeline information, communication systems stored customer conversations, Microsoft Teams held communication context, and Databricks warehouses maintained operational metrics. Each system operated independently, creating data silos that prevented comprehensive BI.

The fragmentation created specific operational pain points. 2 accessing reports from different systems, synthesizing information, and preparing updates. This manual process meant insights arrived too late—weekly reports missed 60% of opportunities because deals had already progressed or stalled by the time analysis was complete. CRM data quality suffered as sales representatives, overwhelmed by manual data entry requirements, entered information inconsistently. Operations teams detected issues hours after they occurred, forcing reactive responses rather than proactive intervention.

OPLOG quantified significant operational costs from fragmented BI—including lost opportunities from delayed insights, manual reporting overhead consuming productive time, inconsistent data quality impacting decisions, and reactive operations forcing inefficient responses. The company needed a solution that could autonomously process data across the systems, deliver real-time intelligence, and remove manual reporting overhead while maintaining data quality and enabling proactive decision-making.