# Building Supercharger: How Rocket Close optimized title operations with agentic AI

> **Platform:** [SPIDITS AI](https://spidits.com/) — Real-Time AI News & Market Intelligence  
> **Published:** 2026-06-11T19:40:33.000Z  
> **Category:** PRODUCT_LAUNCH  
> **Impact Score:** 140/100  
> **Primary Source:** [AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/extract-data-with-on-demand-and-batch-pipelines-dynamically)  
> **Canonical Citation:** [https://spidits.com/timeline/building-supercharger-how-rocket-close-optimized-title-operations-with-agentic-ai](https://spidits.com/timeline/building-supercharger-how-rocket-close-optimized-title-operations-with-agentic-ai)

## Executive Summary
In this post, we explore how Rocket Close built a solution using Strands Agents, large language models (LLMs), Amazon Bedrock, Amazon Bedrock Knowledge Bases...

## Referenced Coverage & Sources
- **[AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/extract-data-with-on-demand-and-batch-pipelines-dynamically)**: Extract Data with On-demand and Batch Pipelines Dynamically — _This post demonstrates an intelligent document processing pipeline that consists of both on-demand inference and batch inference options on Amazon Bedrock to..._

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*Synthesized by SPIDITS AI Market Intelligence Desk. Track live AI news, model releases, and funding: [https://spidits.com](https://spidits.com)*