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The Roadblock to Generative AI Implementation

Generative AI (GenAI) holds immense promise for manufacturers seeking to automate complex processes like custom quoting. Our client, an Iowa based Injection Molder (manufacturing process for producing parts by injecting molten material into a mold), was eager to implement GenAI to enhance their quoting speed and accuracy.

However, their existing process, like many in custom manufacturing, was reliant on partially structured, inconsistent Word documents. This setup created a host of critical problems that actively undermined any sophisticated AI project:

• Data Consistency: The free-form nature of the documents made consistent data extraction and standardization impossible.

• Manual Bottlenecks: Significant manual effort was required to interpret and extract necessary fields, slowing down quoting and introducing errors.

• Impossibility of Training: Without standardized, clean input data, the sophisticated historical context needed to train or fine-tune models on platforms like Amazon Bedrock or Amazon SageMaker was simply unavailable.

Our client had the vision, but their underlying data infrastructure was not in a position to utilize or implement GenAI effectively.

The Value of the Generative AI Assessment

Source Allies partnered with the client to conduct a Generative AI Assessment. The primary goal was to map their business objectives to the correct AI solution. Instead of immediately selecting a large language model (LLM) or a platform, our assessment delivered the essential reality check: data modernization was the non-negotiable prerequisite.

Our findings revealed that any attempt to leverage GenAI on the existing data set would have resulted in inaccurate quotes, model confusion, and a significant waste of time and resources. The assessment identified that the most critical first step was moving away from free-form documents and establishing a system that enforces consistency.

The Proposed Path Forward on AWS

Our comprehensive assessment report provided a clear, phased blueprint for the client to achieve AI readiness. The blueprint begins with a strategic data modernization effort - we estimated that Phases 1 and 2 could be completed within 4 weeks, establishing the foundation needed before tackling complex GenAI projects.

Critical Dependency: Clean Data First

Phases 1 and 2 must be completed and validated before any meaningful implementation of Generative AI (Phase 3) can begin. Attempting to use GenAI on the client’s current unstructured data would lead to inaccurate outputs, model failure, and wasted resources. Clean, consistent data is the fuel for GenAI models.

1. Phase 1: Structured Data Environment: We propose implementing strict data schemas and validation rules to ensure every quote field, from material specifications to tolerances, is captured consistently and adheres to a required format. The data would be centralized in Amazon Simple Storage Service (S3), creating a durable, reliable, and scalable data lake foundation for all future use.
2. Phase 2: Unlocking Analytics: With data now being structured, the client can utilize services like Amazon Redshift (for large-scale analytical processing) or Amazon Athena (for ad-hoc querying directly on S3 data) to enable key business users to quickly query and analyze historical quoting data. This step provides immediate business value (e.g., identifying fastest-selling products or most profitable dimensions) while simultaneously preparing the data sets for eventual model training in Phase 3.
3. Phase 3: Generative AI Implementation: Once the data is standardized and clean, the client can confidently move to high-impact GenAI use cases using their clean data:
   • Intelligent Extraction: Leveraging Amazon Bedrock to analyze incoming, partially structured documents (POs) and instantly extract and summarize key specifications for the structured database.
   • Automated Quoting: Using Amazon SageMaker to train custom models that ingest structured part specifications and historical data, allowing the system to automatically generate highly precise and accurate baseline quotes.

The value of the assessment was in identifying and designing the critical path that makes future GenAI success possible.

Data Integrity is GenAI Readiness

The client assessment serves as a powerful case study for manufacturing leaders everywhere: excitement for Generative AI must be tempered by a disciplined focus on data readiness.

• The Problem: Unstructured data forces human intervention, slows down processes, and renders historical context unusable for AI.

• The Solution: A structured data environment built on the AWS Cloud provides the consistent, clean input necessary for any high-performing GenAI application.

By proposing this path, Source Allies provided our client with a blueprint to avoid costly failures and build a scalable foundation that will truly unlock their future AI ambitions.

Partnering with Source Allies and AWS

Source Allies is an AWS Advanced Consulting Partner with deep expertise in Generative AI, Data Strategy, and Custom Solution Development. We are dedicated to providing the strategic clarity needed to successfully adopt cutting-edge technology. Our vast expertise in Generative AI is designed to map your objectives to the right technology, ensuring you avoid common pitfalls and build sustainable, cloud-native solutions on AWS.

Call to Action

Ready to move beyond unstructured data and ensure your organization is truly ready for Generative AI? Contact Source Allies for an AI Readiness Assessment to identify your most impactful use cases and design the reliable data foundation you need on AWS.