Andis Accelerates Product Knowledge and Support with Generative AI on AWS

Customer

The Challenge

Andis wanted faster, more consistent access to product and support knowledge without adding more manual content work.

• - Product specifications, FAQs, training materials, imagery, support content, and operational information were distributed across multiple repositories and business systems.
• - Marketing and support teams spent significant time updating content, locating approved information, and preparing responses to repetitive product and usage questions.
• - As product launches, campaigns, and digital channels expanded, Andis needed a governed way to make current information easier to find without increasing manual workload at the same rate.

Why Tactical Edge AI

Andis needed more than a chatbot. Tactical Edge AI brought an AWS-native delivery model that connected business discovery, model and retrieval design, security, Responsible AI, production readiness, and operational handoff.

That approach gave Andis a path from fragmented knowledge to a governed production service, while keeping the architecture modular enough to add new knowledge sources and workflows over time.

The Solution

Tactical Edge AI designed a managed RAG architecture centered on Amazon Bedrock. Amazon Bedrock provides managed foundation-model access for natural-language interactions, while Amazon Bedrock Knowledge Bases connects approved enterprise content with the model so responses can be grounded in current Andis information.

Amazon S3 stores source documents and knowledge artifacts. Amazon OpenSearch Service provides semantic vector search so the application can retrieve information based on meaning rather than keyword matching alone. AWS Lambda orchestrates workflows and integrations, and Amazon CloudWatch provides centralized logs, metrics, and operational visibility.

The implementation uses IAM, encryption at rest and in transit, role-based access, and documented Responsible AI controls. Tactical Edge AI validated the architecture through security reviews, functional testing, user acceptance, and production-readiness activities before customer acceptance.

Results

• - Representative product and support queries fell from a 15-minute baseline to 5 minutes, beating the target of 7 minutes or less.
• - Representative support-response workflows fell from 20 minutes to 8 minutes, beating the target of 10 minutes or less.
• - The production architecture gave Andis a reusable foundation for new knowledge sources, additional departments, and future AI-assisted workflows without a complete redesign.

Benefits

Next Steps

Andis can extend the same governed knowledge architecture to additional departments, repositories, and customer-facing workflows while retaining access controls, monitoring, and Responsible AI controls.