ProMaster (Photographic Research Organization)
Shelton, Connecticut
Photographic and Video Accessories
Amazon Bedrock, Amazon S3, AWS Lambda, Amazon API Gateway, Amazon EC2
ProMaster, a leading global brand of photographic and video accessories, wanted to streamline its document processing by automating the extraction of structured data from CSV and XLSX files. The company needed a solution that could accurately identify relevant fields, convert them into a standardized format, and make the data easily accessible. Avahi, a premier AWS partner, designed and implemented an Al-powered pipeline leveraging AWS services to automate structured data extraction, reduce manual effort, and improve data accuracy and accessibility.
ProMaster is a global provider of photographic and video accessories, serving creators, storytellers, and adventurers worldwide. The company is committed to delivering innovative products that support professional and enthusiast photographers in capturing high-quality images and videos.
ProMaster’s growing dataset from sales and inventory reports required significant manual processing to extract, organize, and analyze key metrics such as SKU and sales volume. This manual process was timeconsuming, prone to errors, and hindered timely decision-making. Without automation, data inconsistencies and delays in reporting could negatively impact operational efficiency, product availability, and business planning.
ProMaster selected AWS for its scalability, broad set of Al and data processing tools, and proven reliability. AWS’s managed services such as Amazon Bedrock provided access to state-of-the-art foundation models without the operational overhead of managing Al infrastructure, enabling rapid prototyping and deployment. Additionally, AWS’s secure storage and API capabilities ensured that extracted data could be efficiently processed and accessed across systems.
ProMaster partnered with Avahi because of its deep expertise in AWS cloud architecture and Al-powered automation. As a premier AWS partner, Avahi demonstrated a strong track record in delivering tailored Al solutions that combine technical innovation with measurable business outcomes. Avahi’s ability to quickly design, build, and validate an Al-driven pipeline within strict timelines was critical to meeting ProMaster’s operational needs.
Avahi implemented a three-phase project to design, develop, and validate an Al-powered structured extraction pipeline.
Phase 1 – Discovery & Planning: Conducted a project kickoff to align objectives, identify document types, and define key fields for extraction. Selected Al models for structured parsing and documented success criteria.
Phase 2 – Design & Development: Acquired and preprocessed sample data, implemented Al-based parsing using Amazon Bedrock foundation models, and built a structured extraction pipeline. Data was standardized into JSON format and stored in Amazon S3. APIs were developed using Amazon API Gateway and AWS Lambda for retrieval and integration.
Phase 3 – Quality Check & Handover: Validated extracted data against ground truth values, performed performance testing, and refined the pipeline. Delivered technical documentation and provided a knowledge transfer session to ProMaster’s technical team.
The architecture leveraged Amazon Bedrock for generative Al and model management, Amazon S3 for storage, AWS Lambda for automation triggers, Amazon API Gateway for scalable API delivery, and Amazon EC2 for processing multiple requests.
The Al-powered pipeline significantly reduced manual data extraction efforts, improving both accuracy and speed. ProMaster can now process CSV and XLSX files in a fraction of the time, ensuring faster access to actionable data. The automation improves operational efficiency, supports timely decision-making, and reduces the risk of human error.
Founder, Bravo Foxtrot
