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Summary
Engineering Partners faced inefficiencies due to manual processes for retrieving and referencing past Statements of Work (SOWs) when drafting new ones. To streamline this, Cloud Life Consulting designed and implemented a scalable AWS cloud foundation. On top of this infrastructure, a GenAI-augmented SOW query tool was deployed, enabling internal users to interactively search historical SOWs and generate new drafts with similar specifications and context—dramatically improving productivity and consistency.
“The GenAI tool from Cloud Life turned hours of manual SOW work into minutes. It’s a game changer for how we scale project delivery.” — Erik Kolb, Senior Civil Engineer, Engineering Partners
Challenge
Engineering Partners relied heavily on manually searching and referencing previous Statements of Work (SOWs) to draft new ones, often requiring staff to sift through disjointed documents and formats. This time-consuming process not only slowed down project initiation but also led to inconsistencies in scope definition and formatting. As the firm scaled operations and aimed to standardize processes, the lack of a streamlined, intelligent method to query historical SOWs became a bottleneck—limiting productivity and increasing the risk of errors or oversight in new project documentation.
Solution
To address these challenges, Cloud Life Consulting deployed a production-grade AWS cloud foundation for Engineering Partners, enabling a secure, scalable, and modern infrastructure. Built on top of this foundation, Cloud Life delivered a GenAI-powered SOW query and generation tool that leverages the following AWS services:
- Amazon Textract to extract text from past SOW documents
- Amazon Comprehend to analyze language structure and key insights
- Amazon Rekognition for object and form analysis where applicable
- Amazon Kendra for advanced document search capabilities
- Amazon Q for conversational, natural language interactions with internal users
- Amazon Bedrock with a Knowledge Base to orchestrate foundation models for tailored SOW drafting
This solution empowers Engineering Partners’ teams to rapidly retrieve, reference, and generate new SOWs that align with historical standards—cutting down on manual effort while increasing speed and accuracy.
Results
With the AWS cloud foundation and GenAI-augmented SOW query tool in place, Engineering Partners achieved major gains in operational efficiency. Manual SOW referencing was replaced with intelligent, context-aware search and auto-generation capabilities. As a result, internal users reduced SOW drafting time by over 50%, often completing drafts in minutes instead of hours. The solution also improved standardization and reduced errors in documentation. Beyond immediate productivity improvements, the scalable infrastructure positions Engineering Partners for future cloud-native development, analytics, and AI expansion.
Partner Expertise
Cloud Life Consulting is a cloud-native consulting firm specializing in designing, deploying, and managing AWS-based solutions tailored to each client’s strategic goals. With nearly a decade of experience guiding organizations through digital transformation, Cloud Life brings deep expertise in foundational cloud architecture, data infrastructure, and cutting-edge AI/ML services. For Engineering Partners, Cloud Life delivered not only the core AWS setup but also orchestrated GenAI tools using services like Amazon Kendra, Textract, Bedrock, and Amazon Q—enabling practical innovation that directly supports business productivity.
Customer Overview
Engineering Partners Inc. (EPI), founded in 1985 and headquartered in San Diego, CA, is a women-owned, minority-certified engineering consulting firm with over 100 professionals. Specializing in utility infrastructure—such as electric distribution, power systems, substations, renewable energy, and wildfire mitigation—EPI serves a diverse mix of clients including municipal, industrial, and commercial organizations across California and beyond. The firm is dedicated to innovation, reliability, and sustainability, with a strong emphasis on enhancing grid resilience and supporting the clean‑energy transition.
