Upcoming Event: The AI Agent Implementation Framework
Upcoming Event: The AI Agent Implementation Framework
Expect Moore
Camden, NJ
Business Consulting
IAM, S3, Bedrock, EC2, Lambda
Expect Moore Consulting is a business consulting firm specializing in the offshore wind energy sector. The firm needed to transform complex project lifecycle data into actionable intelligence, visual dashboards and AI-powered analytics that could demonstrate the value of data-driven insights to prospective clients. Avahi partnered with Expect Moore to design and deliver a GenAI-powered analytics solution on AWS, combining interactive data visualization with a conversational AI interface powered by Amazon Bedrock. The solution was delivered within a three-week engagement, enabling Expect Moore to present data-driven capabilities to offshore wind developers and demonstrate the potential of AI-augmented consulting services.
Expect Moore Consulting, led by CEO Brian Moore, is a business consulting firm based in Camden, New Jersey, that serves organizations in the offshore wind energy sector. The firm advises clients across the full offshore wind project lifecycle, from feasibility studies and siting and permitting through manufacturing, construction, operations, and decommissioning. Offshore wind is a capital-intensive, data-rich industry where projects span a decade or more and involve complex supply chains, regulatory requirements, and stakeholder coordination. Expect Moore helps its clients navigate this complexity with better data intelligence and decision support.
Expect Moore Consulting had accumulated extensive data across the offshore wind project lifecycle but lacked the tools to present that data in a compelling, interactive format. The firm needed to move beyond static spreadsheets and slide decks to demonstrate the value of data-driven insights when engaging prospective clients, offshore wind developers evaluating AI and analytics capabilities.
The challenge was multifaceted. Clean, structured data was not immediately available and required collaborative preparation with the client. The breadth of the full five-phase lifecycle, covering Feasibility, Siting and Permitting, Manufacturing and Procurement, Construction and Installation, Operations and Maintenance, and Decommissioning, was too large to address in a single engagement. And the firm needed a working demonstration quickly, while establishing a modular foundation that could expand to cover all phases over time.
Without a solution, Expect Moore risked continuing to rely on manual data presentations that undersold its analytical depth, missing opportunities to differentiate itself in a competitive consulting market where data fluency and AI capabilities are increasingly expected.
Expect Moore Consulting was already operating within AWS, making it the natural cloud platform for this engagement. Amazon Bedrock was a key driver. It provided managed access to leading foundation models without requiring the firm to build or maintain AI infrastructure, allowing the team to focus on delivering business value rather than managing model deployment.
AWS’s ecosystem of services, including Amazon EC2, AWS Lambda, Amazon S3, and AWS IAM, provided the compute, serverless processing, storage, and security needed to build a complete solution within a lean architecture. This combination enabled a rapid path from concept to working product within the three-week engagement window.
AWS introduced Expect Moore Consulting to Avahi based on the firm’s need for a partner with deep GenAI implementation expertise on AWS. As an AWS Premier Consulting Partner, Avahi brought a proven track record of delivering AI-powered solutions on AWS and the ability to move quickly from concept to working solution.
Avahi’s approach, built on collaborative scoping, iterative delivery, and a focus on modular architecture, aligned well with Expect Moore’s need to validate the concept rapidly while building toward a broader, multi-phase vision. The team’s ability to present clear architectural trade-offs and facilitate informed client decisions was a key factor in the engagement’s success.
Avahi partnered with Expect Moore CEO Brian Moore to design and deliver a GenAI-powered analytics solution focused on two critical offshore wind lifecycle phases: Siting and Permitting and Manufacturing and Procurement.
The engagement began with a discovery session where the Avahi team learned the full scope of the offshore wind project lifecycle. Together with the client, the team narrowed the initial focus to the two phases with the richest available data, while designing the architecture for future expansion to the remaining three phases.
The team evaluated two architectural approaches. The first used Amazon QuickSight for dashboard visualization, which offered rapid deployment but limited UI/UX flexibility, restricted personal branding options, and imposed per-user licensing costs. The second used a custom React single-page application hosted on Amazon EC2, providing full control over the user interface and more cost-effective scalability. Through a collaborative review with the client, the team selected the React-based architecture for its flexibility and lower total cost of ownership.
Data preparation was a joint effort. The Avahi team worked with Brian Moore to clean, structure, and organize the source data, consisting of CSV and Excel files covering offshore wind project metrics, before uploading it to Amazon S3. The Amazon EC2 backend reads from S3 and exposes structured KPI metrics to the React frontend via a REST API, keeping the architecture lean and purpose-appropriate.
The frontend integrates an interactive dashboard with KPIs, including wind turbines per state, top manufacturers by turbine count, total installation capacity per year, and onshore versus offshore distribution, alongside an Amazon Bedrock-powered chat panel in a unified layout. The AI chatbot, powered by an Amazon Bedrock foundation model selected for its analytical reasoning capability and output stability, answers natural language questions grounded in the dashboard data and its underlying sources. Queries outside that data boundary return a graceful non-response, ensuring outputs remain trustworthy.
The serverless AI layer uses AWS Lambda to process chat requests, invoke Amazon Bedrock for model inference, and write full conversation logs to Amazon S3 for audit and future model improvement. AWS IAM governs access across all services using least-privilege roles and policies, including EC2 instance profiles, Lambda execution roles, and S3 bucket policies.
– React single-page application with interactive dashboard and integrated AI chat panel
– REST API backend hosted on Amazon EC2, serving data and chat endpoints
– Amazon Bedrock integration for AI-powered natural language querying
– AWS Lambda serverless processing layer for chat request handling and conversation logging
– Amazon S3 storage configuration for source data, KPI files, and conversation logs
– AWS IAM security model with least-privilege access controls across all services
– Architecture documentation with diagrams for both evaluated and selected approaches
– Knowledge transfer documentation for independent operation and extension
– Full AWS account access and backend ownership transferred to the client
Avahi delivered a functional GenAI-powered analytics solution within a three-week engagement window, enabling Expect Moore Consulting to demonstrate AI-augmented data capabilities to prospective offshore wind clients. The modular, five-layer architecture is designed for straightforward expansion to the remaining three lifecycle phases, and the comprehensive knowledge transfer documentation enables the Expect Moore team to operate, extend, and evolve the solution independently.
– Delivered within the three-week engagement timeline with no budget overrun
– Successfully narrowed scope from five lifecycle phases to two for initial viability, with modular architecture supporting future expansion
– Architecture pivot from QuickSight to React-based solution executed based on client preference without scope creep
– Full AWS account ownership and backend access transferred to the client upon completion
– Five-star customer satisfaction rating with formal client sign-off
– AI chatbot integration was added ahead of the original plan following positive prototype feedback
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