Building an AI-Powered Energy Management Platform in 30 Days
Case Study

August 1, 2024

Building an AI-Powered Energy Management Platform in 30 Days

Business Context

A venture-backed startup needed a smart home energy management platform for a product demonstration at an industry event in 30 days. The platform would connect to smart electrical breakers manufactured by a third party, allowing users to monitor and control their energy consumption.

The client managed the project for their executive, operating under fixed budget and timeline constraints. Missing the event deadline was not an option - the demonstration was critical for investor relations and market validation.

Problems

  • Non-negotiable 30-day deadline. The product demonstration was scheduled for a specific industry event. Delay would mean missing the market opportunity entirely.

  • Third-party hardware dependency. The platform required integration with a smart breaker manufacturer's API. Documentation accuracy and API behavior were unknown until tested.

  • Mid-project scope expansion. Two weeks into development, the client's investors requested AI-powered energy optimization capabilities. This functionality had not been part of the original specification.

  • Budget constraints. The client managed budget for their executive. Cost overruns would create significant problems for their position.

Solution

Discovery and Third-Party Integration

Before implementation began, I conducted technical discovery on the manufacturer's API. This involved reviewing documentation, scheduling calls with the manufacturer's technical team, and performing live testing with the client's actual hardware.

This testing revealed discrepancies between the documentation and actual API behavior. Several documented endpoints had different response structures than specified. Others had undocumented rate limits.

I mapped the verified behavior patterns before writing any integration code. This prevented mid-implementation refactoring when documentation proved incorrect.

Fixed-Scope Architecture

I structured the project as independent scopes of work, each with defined completion criteria, timeline, and cost. This approach enabled parallel development across multiple contractors without centralized coordination overhead.

Each scope was fully specified before implementation:

  • User authentication and device pairing
  • Real-time energy monitoring dashboard
  • Device control interface
  • Data analytics and reporting
  • Third-party API integration layer

Because specifications were complete, contractors could work independently without daily synchronization meetings. Integration points were defined upfront, reducing coordination complexity.

AI Integration Pivot

Two weeks before the deadline, the client needed to add AI-powered energy optimization. The requirement was vague initially - the investors wanted "AI capabilities" but hadn't defined what that meant operationally.

I worked with the client to clarify the requirement:

  • Input: Energy consumption data from IoT devices, time-of-day patterns, environmental factors
  • Output: Automated adjustments to device settings to optimize energy efficiency
  • Success criteria: Demonstrable energy reduction during the product demo

Given the constrained timeline and remaining budget, I proposed building a proof of concept first. This isolated the risk - if AI integration proved infeasible, we'd know before committing the full budget.

I built the POC using the existing energy data collected during development:

  • Created a minimal dataset showing consumption patterns
  • Tested several optimization approaches using OpenAI's API
  • Validated that the AI could generate reasonable device adjustment recommendations
  • Designed integration architecture that wouldn't require refactoring existing code

The POC validated feasibility within one week. I then integrated the AI component into the application using Azure Function Apps triggered by Azure Service Bus events.

Technical Architecture

Frontend (Retool):

I decided on Retool to accelerate dashboard development. The low-code approach enabled rapid iteration on UI designs without custom frontend work. Retool's built-in components handled authentication, data visualization, and device control interfaces.

Backend (Azure Function Apps + TypeScript):

Serverless functions handled business logic:

  • Device state management
  • AI optimization triggers
  • Third-party API integration
  • Event processing from IoT devices

Messaging (Azure Service Bus):

Asynchronous event processing decoupled IoT device events from API operations. Device state changes triggered events that the AI optimization service consumed.

Data (MongoDB + Mongo Charts):

MongoDB stored time-series energy consumption data. Mongo Charts provided embedded analytics visualizations in the Retool dashboard without custom charting code.

AI (OpenAI API):

ChatGPT 3.5 processed energy consumption patterns and generated device optimization recommendations. The model received context about device capabilities and historical usage to inform decisions.

Stack Decisions

  • Retool: Eliminated custom frontend development. Built complete dashboard in days instead of weeks.
  • TypeScript: Type safety caught integration errors during development. Essential for coordinating parallel contractor work.
  • Azure Function Apps: Serverless execution eliminated infrastructure management. Pay-per-use pricing fit budget constraints.
  • Azure Service Bus: Decoupled device events from processing logic. Provided natural retry mechanism for transient failures.
  • MongoDB: Flexible schema suited IoT data's variable structure. Time-series capabilities optimized for consumption data queries.
  • OpenAI API: Pre-trained model eliminated custom ML training. Rapid integration timeline required existing AI capabilities.

Results

  • Delivered within 30-day deadline. Platform was functional and demonstrated at the scheduled industry event.

  • AI integration completed mid-project without timeline extension or budget overrun using POC validation approach.

  • Third-party integration succeeded due to early API verification preventing mid-implementation delays.

Conclusion

This project delivered production functionality under a 30-day hard deadline while navigating third-party API unknowns and mid-project scope expansion. Early validation and POC methodology enabled delivery within the fixed window.

Key approaches:

  • Early API verification prevented mid-implementation delays
  • Proof-of-concept validated AI feasibility before budget commitment
  • Low-code tooling (Retool) accelerated dashboard development

For IoT platforms with third-party dependencies and fixed deadlines, this demonstrates the value of early technical validation and POC methodology for late requirements.