Deconstructing AI Products

From complex business ideas to manageable ML components

"We can't directly process audio and come up with answers; there's an intermediary step."

— John, Data Scientist

The Challenge

Business ideas often start as monolithic AI solutions, but technical implementation requires decomposition into manageable components.

Initial Vision

"Build a Siri for real estate: users speak to our app and receive curated property results like a human broker."

Technical Reality

What seems like one AI product actually requires multiple specialized components:

  • Speech-to-Text conversion
  • Intent detection & classification
  • Query processing & results retrieval

Deconstruction Methodology

Breaking down complex AI products into manageable components using a systematic approach.

1

Analyze Input Data

Identify raw data sources (audio, text, images)

2

Define Output Goals

Specify desired actions or results

3

Identify Transformation Steps

Break into sequential processing stages

4

Map to Technical Solutions

Assign ML or traditional software to each step

Voice Assistant Deconstruction

1. Speech-to-Text

Convert audio input to written text

Deep Learning

2. Intent Detection

Classify text into specific actions

Text Classification

3. Action Execution

Perform requested action using software

Traditional Engineering

Complex Case Study: Self-Driving Car

Autonomous vehicles demonstrate how complex AI systems are decomposed into specialized components.

Perception System

Identifies objects in the vehicle's environment

Computer Vision Object Detection
Pedestrians
Vehicles
Signs

Prediction System

Forecasts behavior of other entities

Behavior Modeling

Routing System

Determines optimal path to destination

Traditional Algorithms

Decision Making

Combines ML and rule-based systems for safe navigation

ML Predictions Rule-Based Logic Safety Protocols

Control System

Executes physical vehicle actions

Traditional Engineering

Core Insights & Recommendations

Modularity is Key

Complex AI systems are rarely monolithic. Break them into specialized components with clear interfaces.

ML + Traditional Engineering

Combine machine learning where patterns are complex with traditional software for deterministic rules.

Adjust Project Scope

Sometimes simplifying inputs (e.g., text instead of voice) can dramatically reduce complexity.

Data-First Approach

Start with available data and required outputs, then design transformations in between.

Key Takeaways

80%

Complex AI projects require decomposition

3-5

Typical components after decomposition

40%

Reduction in complexity through scope adjustment