Sentient Shadows: Proactive Home Intruder Prediction

Inspired by the predictive analysis of 'I, Robot' and the data-driven, fragmented information recall of 'Memento', 'Sentient Shadows' operates on the principle of identifying pre-cursors to security breaches. Instead of reacting to an intrusion, it seeks to predict it. Leveraging publicly available, anonymized data akin to how an 'Insurance Offers' scraper gathers information, 'Sentient Shadows' will focus on niche, low-cost data sources. These could include:

1. Anomalous Online Activity: Monitoring for unusual spikes in local online searches related to security system vulnerabilities, specific neighborhood crime statistics, or even unusual patterns of social media activity in the vicinity (e.g., repeated geotagged posts in quick succession from multiple, unconnected accounts). This draws from the idea of piecing together disparate data points as in 'Memento'.
2. Environmental Anomaly Detection: Utilizing cheap, internet-connected sensors (e.g., basic motion detectors, sound sensors, or even smart plug energy consumption data from neighboring properties if accessible and anonymized) to identify subtle deviations from normal patterns. For instance, a sudden, unexplained cessation of activity in a normally bustling adjacent property could be a flag.
3. Open-Source Intelligence (OSINT) Correlation: Analyzing publicly available data feeds for patterns that might indicate casing or reconnaissance activities.

How it Works:

The system would function as a background AI agent. Users would provide a highly anonymized digital 'fingerprint' of their home's typical digital and environmental baseline (e.g., typical internet traffic patterns, energy consumption profiles). The AI then continuously scans a curated set of low-cost, publicly accessible data streams. When anomalies are detected that correlate with known pre-intrusion indicators, 'Sentient Shadows' generates a probabilistic risk assessment and alerts the user via a secure, encrypted channel.

Niche & Low-Cost:
The niche lies in its -proactive prediction- rather than reactive detection. It focuses on low-cost, readily available data, avoiding expensive hardware installations. Implementation can be done with a Raspberry Pi or even a cloud-based server for data processing and analysis.

High Earning Potential:
Subscription-based service for homeowners, offering tiered levels of prediction accuracy and alert sensitivity. Partnerships with insurance companies, who could offer discounts to users of such proactive systems, thereby creating a revenue stream from both consumers and B2B clients. The 'high earning potential' stems from the unique value proposition of preventing crime before it happens, a highly desirable outcome for security-conscious individuals and businesses.