# IFLAI

> Data-efficient, physics-informed, and highly secure AI models and custom solutions.

IFLAI builds custom AI models that operate under limited data regimes, specializing in medicine (Medtech), high-precision manufacturing, and high-security operations.

- [Full documentation for AI agents](https://iflai.com/llms-full.txt): Comprehensive version of this file with detailed service descriptions, technical approach, and all published research articles.

## Core Pages

- [Home](https://iflai.com/) - Main platform overview. Explains how IFLAI's data-efficient, physics-informed approach solves complex AI problems without requiring massive training datasets.
- [AI for Medtech](https://iflai.com/ai-for-medtech) - High-throughput phenotypic screening, few-shot microscopy analysis, representation learning, and digital microscopy.
- [AI for Manufacturing](https://iflai.com/ai-for-manufacturing) - Real-time edge quality control, anomaly and defect detection, and automated assembly inspection.
- [AI for Security](https://iflai.com/ai-for-security) - Massive-scale object retrieval, zero-trust secure AI architectures, and low-latency edge threat detection.
- [AI Courses & Training](https://iflai.com/courses) - Professional R&D deep learning training and courses taught by authors of the *Deep Learning Crash Course* textbook.
- [Insights & Research](https://iflai.com/insights) - Factual research articles, deep dives into AI benchmarking, annotation costs, inductive biases, and active learning.
- [Contact](https://iflai.com/contact) - Contact form and details (contact@iflai.com) for custom R&D projects.
- [RSS Feed](https://iflai.com/feed.xml) - Machine-readable feed of all published research articles.

## Technical Capabilities & Solutions

### 1. Data Efficiency by Design
- **Inductive Biases**: Embedding physics, geometry, or domain constraints directly into the neural architecture to reduce the search space and require 10x-100x fewer training labels.
- **Self-Supervised Learning**: Utilizing contrastive and masked autoencoders on unlabelled measurement data before applying thin guidance layers for labeling.
- **Active Learning**: Intelligent triage and iterative data selection loops to maximize the training value of every annotated point.

### 2. High-Performance Implementations
- **Edge Deployment**: Compiling models to run natively on low-power sensor microcontrollers and edge hardware.
- **Zero-Trust Security**: Local processing pipelines designed to protect private IP, intellectual property, and high-security compliance without cloud leakage.
- **Integration**: Native interfaces supporting MCP (Model Context Protocol), custom Python/C++ interfaces, and LabVIEW handoffs for laboratory automation.