Accessibility Mode

Advancing Accessible AI Infrastructure

Engineering serverless edge-compute and browser-native ML systems to construct scalable, transparent, and private AI infrastructure.

Mission & Impact

Empowering Education, driving Enterprise Innovation, and establishing transparent Public Sector Knowledge Systems through zero-latency edge deployments.

Public Sector

Lowering the barrier to civic data through transparent, instant-access Retrieval-Augmented Generation interfaces.

Enterprise Innovation

Reducing foundational AI operational costs by 80% through deterministic serverless resource architecture.

Accessible Education

Guaranteeing student privacy by executing powerful language models directly entirely on edge devices.

Pillar I Showcase

Public Sector Knowledge

Traditional civic data architectures are opaque and siloed. By deploying vectorized indexing at the network edge, we create zero-latency, transparent access to public records for all citizens.

CivicRAG Sandbox

An interactive demonstration of Retrieval-Augmented Generation for accessible public datasets. Type a query to visualize the sub-second edge retrieval process.

1. Embed Query
2. Vector Search
3. LLM Generation
Pillar II Showcase

Enterprise AI Innovation

Monolithic cloud architectures bottleneck enterprise AI adoption. Our proposed serverless paradigm optimizes resource utilization, proving scalability while drastically reducing operational expenditure.

⚙️ ETL AI Engine & Cost Calculator

Visualizing the automated data pipeline required for Enterprise AI applications, alongside real-time infrastructure cost analysis comparing legacy monolithic architectures to the proposed serverless edge model.

Infrastructure Scale

Legacy Cloud Cluster
Always-on VM Instances
$450/mo
Serverless Edge Model
Proposed Architecture
$85/mo
Efficiency Gain: 81% Cost Reduction
1
Extract Unstructured Data
2
Transform & Embed
3
Load to Target Subsystem
Pillar III Showcase

Accessible Education

Student privacy is the ultimate bottleneck for EdTech AI. By leveraging WebAssembly and quantized models, we deploy powerful educational assistants directly to the student's browser—guaranteeing 100% data privacy.

🧠 Local-Model Tutor

A privacy-first educational tool. This component simulates a quantized language model running entirely in the browser via WebAssembly, simplifying complex text without ever sending student data to a cloud server.

Awaiting execution...
Privacy: Local Only
Payload Sent: 0 bytes