Building scalable softwarepowered by AI when it matters.

The Problem

ML fundamentals are hard to build real intuition for from equations alone you have to watch a model converge to understand what a learning rate or a hidden layer is actually doing.

The Solution

An interactive visualizer with live, parameter-driven simulations that render decision boundaries and training behavior in real time, so every change to the inputs shows its effect immediately.

The Problem

LLM answers are only useful if they can be trusted a plausible-sounding response with no source is a liability, not an answer, especially over private documents.

The Solution

A full-stack RAG platform where every answer is grounded strictly in the uploaded document and returned with page-level citations, backed by a page-aware chunking and embedding pipeline built for retrieval accuracy.

The Problem

Multi-tenant systems usually force a tradeoff: isolate each tenant's data completely, which gets expensive and hard to scale, or share infrastructure and risk data leaking across tenants.

The Solution

A shared-database, shared-schema API with strict row-level tenant isolation and role-based access control, so tenants stay fully isolated without the overhead of separate infrastructure per tenant.