AI / LLM
Bridging Data & Intelligence
Teaching a computer to read through piles of documents so it can answer your questions correctly using only facts, without making things up.
Each word becomes a list of numbers — similar words get similar numbers
Computers don't read words — each word becomes a list of numbers called a vector, learned from huge amounts of text. Words used in similar ways get similar numbers. To search, the AI ranks every word by how close its vector is to yours.
Click a word to search from it — or switch to Word Math to see KING − MAN + WOMAN ≈ QUEEN.
These are real GloVe vectors — 50 numbers each. Search ranks every word by how close its numbers are.
Normal computers are too slow for AI
An AI model has to do millions of tiny calculations per word. A normal computer chip (CPU) does them one by one. A graphics chip (GPU) does thousands in parallel — that's why every AI lab runs on GPUs.
Flip the switch. The CPU does one thing at a time. The GPU does thousands at once.
The AI looks things up before answering
Plain AI guesses from what it was trained on. Sometimes it makes things up. With RAG, the AI checks your files first and answers from what it actually finds.
Click a question. Watch it search the knowledge base, pull the matching facts, and answer from only those.
Teach the AI to speak your industry's language
Out of the box, AI speaks general English. To make it useful in medicine, law, or your specific business, you train it on examples from that field. The brain stays the same. The accent changes.
Pick a dataset. Watch the model's vocabulary shift — same brain, different specialty.
Stop bad inputs going in. Stop private data going out.
AI will follow any instruction it's given — including ones from attackers hidden inside user input. Guardrails sit on both sides of the model. They block bad prompts and remove private data before it leaks out.
Send a good or a bad input. The guardrail lets safe prompts through — and slams shut to block an attack.
