AiWerkz

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AI-Powered Document Sharing. Reinvented.

Convert your documents to become portable, private, and easy to chat with.

Explore an AiWerkz Chat Package Create a Simple AiWerkz Chat Package

Save Your Frequently Used Prompts

We created this simple tool because we got tired or retyping our often used prompts.

Why AiWerkz?

An AI chat package is a tailored set of data from AiWerkz that you can paste into AI systems like ChatGPT or Google Gemini to engage with a specific topic.

Better Privacy

No intermediaries. Your customers chat directly with documents using their AI accounts like ChatGPT or Gemini.

Lower Costs

No complex infrastructure. No ongoing subscription costs. Shift costs to users' own AI accounts.

Simplicity

We specialize in packaging documents for AI. Copy, paste, and chat. No technical skills required.

One minute to set up a document for chat!

Step 1

You have content or a document. Or several.

Step 2

Enter the content into AiWerkz using a form.

Step 3

Share the link or a Chat Package via email, web page, etc.

Step 4

People paste the Chat Package into ChatGPT and then chat with your content.

We are able to make this process so simple and easy because we take your document and package it in a way where the user can simply paste the information into ChatGPT (or other AI systems) or if the AI System (like ChatGPT 4o) can accept a URL then the user only needs to paste the link and then start chatting on the content.

And if you have multiple related documents, we can tie them all together.

The trade off for all this simplicity is that the user will have to paste information in to the AI system that they are using. But there are some great benefits to this:

Get your users chatting with your content very quickly.
Your users chats stay private between them and the AI system that they want to use.
Super simple and cost effective. No servers to set up. No complicated configuration. Very inexpensive.

Rethinking RAG with Chat Packages

If you have documents that you want your users to be able to chat with you have likely come across RAG. We are an alternative.

Retrieval-Augmented Generation (RAG) integrates with Large Language Models (LLMs) to enable AI chat systems to utilize external documents that are not part of the model's training data. Typically, RAG involves the following steps:

Document Repository: A collection of documents is stored in a repository.
Vectorization: These documents are converted into vectors using a process called vectorization, often utilizing embeddings generated by a machine learning model.
Query Handling: When a user submits a query through a customer-facing web app, the query is also vectorized.
Search Function: The vectorized query is then used to search the document repository to find the closest matches based on vector similarity.
Retrieval and Generation: The relevant documents are retrieved and provided as context to the LLM, which then generates a response using both the retrieved documents and its own trained knowledge.

Along with cost and complexity, another downside to RAG systems is that the operator of the RAG system potentially has access to all information shared in the user chat session, which raises privacy and security concerns.

User Privacy AiWerkz, using AI Chat Packages, removes the operator of the RAG system from having access to the chat session. We do this by packaging content in a special Chat Package that the user can paste directly into their AI system of choice (like ChatGPT). So, one may consider an ICE Package as a sort of improvised RAG.

RAG done Easier, Cheaper and Faster AiWerkz Chat Packages are the fastest, easiest and least expensive way to share "chatable" documents with your customers.