RAGForge.Build your chatbot
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Connect. Ask. Improve.

Your knowledge in.
Useful answers out.

A chatbot built around your sources, your audience, and the questions that matter to your business. Start small, then make it better with every feedback cycle.

Try the Apple demo first
01

A first pilot in under 12 hours

Start with the knowledge
you already trust.

Choose your documents or website pages, define who the chatbot serves, and give it a clear job. RAGForge prepares your content so it can find useful passages when someone asks a question.

  • Customer support, documentation, or team knowledge
  • A focused collection of trusted sources
  • Access and deployment agreed before connecting private content
START WITH A CLEAR JOB

“Help new customers set up our product.”

Target: a working pilot in under 12 hours after sources, access, and scope are agreed.
02

Answers with evidence

Find the right information.
Then answer the question.

The retrieval system looks for relevant passages in your knowledge. The chatbot uses that evidence to compose an answer and cite its sources.

This is retrieval-augmented generation, or RAG. Your content remains the source of truth, and people can open the original material to check the answer.

A question from your userRelevant passages from your sourcesA grounded answer + citations
03

A reviewed improvement within 24 hours

Your feedback guides
what improves next.

Flag an answer that missed the point. Identify a missing source. Show which passage should have been found. That feedback guides changes to retrieval and the next version of your chatbot.

Test the changes against real questions before releasing them. Repeat the cycle as your documents, users, and needs evolve.

A CONTINUOUS IMPROVEMENT CYCLE

Ask. Review. Refine. Repeat.

Target: a reviewed improvement within 24 hours of actionable feedback. Timing depends on the scope of the change.

Designed around your data boundary

In the cloud.
Or entirely on premise.

The right deployment is the one that fits your organisation.

Cloud deployment

Run the chatbot on cloud infrastructure with the access controls and services agreed for your organisation. Make it available to your customers or team.

A shared assistant, wherever people work

100% local deployment

Keep documents, questions, retrieval, and AI inference on your own infrastructure. A fully local setup uses local models as well as local storage, without sending your content to an external AI provider.

On-premise storage, retrieval, and models
Include your deployment needs in your brief

A few straight answers

Before you start.

What can I try on this website today?

Try the interactive Apple example and create a downloadable chatbot brief. The example uses three prepared answers. Live chat, document uploads, builds, and deployments are not yet available through this website.

What do the 12-hour and 24-hour timings cover?

These are pilot targets: a first chatbot in under 12 hours after your sources, access, and scope are agreed, then a reviewed improvement within 24 hours of actionable feedback. Large collections, complex integrations, and on-premise setup can need additional time.

Does the chatbot learn automatically from every message?

The improvement cycle uses reviewed feedback to refine what the system retrieves and how it answers. Changes are tested before release. An individual user message does not silently retrain the model or become a new source of truth.

What does “100% local” mean?

For a fully local deployment, your storage, retrieval, and AI models all run inside your infrastructure. Cloud deployments have a different data boundary. Choose the setup that matches your requirements before connecting private information.

Does a source citation guarantee accuracy?

A citation makes an answer easier to verify. Sources can be incomplete or outdated, and answers can still be wrong. Review the original material when the details matter, and use feedback to improve the next version.

Start with one useful question.

Choose your audience, your knowledge, and where it should run.

Plan your chatbot