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Caperbit

RAG model development

AI that reads your documents before it answers

We build RAG assistants that use your own business knowledge, so customers and staff get answers specific to your company.

  1. 01

    Collect

    We gather your PDFs, FAQs, catalogs, policies and support manuals.

  2. 02

    Index

    Content is split into chunks and stored in a vector database for search by meaning.

  3. 03

    Retrieve

    Each question pulls back the most relevant passages from your content.

  4. 04

    Answer

    The model replies from those passages and can hand off when it lacks the information.

Where it helps

Common starting points

  • Customer support

    Answer product, policy and order questions on WhatsApp or your website.

  • Sales assistant

    Help prospects with catalog and pricing questions, then capture the lead.

  • Internal knowledge

    Let staff search SOPs and manuals by asking a plain question.

Client Need Form • Direct Project Inquiry

Tell us what your AI should know

Share your document types, channels, and the questions you want answered.

Technology

The stack behind our AI systems

AI & Knowledge

Models and retrieval for assistants that answer from your data.

  • OpenAI
  • Gemini
  • Claude
  • RAG pipelines
  • Vector databases
  • Python

WhatsApp & Messaging

Conversation channels and the workflows around them.

  • Meta WhatsApp Cloud API
  • WhatsApp Flows
  • Webhooks
  • CRM integrations

Backend & Data

APIs and databases that keep products reliable.

  • Node.js
  • Laravel (PHP)
  • PostgreSQL
  • MySQL
  • REST & GraphQL

Cloud & Interfaces

Infrastructure and front ends that put it in front of users.

  • AWS
  • Google Cloud
  • Docker
  • Kubernetes
  • React & Next.js
  • Flutter

RAG FAQs

What is a RAG model?

RAG stands for Retrieval-Augmented Generation. Before answering, the AI searches your own content and uses what it finds as context, rather than relying only on its general training.

Do you train a new AI model on our data?

Usually not. A RAG assistant keeps your content in a searchable index and passes relevant passages to an existing model such as OpenAI, Gemini or Claude.

Will the assistant always be correct?

No AI is perfect. RAG reduces wrong answers by grounding replies in your content, and we add handoff to a person for questions it cannot answer confidently.

Can it work on WhatsApp?

Yes. We connect RAG assistants to the Meta WhatsApp Cloud API, and also to websites and internal tools.