AI Integration with Claude and RAG
AI that knows your company's data, answers customers correctly and gets work done for your team. Solutions that run in real businesses, built on Claude models and a RAG architecture.
What is Claude AI & RAG and why we use it
AI integration is no longer about adding a chat box. Set up properly, it becomes a colleague that instantly answers customers' service, price and booking questions, prepares quotes and content for the team and summarises CRM data. At Saybes we develop company-specific assistants and automations using Anthropic's Claude models.
RAG (Retrieval-Augmented Generation) is the method of grounding the model's answers in your documents, product catalogue and database: when a question arrives, the relevant content is retrieved, given to the model as context, and the answer is generated from that context. So the assistant doesn't make things up; it knows your price list, opening hours and processes. For data privacy we work with access controls and data processing agreements.
The 'Mati' assistant on Mat Garaj's website answers service and price questions and guides visitors to a booking; the assistant inside MatCRM summarises customer history and drafts quotes; the assistant on Saybes's own site points visitors to the right service. We adapt that experience to different scenarios, from WhatsApp bots to content-production automation.
Six concrete reasons
Our technology choices are driven by performance, security and sustainability, not by fashion.
Answers grounded in company data
With RAG the assistant answers from your documents and database; the risk of fabricated information is minimised.
Sales and support automation
24/7 first contact, qualified lead capture, booking guidance; the team's load drops.
Internal assistants
Quote drafts, customer summaries, content and email generation; it works inside the CRM and the panel.
Multi-channel
Web chat, WhatsApp Cloud API, in-panel assistant; the same knowledge base on every channel.
Security and control
Access permissions, logging and auditing, data processing agreements; restricted context for sensitive data.
Measurement
Answer quality, resolution rate and booking conversion are measured; the assistant is improved continuously.
Claude AI & RAG in our projects
How we position it in real projects and which work it carries.
Website assistant
A chat assistant that answers service, price and booking questions and converts them into enquiries (the Mati example).
WhatsApp bot
Automatic replies, booking and order flows via the official WhatsApp Cloud API; hand-over to a human when needed.
CRM and panel assistant
Customer summaries, quote and email drafts, insights from reports (the MatCRM example).
Content and marketing automation
Product descriptions, blog drafts, ad-copy variations; generation in line with the brand voice.
OUR TECHNOLOGY APPROACH“For AI to be useful it first has to know the right data; RAG provides exactly that.”
The tools we use alongside it
An open, documented stack any team you choose can take over.
Services where we use this technology
Technology is a means, not an end; on the service pages we explain what we build and how.
Technical questions
Book a technical call →What if the assistant gives wrong information?+
With RAG, answers are grounded in your content; on topics it doesn't know it redirects. Accuracy is measured with test sets before launch and improved continuously.
Is our data used to train the model?+
No. With API usage your data is not used for model training; we also set rules that keep sensitive data out of the context.
How is the cost calculated?+
Usage-based API cost + development. A typical web assistant's monthly API cost is low; we budget based on estimated usage.
How good is the Turkish?+
Claude models are strong in Turkish; we also define guidelines and examples for a tone and wording that fit the brand.
Other technologies in our stack
Let’s talk about your Claude AI & RAG project.
Let us review your current system and define the right architecture and roadmap together.