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Artificial Intelligence & LLM

Integrate the power of LLMs directly into your business tools. No generic ChatGPT wrapper — custom solutions integrated into your workflows, with your data hosted on your infrastructure.

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Generative AI is transforming how businesses process information. But a generic chatbot isn't enough: you need solutions integrated into your processes, configured for your data, and compliant with your privacy requirements.

INYSTER designs custom AI systems: RAG (Retrieval-Augmented Generation) to query your internal documents, business chatbots connected to your databases, automated document processing pipelines, and more.

What's included

RAG systems to query your internal documents
Business chatbots connected to your data
Automated document processing (extraction, classification)
LLM API integration (OpenAI, Anthropic, open-source models)
Hosting on your infrastructure (GDPR compliance)

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RAG — Retrieval-Augmented Generation

RAG allows an LLM to answer questions based on your internal documents: contracts, technical documentation, knowledge bases, manuals. The model doesn't guess — it cites its sources. Result: reliable, contextualized, and traceable answers.

LLM Integration

We connect OpenAI, Anthropic or open-source models (Mistral, Llama) to your existing tools. Automatic email summarization, report generation, ticket classification — the use cases are numerous.

AI & Automation

By combining n8n and LLMs, we create intelligent workflows: data extraction from PDF invoices, automatic classification of incoming documents, sentiment analysis on customer feedback.

Internal GPT & Chatbots

An AI assistant connected to your internal data, accessible to your teams. Ideal for HR knowledge bases, internal technical support, or business document drafting assistance.

Tech stack

Technologies used

PythonLangChainOpenAI APIAnthropic APIpgvectorn8n

Realistic project scenarios

AI serving your business

Internal GPT for Law Firm

RAG system allowing lawyers to query 10,000+ legal documents in natural language. Sourced answers with article and case law citations.

Before Lawyers manually searching through thousands of legal documents

After RAG system: natural language search with source citations

Estimated gain ~Document research reduced from hours to minutes

Technical Knowledge Base RAG

RAG system for a SaaS vendor: support and development teams query technical docs, changelogs and resolved tickets in natural language.

Before Support and dev teams manually searching technical documentation

After RAG-powered knowledge base queryable in natural language

Estimated gain ~Ticket resolution time significantly reduced

E-commerce Customer Support Chatbot

AI assistant integrated into the e-commerce site, connected to product catalog, FAQ and return policies. Automatic escalation to human agent when needed.

Before Customer support overwhelmed by repetitive inquiries

After AI assistant integrated into site, trained on catalog and FAQ

Estimated gain ~Majority of simple requests resolved without human intervention

Estimates based on typical scenarios. Results vary by context.

Typical feedback

What our clients generally say

The internal RAG system lets our teams find relevant information in seconds across thousands of documents. It's a real production system, with source citations and access control.

Illustrative testimonial — based on typical client feedback

FAQ

Frequently asked questions

What is RAG?

RAG (Retrieval-Augmented Generation) is a technique that allows an AI model to answer questions based on your internal documents. The model first searches for relevant passages in your knowledge base, then generates a sourced answer.

Can we use open-source models instead of OpenAI?

Yes. We work with Mistral, Llama and other open-source models that can be hosted entirely on your infrastructure. Your data never leaves your servers.

How is data privacy ensured?

We prioritize hosting on your infrastructure. When using external APIs (OpenAI, Anthropic), data is transmitted encrypted and is not used to train models. We advise on the architecture best suited to your GDPR requirements.

Why does INYSTER recommend RAG over model modification?

RAG keeps the standard model but provides relevant context with each query via your documents. It's faster to implement, less expensive, and easier to maintain — you just update the source documents. It's the most pragmatic approach for the majority of business use cases.

Not sure which option?

Tell us about your needs in 30 minutes. We'll guide you toward the right approach.

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