AI & Machine Learning

Hugging Face & LLM Deployment Agency

Deploy open-source LLMs for your business — fine-tuning, on-premise or cloud, full data privacy.

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Use Cases

  • Customer support chatbots powered by private knowledge bases
  • Intelligent document processing and structured data extraction
  • Product and content recommendation engines
  • Predictive analytics for business KPIs and churn reduction
  • AI-powered workflow automation eliminating repetitive tasks

What We Build

RAG pipelines with private knowledge bases
Conversational AI assistants and chatbots
Document intelligence and OCR systems
Automated content and copy generation systems
AI-powered fraud detection models
Image and video analysis pipelines

Works Great With

PythonDockerAWSOpenAI

Why Us — Tuplify Studio

Our AI team has delivered production RAG systems, chatbots, and CV pipelines for clients in healthcare, legal, and finance. We focus on measurable business impact, not demos.

Frequently Asked Questions

How does Hugging Face / LLM integrate with my existing product?

Hugging Face / LLM connects via API. We build a thin integration layer between your app and the AI service — no need to rewrite anything. Your users see an AI-powered feature; the underlying stack stays stable.

Is my business data safe with Hugging Face / LLM?

We implement data isolation, optional on-premise deployment, and anonymization pipelines. Your business data never trains public models. For highly regulated industries, we deploy fully on-premise with no external API calls.

How accurate are Hugging Face / LLM-powered features?

Accuracy depends on data quality and use case. For document Q&A and RAG, we consistently achieve 85–95% accuracy with proper chunking and retrieval tuning. We always run an evaluation phase before going live.

Can Hugging Face / LLM work in French and Arabic?

Yes. Modern LLMs (OpenAI, Mistral, Llama 3) perform very well in French and Arabic. We test multilingual quality — including RTL rendering for Arabic — as part of every delivery.

How long does a Hugging Face / LLM integration project take?

A simple chatbot integration takes 2–4 weeks. A production-grade RAG pipeline with document ingestion, retrieval, and UI takes 6–10 weeks. Complex fine-tuning projects take 3–4 months.

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