> ## Documentation Index
> Fetch the complete documentation index at: https://privai.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# 🎯 AI Fine-Tuning

> Adapt artificial intelligence to your business for precise, consistent, and secure responses

![AI Fine-Tuning - Business personalization](https://mintlify.s3.us-west-1.amazonaws.com/privai/images/fine-tuning-hero.jpg)

Your AI trained with your data

# AI Fine-Tuning

Every business has its own language, processes, and requirements.\
**Fine-tuning** consists of training artificial intelligence on your specific data so that it **understands your issues** and **responds like an expert in your field**.\
Result? Your teams save time, your clients are delighted, and your business excels.

***

## Why choose fine-tuning?

> [card] A language adapted to your business
>
> Your AI speaks the vocabulary of your teams and clients, without approximation.

> [card] Precision and reliability
>
> Contextualized responses compliant with your internal standards.

> [card] Guaranteed privacy
>
> All your data remains encrypted and stored on your servers, never elsewhere.

***

## How does it work?

**1. 1. Data preparation**

We select and structure your business examples (procedures, documents, use cases...).

**2. 2. Model training**

The AI learns from these data to better understand your specific needs.

**3. 3. Secure deployment**

The personalized model is installed directly on your infrastructure, without information leakage.

**4. 4. Continuous improvement**

We monitor its performance and adjust the training for maximum efficiency.

***

## Concrete examples by sector

> [tab] Banking & Insurance
>
> * Quick loan application analysis
> * Automated complaint procedures
> * Regulatory compliance assistance

> [tab] Healthcare & Medical
>
> * Preliminary diagnosis assistance (internal use)
> * Patient record summaries
> * Medical recommendation drafting

> [tab] Legal & Compliance
>
> * Civil Code compliant contract generation
> * Automated regulatory monitoring
> * GDPR compliance verification

> [!NOTE]
>
> Each example is built with your own data. The AI remains **under your control** and respects your internal rules.

***

## Measurable benefits

> [card] +85% precision
>
> Adapted and aligned responses with your business standards.

> [card] -90% errors
>
> Reduction of approximations compared to generic AI.

> [card] Real time
>
> Instant responses, even on large databases.

> [card] ROI x3
>
> Proven return on investment in less than 12 months.

***

## In summary

Fine-tuning allows you to transform generic AI into a **virtual expert** in your sector.\
With privAI, you benefit from a **secure, personalized, and scalable** solution that respects your data and integrates directly into your internal tools.

> [!TIP]
>
> The success of fine-tuning relies on the quality of your data: the clearer and more representative your examples, the more performant the AI will be.

***

## Ready to train your AI? 🚀

**1. Assess your needs**

Which processes would benefit from more efficient AI?

**2. Share your business data**

Your documents and use cases feed the learning.

**3. Launch your project**

We deploy the fine-tuned AI in your secure environment.

👉 [Contact our team](https://privai.mintlify.site/contact) to start your fine-tuning project.

***

## Frequently asked questions ❓

> [accordion] How much data is needed for good fine-tuning?
>
> We recommend a minimum of **1000 quality examples**. The more representative your data of your business, the better the results.

> [accordion] How long does the process take?
>
> On average, a complete project takes between **2 and 6 weeks**, depending on data preparation and domain complexity.

> [accordion] Where is my data stored?
>
> All your data remains **on your servers**, end-to-end encrypted. Nothing is transferred externally.

> [accordion] Which sectors can benefit?
>
> Banking, insurance, healthcare, legal, retail, industry…\
> Any organization needing reliable and contextual responses.


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.