Building Your Own LLM - Pies Studio

Train It. Own It. Keep It.

PIES AI - Private Intelligence Engine

There’s a compelling idea that public AI models are like having a country of geniuses in a data centre, working for your business. And it’s true — they’re extraordinary tools.

But here’s what that framing misses.

Every prompt your employees enter into a public LLM externalises organisational knowledge. Product plans. Customer insights. Pricing strategies. Confidential data. And because public AI providers have unclear data retention and training policies, businesses often have no visibility into where that information goes next.

We’ve seen this before. Shadow IT took years to create problems. Shadow AI is doing it in months.

The deeper issue: public AI platforms aren’t neutral tools. They’re externally controlled platforms with their own commercial incentives. You are not the customer. You are the input.

The alternative is AI sovereignty. A private LLM deployed behind your own firewall gives you the same capabilities — without surrendering your data, your governance, or your competitive advantage to someone else’s platform.

The question isn’t whether to use AI. It’s whether the AI you use works for you — or on you.

The PIES LLM Family

PIES Lite

Parameters 3B

RAM Required 8GB+

TARGET Lightweight testing and evaluation

PIES Dev

Parameters 8B

RAM Required 16GB+

TARGET Development environments and prototyping

PIES Pro

Parameters 70B

RAM Required 64GB+

TARGET Production workloads with reliable output

PIES Expert

Parameters 17B active / 109B total

RAM Required 96GB+

TARGET Mixture-of-Experts architecture for efficiency

PIES Ultra

Parameters 17B active / 400B total

RAM Required 256GB+

TARGET Enterprise-grade, comparable to leading cloud models

Private AI & White-Label Branding

Train your own LLM. Customise every visual element. Your brand, your platform.

LLM Model Selection

5 models from 3B to 400B parameters. Choose based on your hardware and needs.

LoRA Fine-Tuning

Train on your data. Configure epochs, batch size, learning rate. Manage trained adapters.

Theme & Branding

Full colour palette, typography, spacing control. White-label for OEM partners.

Why This Matters For Partners

OEM Partners

White-label the entire platform. Your colours, your fonts, your brand. Customers see your product, not PIES.

Resellers

Demonstrate private AI as the differentiator vs OutSystems and Mendix. No competitor offers self-hosted LLM training.

Distributors

GPU add-on creates an upsell path on every deal. Train-the-trainer on LLM management for your reseller network.

Customisation for Your Specific Domain

Open-source models like LLaMA can be fine-tuned on proprietary datasets, allowing them to perform better in specialised industries or workflows.

For example, an organisation can train a model specifically for:

Legal document analysis
Financial forecasting
Internal knowledge bases
Customer support automation

Fine-tuned domain models often outperform general-purpose models on specialised tasks because they learn the vocabulary, context, and reasoning patterns of that domain.

Full Control Over the Model and Infrastructure

Building your own LLM means you control:

Model architecture
Training process
Deployment infrastructure
Performance optimisation

This allows organisations to optimise the model for specific needs such as low latency, high throughput, or integration with internal systems.

It also enables deeper experimentation and innovation that may not be possible with closed APIs.

Avoid Vendor Lock-In

Using third-party AI services means your application depends on:

Pricing models
API limits
Service availability
Vendor policy changes

Running your own LLM removes that dependency and gives organisations long-term technological independence.

This is particularly important for companies building AI as a core product capability rather than just a feature.

Lower Long-Term Costs at Scale

API-based LLMs usually charge per request or per token. For applications with high volumes of usage, these costs can become substantial.

While self-hosting requires an initial investment in GPUs and infrastructure, the cost per request can become significantly lower over time once the infrastructure is in place.

For example:

AI chatbots with millions of queries
Internal enterprise AI assistants
Automated document processing systems

Faster Response Times (Lower Latency)

When running models locally or within your own cloud environment, requests do not need to travel to external servers.

This reduces latency and enables faster real-time AI interactions, which is critical for:

AI copilots
Real-time automation
AI-powered applications embedded in software platforms.

Ownership of Intellectual Property

When you build and fine-tune your own LLM, the resulting:

Model weights
Training data pipelines
Domain knowledge

become valuable intellectual property assets.

These assets can become a competitive advantage, especially when the model incorporates proprietary company knowledge.

Ability to Integrate Deeply with Internal Systems

Self-hosted LLMs can be tightly integrated with internal infrastructure such as:

Databases
Enterprise applications
Internal APIs
Workflow automation systems

This allows the AI to function as a core internal capability rather than an external service.

Strategic AI Capability

Companies that build their own models gain deeper expertise in:

Machine learning infrastructure
AI optimisation
Domain-specific modelling

This can position the organisation as an AI-driven business, giving it long-term strategic advantages.

When Building Your Own LLM Makes the Most Sense

Building your own LLM is typically beneficial when:

You handle sensitive data
You need deep customization
Your application has very high AI usage
AI is a core part of your product