Custom AI Engineering • Private Fine-Tuning & Training

AI That Speaks
Your Language.

Generic AI gives generic answers. Custom-trained models understand your terminology, your workflows, and your data — delivering expert-level results that off-the-shelf AI simply cannot match. Petronella builds, fine-tunes, and deploys custom AI models on your infrastructure, ensuring your proprietary data never leaves your control.

HIPAA • CMMC • SOX • FERPA Compliant Training & Deployment

60%
Less Memory
With LoRA/QLoRA
2x
Faster Training
With Unsloth
100%
On-Premise
Data Retention
23+
Years Cybersecurity
Experience
The Problem

Why Generic AI Falls Short for Enterprises

ChatGPT and Claude are impressive general-purpose tools — but they don’t know your industry, your processes, or your data. That gap between “good enough” and “production-ready” is where custom models deliver.

Generic Models Hallucinate

General-purpose AI invents plausible-sounding but wrong answers when it encounters unfamiliar domain-specific questions. In healthcare, legal, or defense contexts, hallucinations are not just annoying — they are dangerous and potentially non-compliant.

Your Data Trains Their Models

When you use cloud AI APIs, your prompts may be logged, analyzed, and used to improve the provider’s models. Proprietary business processes, trade secrets, and regulated data become training material for a model that serves your competitors.

Per-Token Costs Add Up

API pricing for GPT-4 class models runs $15–$60+ per million tokens. At enterprise scale — thousands of documents processed daily — monthly bills can reach tens of thousands of dollars. A custom model running on your hardware has zero per-token costs after deployment.

Our Process

Custom AI Model Development — From Data to Deployment

Why Custom Models Beat Generic AI

Fine-tuning takes a powerful open-source foundation model — Llama 3.3, Qwen 2.5, Mistral, or others — and trains it further on your specific data. The result is a model that combines the general intelligence of a large language model with deep, specialized knowledge of your domain.

Custom Model Advantages

  • Higher accuracy on domain tasks — a model fine-tuned on your medical records, legal contracts, or engineering specs outperforms GPT-4 on those specific tasks because it has learned your exact terminology and patterns
  • Reduced hallucination — fine-tuned models stick to what they’ve been trained on, dramatically reducing fabricated responses on domain-specific questions
  • Consistent output format — train the model to produce outputs in your exact required format: structured JSON, specific report templates, standardized coding patterns
  • Complete data sovereignty — training happens on your hardware, with your data, under your control. No third party ever sees your proprietary information
  • Zero ongoing API costs — once deployed, the model runs on your infrastructure with no per-token charges, rate limits, or vendor lock-in
The Fine-Tuning Process — Step by Step
Use Case Definition & Data Audit
We work with your team to define exactly what the model needs to do — answer domain questions, classify documents, generate reports, or automate workflows. We then audit your available training data for quality, volume, and compliance readiness.
Data Preparation & Curation
Raw data is cleaned, deduplicated, anonymized (if required for compliance), and formatted into training pairs. We create instruction-response datasets, conversation examples, and evaluation benchmarks specific to your use case.
Base Model Selection
We benchmark multiple open-source foundation models against your task requirements. Factors include parameter count, context window, language support, inference speed, and hardware requirements. The right base model makes fine-tuning dramatically more effective.
Fine-Tuning & Training
Using LoRA or QLoRA adapters with Unsloth for 2x faster training, we fine-tune the base model on your dataset. This happens entirely on your infrastructure or our secure GPU servers — your data never leaves a controlled environment.
Evaluation & Iteration
The fine-tuned model is evaluated against held-out test data and compared to the base model and commercial alternatives. We measure accuracy, latency, hallucination rate, and task-specific metrics. If results don’t meet targets, we iterate on data and training parameters.
Deployment & Integration
The production model is deployed via vLLM or Ollama on your infrastructure with API endpoints that integrate directly into your applications. We configure monitoring, logging, and access controls from day one.
Use Cases — What Custom Models Can Do

Custom fine-tuned models excel at specialized tasks where general-purpose AI falls short. Here are the most impactful use cases we deploy for clients.

Domain-Specific Q&A
Train a model on your internal knowledge base, policies, procedures, and documentation. Employees get instant, accurate answers about company-specific topics instead of searching through SharePoint or asking colleagues.
Document Analysis & Extraction
Automatically extract structured data from contracts, medical records, invoices, compliance documents, or engineering specifications. The model learns your exact document formats and field definitions.
Code Generation & Review
Fine-tune on your codebase, coding standards, and architectural patterns. The model generates code that follows your conventions, uses your internal libraries, and passes your linting rules on the first try.
Compliance Document Generation
Generate HIPAA policies, CMMC SSPs, SOX controls documentation, and audit responses using a model trained on regulatory frameworks and your specific organizational context.
Medical & Clinical AI
Clinical note summarization, medical coding assistance, patient communication drafting, and drug interaction analysis — all processed on HIPAA-compliant infrastructure with no data leaving your environment.
Customer Support Automation
Train on your product documentation, past tickets, and resolution workflows. The model handles tier-1 support with domain expertise that generic chatbots cannot match, escalating only when necessary.
Technology Stack — Enterprise-Grade Fine-Tuning

We use the same tools trusted by leading AI labs and enterprises, configured and hardened for secure, compliant model training.

Unsloth
2x faster fine-tuning with 60% less GPU memory usage
LoRA / QLoRA
Parameter-efficient fine-tuning that adapts models without retraining billions of parameters
vLLM
High-throughput inference engine for production deployment
Ollama
Simplified model management for rapid deployment and testing
NVIDIA Enterprise GPUs
RTX 5090, A100, H100 for training and inference workloads
Hugging Face Transformers
Industry-standard library for model loading, tokenization, and evaluation

All training infrastructure is hardened per NIST 800-53 controls with encryption at rest (AES-256), encryption in transit (TLS 1.3), and comprehensive audit logging.

Why Choose Petronella for Custom AI?

Building a custom AI model requires expertise in both machine learning engineering and enterprise security. We bring both — which is why regulated industries trust us.

  • 23+ years in cybersecurity and compliance — we handle your most sensitive training data with the rigor that HIPAA, CMMC, and SOX demand
  • Own GPU infrastructure — we train and benchmark models on our own NVIDIA-powered clusters before deploying to your environment
  • Security-first architecture — data handling agreements, encrypted pipelines, access controls, and audit trails are built into every training workflow
  • Full lifecycle support — from data preparation through deployment and ongoing model updates, we manage the entire custom AI pipeline
  • No vendor lock-in — you own the trained model weights, the training data, and the deployment infrastructure. Walk away any time with everything you’ve built
FAQ

Frequently Asked Questions

How much data do I need to fine-tune a custom model?
Surprisingly little. With LoRA fine-tuning, as few as 500–1,000 high-quality instruction-response pairs can produce measurable improvement on domain-specific tasks. For production-grade models, we typically work with 5,000–50,000 examples. Quality matters far more than quantity — 1,000 well-curated examples often outperform 100,000 noisy ones.
What is the difference between fine-tuning and RAG?
Fine-tuning changes the model’s weights so it permanently “knows” your domain. RAG (Retrieval Augmented Generation) feeds relevant documents to the model at query time. They serve different purposes: fine-tuning is best for teaching the model new skills, terminology, and output formats. RAG is best for giving the model access to frequently updated information. We often combine both for maximum effectiveness.
How long does custom model development take?
A typical custom model project takes 4–8 weeks from kickoff to production deployment. Data preparation usually takes 1–2 weeks, training and evaluation 1–2 weeks, and deployment with integration 1–2 weeks. Complex projects with multiple use cases or extensive data cleaning may take longer. We provide a detailed timeline during the initial assessment.
Can the model be updated as our data changes?
Yes. We design the training pipeline for repeatability. When your data changes — new products, updated regulations, revised procedures — we can retrain the model with new data and deploy the updated version with minimal downtime. Most clients update their models quarterly, though some high-velocity environments update monthly.
What happens to my data after training is complete?
Your data remains entirely under your control. Training happens on your infrastructure (or our secure, audited GPU servers with a signed data handling agreement). After training, you receive the complete model weights and all training artifacts. If we handled training on our infrastructure, all copies of your data are securely wiped per NIST 800-88 guidelines, and you receive a certificate of destruction.

Related Services

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