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LLM Fine-Tuning Services

Make foundation models speak your language. We fine-tune large language models on your domain data to achieve expert-level performance on your specific tasks — with full control over the training pipeline.

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What Fine-Tuning Unlocks

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Domain expertiseModel learns your industry terminology, formats, and reasoning patterns

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Lower latency & costSmaller fine-tuned model outperforms larger generic model at fraction of the cost

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Consistent output formatReliable structured responses that match your exact schema every time

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Data privacySelf-hosted models keep sensitive data within your infrastructure

RAG or Fine-Tuning?

The most common question we get. Here’s a clear framework for when each approach wins — and when you need both.

Use RAG When…

Your knowledge changes frequently and the model needs to cite specific sources.

Answering questions from documents, wikis, or databases
Knowledge base changes weekly or daily
You need source citations for every answer
Data volume is large but task complexity is moderate
Budget and timeline are limited

Use Fine-Tuning When…

You need the model to behave differently — learn your style, format, or reasoning patterns.

Specialized output format (medical notes, legal briefs, code)
Domain-specific reasoning the base model gets wrong
You need a smaller, faster, cheaper model for high-volume tasks
Consistency in tone, style, or terminology matters
Self-hosted deployment for data privacy compliance

Models We Fine-Tune

OpenAI

GPT-4

Best for complex reasoning tasks with API deployment

Anthropic

Claude

Long-context mastery, structured output, safety

Llama 3

Open-source, self-hosted, full data control

Mistral

Efficient, fast inference, multilingual strength

Custom

Domain

Specialized models for legal, medical, finance

What We Deliver

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Dataset Preparation

We clean, format, augment, and split your training data. Synthetic data generation for edge cases. Quality scoring to filter noisy examples that would hurt model performance.

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Training Pipeline

Reproducible training with hyperparameter tuning, LoRA/QLoRA for efficient fine-tuning, and experiment tracking. Every run is logged and comparable.

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Evaluation Framework

Custom benchmarks for your domain. Automated evaluation against held-out test sets, human evaluation protocols, and regression testing to prevent catastrophic forgetting.

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Production Deployment

Optimized inference with vLLM or TGI. Quantization for faster, cheaper serving. A/B testing between base and fine-tuned models. Auto-scaling for traffic spikes.

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Continuous Improvement

Feedback loops to capture production examples, automatic retraining pipelines, and drift detection. Your model gets better as it serves more requests.

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Safety & Guardrails

Output filtering, toxicity detection, and hallucination monitoring. Ensure your fine-tuned model doesn’t generate harmful, off-brand, or incorrect content.

Our Process

Feasibility Assessment

We evaluate whether fine-tuning is the right approach. Analyze your data, define success metrics, and compare against RAG and prompt engineering baselines.

Dataset Engineering

Clean and format training data. Generate synthetic examples for rare scenarios. Create evaluation datasets. Typically 1,000-50,000 examples depending on complexity.

Training & Iteration

Multiple training runs with different hyperparameters. LoRA for efficient adaptation. Evaluate each run against your benchmarks. Typically 3-5 iterations to reach target quality.

Evaluation & Validation

Rigorous testing against held-out data, edge cases, and adversarial inputs. Human evaluation where automated metrics aren’t sufficient. Regression testing on general capabilities.

Deploy & Monitor

Production deployment with optimized inference, A/B testing, monitoring dashboards, and feedback collection. Full handoff with training pipeline documentation.

Who This Is For

Regulated Industries

Healthcare, legal, finance — where you need self-hosted models that keep sensitive data on-premise, with output that matches industry-specific formats and terminology.

High-Volume AI Tasks

Running thousands of LLM calls daily and API costs are unsustainable. A fine-tuned smaller model can match GPT-4 quality at 10-20x lower inference cost.

Unique Output Requirements

Your use case needs specific formatting, reasoning patterns, or domain knowledge that prompt engineering alone can’t reliably achieve.

Competitive Advantage

You want an AI model that’s uniquely yours — trained on your proprietary data, impossible for competitors to replicate, and improving with every interaction.

Frequently Asked Questions

How much training data do I need?

It depends on the task complexity. For simple format/style adaptation: 100-500 examples can work. For domain-specific reasoning: 1,000-5,000 examples is typical. For complex multi-step tasks: 5,000-50,000 examples. We also use synthetic data generation to augment limited datasets — so even 200 real examples can be a viable starting point.

How long does fine-tuning take?

The actual training is fast (hours to a couple of days). Dataset preparation is the real bottleneck — typically 2-3 weeks. The full engagement including evaluation, iteration, and deployment is 4-8 weeks. We start showing results from the first training run within the first two weeks.

Will fine-tuning make the model forget general knowledge?

This is called “catastrophic forgetting” and it’s a real risk with naive fine-tuning. We use LoRA (Low-Rank Adaptation) which modifies only a small subset of the model’s weights, preserving general capabilities while adding domain expertise. We also run regression tests to catch any capability loss.

Can I fine-tune GPT-4 or Claude?

OpenAI supports fine-tuning GPT-4 and GPT-4o through their API. Anthropic doesn’t yet offer public fine-tuning for Claude. For maximum flexibility and data control, we often recommend fine-tuning Llama 3 or Mistral — open-source models you can host yourself with no vendor dependency.

What’s the ongoing cost after deployment?

For API-hosted models (OpenAI): fine-tuned inference costs roughly 3-6x the base model rate, but you use a smaller model for the same quality — often net savings. For self-hosted models: GPU hosting costs vary by model size and traffic — we help you choose the most cost-effective option. We optimize for your budget during architecture design.

Ready to Build a Custom AI Model?

Book a free consultation. We’ll assess your data, define the right approach (RAG, fine-tuning, or both), and give you a clear roadmap.

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LLM Fine-Tuning — Available Worldwide

We deliver llm fine-tuning services globally. Select your country:

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