Pakistan

LLM Fine Tuning Services

Fine-tuning is usually not the first thing you need

Before recommending fine-tuning, we check whether well-designed prompting and retrieval-augmented generation (feeding relevant context to a general model at query time) can solve the problem more cheaply and with less ongoing maintenance. Fine-tuning makes sense when you need a model to consistently follow a very specific format, tone or domain vocabulary that prompting alone struggles to enforce reliably at scale - not as a default first step.

What's included when fine-tuning is the right call

Dataset preparation from your existing data (support transcripts, documentation, past outputs) into a format suitable for fine-tuning
Fine-tuning via official provider APIs (OpenAI fine-tuning, or equivalent) rather than unofficial local training with unpredictable results
Evaluation against a held-out test set to confirm the fine-tuned model actually outperforms base-model prompting on your specific task
Deployment into your application with proper versioning, so you can compare or roll back to the base-model approach if needed

Our honest assessment process

1. Try prompting and RAG first

We build a prompt-engineered or retrieval-augmented solution first and measure its real performance - often this alone solves the problem without the added cost and maintenance burden of fine-tuning.

2. Identify the specific gap

If prompting genuinely falls short, we pin down exactly what's failing - format consistency, domain vocabulary, tone - to scope fine-tuning around that specific gap rather than a vague "make it better."

3. Prepare data and fine-tune

Using your real examples of desired output, via official provider fine-tuning APIs.

4. Evaluate honestly

Compared directly against the prompting-only baseline - if fine-tuning doesn't measurably beat it, we say so rather than ship it anyway to justify the cost.

Pricing

Rs 100,000-400,000 depending on dataset preparation complexity and evaluation scope. Includes the initial prompting/RAG comparison, since that's necessary to justify the fine-tuning investment either way.

Hire AI Developers · AI Automation · MLOps Pipeline · All services

Frequently asked questions

Do we actually need fine-tuning, or would prompting work?

We check this first, honestly - a well-designed prompt or retrieval-augmented approach often solves the problem more cheaply, and we'll tell you directly if that's the case rather than sell fine-tuning by default.

What data do we need to provide?

Real examples of the input/output pattern you want the model to learn - support transcripts, documentation, or past outputs, depending on the task.

Which models can be fine-tuned?

We use official provider fine-tuning APIs (such as OpenAI's) rather than unofficial local training, which tends to produce unpredictable results without a dedicated ML infrastructure team.

How do we know if it actually worked?

We evaluate the fine-tuned model against a held-out test set and compare it directly to the prompting-only baseline - if it doesn't measurably outperform, we say so.

Can we roll back if the fine-tuned model underperforms in production?

Yes - deployment includes versioning specifically so you can compare or revert to the prior approach.

What does it cost?

Rs 100,000-400,000 depending on dataset and evaluation complexity, including the initial prompting comparison.

Get Quote WhatsApp Contact Book Meeting