Healthcare & Medical
Clinical note summarisation, ICD coding, drug interaction queries — tuned to your clinical language.
LLM Fine-Tuning
Domain-specific models trained on your data
Fine-tuned LLMs that outperform generic models on your tasks — lower latency, lower cost and better accuracy for your specific domain.
Lower costVs GPT-4 API calls
Higher accuracyOn domain tasks
Your dataStays private
What We Do
Fine-tuning for every use case
Legal
Contract analysis, clause extraction, jurisdiction-specific advice — tuned to your practice area.
Finance
Earnings analysis, risk scoring, regulatory Q&A — tuned to your data and compliance requirements.
Manufacturing
Technical documentation, fault diagnosis, parts lookup — tuned to your product knowledge base.
Education
Tutoring, assessment generation, curriculum-aligned responses — tuned to your learning framework.
eCommerce
Product descriptions, customer query handling, returns processing — tuned to your catalogue.
Our Process
From data to production model
1
Data Audit
Review your training data — quality, coverage, bias and gaps identified before training.
2
Base Model Selection
Llama, Mistral, Qwen or proprietary — selected for your task, size and deployment constraints.
3
Fine-Tuning
LoRA, QLoRA or full fine-tune — with checkpoint evaluation throughout.
4
Evaluation
Benchmarked against GPT-4 and base model on your actual tasks.
5
Deployment
API endpoint, edge device or self-hosted — with monitoring and retraining triggers.
FAQ
Common questions
How much training data do we need?
As few as 500–1,000 high-quality examples for LoRA fine-tuning. More data improves quality — we'll advise on your specific task.
Does our data leave our systems?
Only if you choose cloud training. We offer on-prem and VPC training runs where your data never leaves your infrastructure.
How long does fine-tuning take?
Typically 1–4 weeks including data preparation, training and evaluation. Larger datasets take longer.
Can we update the model as our data changes?
Yes — we build a retraining pipeline so you can update the model as your knowledge base grows.
LLM Fine-Tuning Services — Custom AI Models on Your Data
TECHTARN provides expert LLM fine-tuning services, training custom language models on your data for domain-specific AI across the USA, Canada, UK, UAE, Singapore, Australia and worldwide. When you need AI that speaks your language and knows your domain, fine-tuning delivers.
While RAG systems are ideal for dynamic knowledge, fine-tuning is the right choice when you need consistent style, domain-specific language, or a smaller, faster, cheaper model. Our fine-tuning services create models tailored precisely to your use case, tone and terminology.
Our Fine-Tuning Process
We handle the complete process: data preparation and curation, training runs using LoRA and QLoRA techniques, evaluation and quality assurance, and deployment. We can fine-tune open-source models like Llama, Mistral and Qwen, giving you full ownership and control with no ongoing API fees at scale.
For businesses with sensitive data, we offer on-premise and private cloud training runs where your data never leaves your infrastructure. Fine-tuned models integrate seamlessly into AI agents, chatbots and custom applications, all deployed with proper MLOps for monitoring and retraining as your data grows.
Fine-tuning can require as few as 500-1,000 quality examples. Ready to build a custom AI model? Explore our AI development services or contact us for a free fine-tuning consultation.
Ready to train your own model?
Send us your use case and a sample of your data. We'll evaluate feasibility and quote within 48 hours.
Get In Touch
Ready to get started?
Tell us about your project. We reply within 4 business hours.
Send Us a Message