Fine-Tuned LLMs That
Own Your Domain
General-purpose LLMs are trained on the internet. Your domain is legal contracts, clinical notes, or financial filings - not Reddit. Fine-tuning adapts a powerful base model to your domain, your terminology, and your style - outperforming GPT-4o on your specific tasks at a fraction of the inference cost.
Our Service Capabilities
End-to-end capability - from strategy and build to integration, monitoring, and ongoing support.
LoRA & QLoRA Fine-tuning
Parameter-efficient fine-tuning with LoRA and QLoRA - adapting Llama 3, Mistral, and Phi-3 to your domain with minimal GPU resources, fast training cycles, and production-ready outputs.
Instruction Tuning (SFT)
Supervised fine-tuning on instruction-response pairs - teaching the model to follow your specific task formats, output schemas, and domain conventions with precision.
RLHF & DPO Alignment
Reinforcement Learning from Human Feedback and Direct Preference Optimisation - aligning model outputs to your quality standards, safety requirements, and preferred response style.
Domain Adaptation
Continued pre-training on your domain corpus - legal texts, clinical literature, financial filings, or technical documentation - embedding deep domain knowledge into the model weights.
Model Evaluation & Benchmarking
Rigorous evaluation of fine-tuned vs baseline models on your specific tasks - custom benchmarks, automated eval pipelines, and comparison against GPT-4o on your actual use cases.
Self-hosted Deployment
Optimised deployment of fine-tuned models using vLLM, Ollama, or TGI - quantised for your hardware, with streaming API, monitoring, and cost dashboards.
Our Engagement Process
A disciplined, outcome-focused approach from first call to go-live.
- 1
Use Case & Data Assessment
Define the fine-tuning objective, evaluate your training data, and determine the optimal approach - LoRA, SFT, or domain adaptation.
- 2
Dataset Preparation
Clean, format, and structure your training data into instruction-response pairs - with quality filtering and deduplication.
- 3
Training & Hyperparameter Optimisation
Run fine-tuning experiments with hyperparameter search - learning rate, LoRA rank, batch size - tracked in Weights & Biases.
- 4
Evaluation & Comparison
Evaluate fine-tuned model vs GPT-4o baseline on your task-specific benchmarks - with automated test sets and human evaluation.
- 5
Optimisation & Deployment
Quantise and optimise for production, deploy with vLLM or Ollama, and set up performance monitoring and retraining triggers.
Tools & Frameworks We Master
A production-tested, vendor-agnostic stack built for enterprise security and compliance requirements.
Fine-tuning Frameworks
Base Models
Training Infrastructure
Serving
Use Cases by Industry
Production AI systems we have built across regulated, data-heavy industries.
Llama 3 70B for Contract Law
Fine-tuned Llama 3 70B on 200K+ contract clauses - outperforms GPT-4o on legal clause extraction by 8% at 40% lower inference cost. Air-gapped self-hosted.
Clinical NLP Model Fine-tuning
Fine-tuned Mistral 7B on 50K clinical notes - achieves 93.1% ICD-10 coding accuracy, self-hosted for HIPAA compliance, 70% lower cost than GPT-4o.
Financial Analysis LLM
Llama 3 8B fine-tuned on earnings transcripts and analyst reports - generates structured investment summaries with financial entity extraction and sentiment.
Support Agent Fine-tuning
Phi-3 mini fine-tuned on 10,000 resolved support tickets - on-device deployment on support terminals, 89% first-response accuracy, zero cloud inference cost.
Curriculum-Specific LLM Tutor
Llama 3 fine-tuned on K-12 curriculum materials - generates accurate, age-appropriate explanations and quiz questions. Deployed for 50K+ students.
Internal Codebase Fine-tuning
Llama 3 70B fine-tuned on internal codebase + docs - completes code in proprietary frameworks that GPT-4o does not know, 3x faster developer productivity.
What Teams Say After Shipping with Us
Real results from teams who needed fine-tuned models to work in production, not just in a demo.
AndolaSoft has been a valued partner providing excellent customer service. Issues with clients or troubleshooting are handled in a timely manner and positive resolution is always the outcome.
I got a recommendation on AndolaSoft. They are more than half the cost, they have a can-do attitude, and they are responsive, timely, and easy to work with.
Andolasoft team is very hardworking, dedicated and professional that follows through with their goals. The technical leadership is also a superior value to any other developers.
Frequently Asked Questions
Fine-tuning adapts a pre-trained LLM to your specific domain, tasks, or style using your own data. Instead of training from scratch, you start from a powerful base model (Llama 3, Mistral, GPT-4o) and continue training on your domain-specific dataset - achieving higher accuracy on your tasks at lower inference cost.
Ready to Fine-Tune an LLM on Your Data?
Tell us your domain, task, and data availability. We will design a fine-tuning plan that outperforms GPT-4o on your specific use case at lower cost.