The right LLM,
fine-tuned for your domain.
We help you choose, fine-tune, and deploy large language models — from RAG to self-hosted Llama — benchmarked for accuracy, cost, and compliance. No vendor lock-in, no guesswork.
End-to-End LLM Solutions
From model choice to production deployment — accurate, cost-efficient, and compliant.
Model Selection & Benchmarking
Benchmark GPT-4o, Claude, Llama, and Mistral against your tasks, latency, cost, and compliance needs.
Fine-Tuning
Adapt base models to your domain with LoRA, QLoRA, or full fine-tuning for style and accuracy.
RAG Development
Retrieval pipelines that ground LLM answers in your data with citations and no hallucinations.
Self-Hosted Deployment
Deploy open models in your cloud or on-prem for cost control and air-gapped compliance.
Prompt Engineering
Systematic prompt design and optimisation for reliable, repeatable LLM behaviour.
LLM Evaluation
Automated eval pipelines measuring accuracy, safety, and regressions before every release.
From model choice to production in 5 steps
A pragmatic methodology that gets you the right LLM solution without trial-and-error spend.
- 1
Requirements & Benchmark
Define tasks and constraints, then benchmark candidate models objectively.
- 2
Architecture Decision
Choose RAG, fine-tuning, or both — and the deployment model that fits.
- 3
Build & Fine-Tune
Implement pipelines, fine-tune where needed, and engineer reliable prompts.
- 4
Evaluate & Harden
Run automated evals, add guardrails, and validate accuracy and safety.
- 5
Deploy & Operate
Ship to production with monitoring, cost controls, and ongoing evaluation.
Modern LLM Technology Stack
We benchmark all major models so your choice is driven by evidence, not hype.
LLMs
Fine-Tuning
RAG & Serving
Eval & Ops
LLM Solutions in Production
Model selection, fine-tuning, and deployment delivered across regulated and high-scale domains.
Self-Hosted Compliance LLM
Air-gapped Llama deployment for document analysis meeting strict data residency rules.
Fine-Tuned Clinical LLM
Domain fine-tuning that improved clinical summarisation accuracy by 19%.
RAG Research Assistant
Grounded LLM with citations that cut legal research time by 60%.
Model Cost Optimization
Right-sized model routing that reduced LLM spend 40% with no quality loss.
LLM Evaluation Harness
Automated eval pipeline catching regressions before each model update.
Prompt Optimization
Systematic prompt redesign that lifted response accuracy from 78% to 94%.
Trusted by Teams Shipping AI
Real results from teams who needed LLMs that are accurate, affordable, and compliant.
AndolaSoft has been a valued partner providing excellent customer service. Issues are handled in a timely manner and a positive resolution is always the outcome.
They are more than half the cost, they have a can-do attitude, and they are responsive, timely, and easy to work with.
The Andolasoft team is hardworking, dedicated and professional. The technical leadership is a superior value to any other developers.
Frequently Asked Questions
Use RAG when your knowledge base changes frequently, you need citations, or you want to avoid retraining. Use fine-tuning when you need to change the model style or reasoning patterns on a relatively stable task. Often the best solution combines both.
Ready to put LLMs to work?
Book a free consultation and we will recommend the right LLM approach for your use case — no jargon, no obligation.