---
title: LLM Fine-Tuning Services | Avinya Labs
description: LLM fine-tuning by Avinya Labs: adapting language models to your domain, terminology and output formats, with evaluation against a baseline.
url: /service-pages/service-ai-llm-fine-tuning.html
---

[AI Development](/service-pages/service-ai-development.html) / LLM fine-tuning

# LLM fine-tuning for your domain and formats.

Adapting language models to your terminology, tone and output formats, measured against a baseline before anything ships.

[Book a call](https://calendly.com/abbylester/30-mins-meeting) [See the work](/service-pages/service-ai-llm-fine-tuning.html#solutions)

## LLM fine-tuning services. Only when fine-tuning beats simpler options.

### Fit assessment

Check whether prompting or retrieval would solve the problem first.

### Dataset preparation

Clean, labelled training examples from your own data.

### Fine-tuning runs

Training on your examples for domain terms and formats.

### Baseline evaluation

Compare the tuned model against the untuned one on real cases.

### Output format control

Consistent structured output for downstream systems.

### Deployment

Serve the tuned model inside your product.

### Monitoring

Watch quality drift once live.

### Retraining

Refresh the model as your data changes.

### Documentation

Clear records of data, settings and results.

## Related AI work. Delivered engagements in neighbouring areas. None of them is a LLM fine-tuning project.

AI · Legal

### Research & review assistant

Citation-grounded answers, each one traced back to its source document.

~50% faster review time

Link: /case-studies/ai-legal-assistant.html

AI · Document intelligence

### AI document intelligence

Extraction and classification that sends only the exceptions to people.

>80% less manual review

Link: /case-studies/enterprise-document-intelligence.html

AI · Insurance

### Full-stack AI platform for insurance

Fragmented policy, claims and correspondence data connected into one structured workflow.

~2.5x faster processing

Link: /case-studies/ai-platform-insurance-broker.html

## Have an AI idea you want to test against real data?

[Book a call](https://calendly.com/abbylester/30-mins-meeting)

## From idea to production. Four stages, with a working version reviewed at each one.

### Discovery

Map the use case, the systems and data involved, and what good looks like.

Discovery notesDone

  - Goals and constraints
  - Data available
  - How success is measured

### Scope & architecture

Agree the architecture, success measures and delivery milestones.

Solution designDone

  - Approach and architecture
  - Evaluation plan
  - Delivery milestones

### Build

Build against real data, with a working version reviewed at each milestone.

Working buildDone

  - Built against real data
  - Results measured
  - Review at each milestone

### Ship & support

Roll out to production with monitoring and a defined support window.

Production rolloutDone

  - Production rollout
  - Monitoring
  - Defined support window

## Engagement models. Pick the shape that fits where you are.

Ongoing

### Dedicated team

A dedicated team that works as an extension of yours, from first release to scale.

Fixed scope

### Project-based

A defined scope, milestones and delivery date for a specific product or workflow.

Advisory

### Consulting & advisory

Architecture and readiness advice before you commit to a build.

## What our clients say.

AI platform for an insurance broker

> “Avinya Labs built our full-stack AI solution, delivering scalable infrastructure, optimized AI workflows, and a seamless user experience. Their expertise accelerated our launch and significantly reduced execution risk.”

SandraProject lead, Insurance broker

Result~2.5x Faster processing speed [Read case study →](/case-studies/ai-platform-insurance-broker.html)

AI chatbot for a travel platform

> “Avinya Labs built our AI travel chatbot … A highly capable team that delivers ahead of the curve.”

LauraCo-founder, Travel platform

[Read case study →](/case-studies/ai-travel-chatbot.html)

## LLM fine-tuning FAQs.

### When is fine-tuning worth it?

When prompting and retrieval cannot reach the quality, format or cost you need. We test the simpler options first.

### How much data do we need?

It depends on the task. We assess your data during discovery before recommending a run.

### How do you handle our data?

Access is scoped to what the system needs, with role-based permissions and audit trails where records change. We agree data handling during discovery.

### How long does a project take?

It depends on scope and integrations. We agree milestones up front and review a working version at each one.

## Ready to turn your vision into reality?

Tell us the problem, the data, and where manual effort sits today. We'll map the path from MVP to scale.

[Book a call](https://calendly.com/abbylester/30-mins-meeting) [See the work](/work/index.html)

Or email [contact@avinyalabs.co](mailto:contact@avinyalabs.co)