AI Development / LLM fine-tuning
LLM fine-tuning for your domain and formats.
LLM fine tuning that adapts language models to your terminology, tone and output formats, measured against a baseline before anything ships.
Astra Short on time? Ask about this page in plain words.
Sound familiar? Four problems we are usually called in to fix.
A general model does not speak your language
Terminology, tone and formats come out wrong.
- Fit assessment first
- Trained on your examples
- Your formats and terms
Output formats are unreliable
Downstream systems break when the structure changes.
- Output format control
- Tested against a baseline
- Same structure every time
Not sure fine-tuning is even needed
Prompting or retrieval might solve it for less.
- Honest fit assessment
- Baseline evaluation
- A recommendation before training
The model goes stale
Your domain moves on and the model does not.
- Monitoring after deployment
- Planned retraining
- Dataset kept current
Fine-tune only when it pays.
Let’s talkLLM 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.
Research & review assistant
Citation-grounded answers, each one traced back to its source document.
~50% faster review time AI · Document intelligenceAI document intelligence
Extraction and classification that sends only the exceptions to people.
>80% less manual review AI · InsuranceFull-stack AI platform for insurance
Fragmented policy, claims and correspondence data connected into one structured workflow.
~2.5x faster processingHave an AI idea you want to test against real data?
From idea to production. Four stages, with a working version reviewed at each one.
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Discovery
Map the use case, the systems and data involved, and what good looks like.
Discovery notes
- Goals and constraints
- Data available
- How success is measured
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Scope & architecture
Agree the architecture, success measures and delivery milestones.
Solution design
- Approach and architecture
- Evaluation plan
- Delivery milestones
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Build
Build against real data, with a working version reviewed at each milestone.
Working build
- Built against real data
- Results measured
- Review at each milestone
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Ship & support
Roll out to production with monitoring and a defined support window.
Production rollout
- Production rollout
- Monitoring
- Defined support window
Why Avinya for LLM fine-tuning. Fine-tuning only where it beats the simpler option.
Tested against the alternative
Fine-tuning is compared with retrieval and prompting on your data before it is recommended.
People stay in control
Approval checkpoints, escalation paths and exception review are designed in from day one.
Traceable by default
Citations, audit trails and logs, so outputs can be verified rather than trusted blindly.
Production, not demos
Built to run against real systems and real data, with monitoring once live.
Full-stack delivery
Data pipelines, models, product and integrations from one team.
AI and Web3 under one team
One studio for AI systems and, where it helps, on-chain infrastructure.
Engagement models. Pick the shape that fits where you are.
Dedicated team
A dedicated team that works as an extension of yours, from first release to scale.
Project-based
A defined scope, milestones and delivery date for a specific product or workflow.
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 … Their expertise accelerated our launch and significantly reduced execution risk.”
Guides on this topic. Plain answers to the questions buyers ask first.
RAG vs Fine Tuning: Which Does Your Business AI Need?
When to use each, when to combine them, and how cost and upkeep compare.
Read the guide → GuideHow to Build a RAG System for Your Company Knowledge Base
Architecture, components and stack choices, and when RAG is the wrong tool.
Read the guide → GuideAI Agent Development Cost: What Drives the Price (2026)
What drives the price of an AI agent: scope, complexity, iterations and retainer.
Read the guide →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.
When is LLM fine tuning worth it?
Fine tuning pays off when prompting and retrieval cannot reach the quality or style you need, and when you have clean examples to train on. We test cheaper options first, then run LLM fine tuning with an evaluation set, guided by the NIST AI Risk Management Framework.
Free tool
How ready is your business for AI?
Twenty questions, about four minutes. You get a score across five areas and what to fix first.