AI Development / 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.
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.
Research & review assistant
Citation-grounded answers, each one traced back to its source document.
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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 notesDone
- Goals and constraints
- Data available
- How success is measured
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Scope & architecture
Agree the architecture, success measures and delivery milestones.
Solution designDone
- 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 buildDone
- 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 rolloutDone
- Production rollout
- Monitoring
- Defined support window
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.
“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.”
“Avinya Labs built our AI travel chatbot … A highly capable team that delivers ahead of the curve.”
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.
Or email contact@avinyalabs.co