Fine-Tuning at Shivacha
Adapting models to specific domains, formats and tasks.
Overview
Fine-tuning adapts pre-trained models using task-specific data — improving format adherence, domain vocabulary or classification accuracy, or enabling smaller models to match larger ones on narrow tasks. We use parameter-efficient methods and rigorous evaluation, and combine fine-tuning with retrieval where knowledge freshness matters.
Why we use it
- Domain adaptation
- Consistent output formats
- Smaller, cheaper models
- Improved narrow-task accuracy
How we use it
Fine-Tuning in our engineering work
Classification models
High-volume, low-cost classifiers.
Domain assistants
Specialised vocabulary and style.
Distillation
Smaller models from larger ones.
Services
Services that use Fine-Tuning
LLM Development
LLM application and platform engineering: model selection, fine-tuning, serving, evaluation and cost-optimised inference.
Learn moreMachine Learning Development
Custom machine learning models for prediction, scoring, forecasting and recommendation, deployed and monitored with MLOps.
Learn moreGenerative AI Development
Generative AI applications for text, documents, code and media — grounded in your data and engineered for production reliability.
Learn moreRAG Development
Retrieval-augmented generation systems that ground LLM answers in your documents with permissions, citations and measurable accuracy.
Learn moreAI Chatbot Development
LLM-powered chatbots for customer service, sales and internal support — grounded, on-brand and integrated with your systems.
Learn moreAI Copilot Development
Embedded AI copilots that help users inside your product or internal tools — drafting, analysing, searching and taking actions.
Learn morePairs well with
What we combine with Fine-Tuning
Models, retrieval, agents and ML operations.
Insights
Related insights
Designing AI products users trust
Trust in AI products is designed: show sources, make uncertainty visible, make correction easy and keep humans in control of consequential actions.
Learn moreWhy most AI pilots never reach production — and the engineering that fixes it
The model is rarely the problem. Retrieval quality, evaluation, integration and governance decide whether an AI pilot becomes a production system.
Learn moreAI development cost: what you pay for when you build an AI product or agent
Model fees are rarely the main cost. Data preparation, evaluation, integrations and guardrails decide both the budget and whether the system works.
Learn more
Build with Fine-Tuning.
Tell us about your project, or the engineers you need, and we will propose an approach.