Generic AI models are powerful, but they don't know your business. A customer support chatbot trained on general data won't understand your product terminology. A document classifier built for generic text won't handle your industry-specific formats. Fine-tuning bridges this gap — taking a pre-trained model and teaching it the nuances of your specific domain.
At Fillicore Technologies, we specialize in making AI models work for your exact use case. Whether it's fine-tuning GPT for your customer service workflows, training a BERT model on your legal documents, or building a RAG pipeline that retrieves from your knowledge base — we deliver models that perform with the precision your business demands.
01 — Approaches
We choose the right approach based on your data, budget, and accuracy requirements.
Parameter-efficient fine-tuning that trains only a small fraction of model weights. Get domain-specific performance at a fraction of the compute cost — ideal for LLMs like Llama, Mistral, and Gemma on limited GPU budgets.
Retrieval-Augmented Generation combines your knowledge base with LLM reasoning. We build vector databases, embedding pipelines, and retrieval systems that ground AI responses in your actual data — reducing hallucinations and improving accuracy.
Complete model retraining on your dataset for maximum accuracy. Best for specialized domains like medical, legal, or financial text where generic models fall short. We handle data preparation, training infrastructure, and evaluation.
02 — Use Cases
Real-world scenarios where custom models outperform generic ones.
Customer support bots that understand your product catalog, pricing, and policies. Fine-tuned on your actual support tickets and documentation for responses that are accurate, on-brand, and helpful.
Extract structured data from invoices, contracts, medical records, or industry-specific documents. Models trained on your document formats achieve extraction accuracy that generic OCR and NLP can't match.
Generate product descriptions, marketing copy, or technical documentation in your brand voice. Fine-tuned models produce content that matches your tone, terminology, and style guidelines consistently.
03 — Process
From raw data to production-ready custom models.
Assess your data quality, volume, and labeling needs.
Clean, format, and create training/validation splits.
Fine-tune with hyperparameter optimization and evaluation.
Benchmark against baselines with domain-specific metrics.
Production deployment with monitoring and retraining pipeline.
04 — FAQ
Fine-tuning changes the model's weights to learn new patterns from your data. RAG keeps the model unchanged but retrieves relevant context from your knowledge base at query time. We often combine both — fine-tuning for tone and domain understanding, RAG for factual accuracy from your documents.
For LoRA fine-tuning, as few as 100-500 high-quality examples can show significant improvement. Full fine-tuning typically needs thousands of examples. We help you assess data requirements during the audit phase and can assist with data augmentation strategies.
Absolutely. We can fine-tune open-source models (Llama, Mistral, Gemma) on your own infrastructure or private cloud, ensuring your data never leaves your environment. For API-based fine-tuning (OpenAI, Anthropic), we follow their data handling policies and can advise on privacy implications.
Phone
+91 00000 00000Location
Salem, Tamil Nadu · Working Globally