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Adapt foundation models with high-quality domain-specific data and evaluation workflows to create AI systems tailored to your business requirements.
Adapt foundation models with high-quality domain-specific data and evaluation workflows to create AI systems tailored to your business requirements.
Foundation models are trained on the public internet, which means their default behavior is generic. While prompt engineering and RAG are excellent for giving the model new facts, LLM Fine-Tuning is the process of fundamentally altering the model's behavior.
By training the model on thousands of carefully curated examples of your domain data, we adapt its internal neural weights. This creates a highly specialized AI that naturally speaks your industry's jargon, strictly follows complex formatting instructions, and performs niche business tasks with consistency that prompt engineering simply cannot achieve.
Fine-tuning is a powerful tool, but it is not always the answer. Use this framework to understand when model customization makes sense for your business.
Injecting private documents into the prompt at runtime.
Rewriting system instructions to guide model behavior.
Connecting models to APIs to take autonomous actions.
Training the model on custom datasets to alter its base capability.
Base models sound robotic and generic. They don't naturally speak in your brand's specific tone of voice.
Standard LLMs struggle to understand highly specific medical, legal, or proprietary engineering jargon.
Prompt engineering often fails to consistently force the model to output strict, complex JSON or XML schemas.
Without training, models can behave unpredictably, giving different types of answers to the same instructions.
Supervised fine-tuning (SFT), LoRA/QLoRA optimization, and domain-specific dataset preparation for specialized AI model performance.
Base models are generic. We fine-tune open-source models on your industry vocabulary, legal formats, medical terminology, and code rules.
Train open-source model parameters on curated high-quality Q&A pairs for specialized task mastery.
Low-Rank Adaptation techniques allowing model fine-tuning at a fraction of standard GPU hardware costs.
Clean, format, anonymize, and augment instruction datasets required for high-performance model training.
Rigorous benchmarking of tuned models against base models for accuracy, hallucination rate, and speed.
Reduce API costs and improve response quality by building a custom-tuned model trained specifically on your data.
A fine-tuned model is only as good as the data it learns from. We specialize in transforming your raw business data into high-quality, perfectly formatted Instruction-Response datasets.
Fine-tuning requires rigorous evaluation. We combine automated programmatic testing (benchmarks, regression tests) with expert Human-in-the-Loop (HITL) review to guarantee model quality and safety before production deployment.
Determine whether fine-tuning is appropriate vs RAG.
Choose the appropriate open-source foundation model.
Create and validate the training and test datasets.
Define training parameters and evaluation criteria.
Fine-tune the model using the prepared dataset.
Measure quality, consistency, safety, and performance.
Improve data, configuration, or model selection if needed.
Deploy the selected model into the application.
Track model behavior and production performance.
A healthcare startup needed an LLM to accurately extract complex diagnoses from messy clinical notes, but base models hallucinated medical terms.
Curated 20,000 instruction-response pairs from anonymized clinical data and performed supervised fine-tuning on a Llama 3 8B model.
Increased medical extraction accuracy from 62% to 94%, passing rigorous clinical safety evaluations.
An accounting firm needed an LLM to read invoices and output a very strict, complex JSON schema for their ERP system. Prompt engineering failed 15% of the time.
Fine-tuned a specialized 7B parameter model using 5,000 perfectly formatted JSON examples to learn the exact structural requirements.
Achieved 99.9% JSON schema adherence without requiring expensive and slow retry loops at inference time.
Explore some of our most impactful digital transformations.
"We spent months trying to prompt our way to the right tone of voice, but it always sounded like a generic AI. CodeCyper took our brand guidelines, built a dataset, and fine-tuned a model that finally sounds like us."
"The data preparation phase was incredible. They didn't just run a script; they meticulously cleaned and formatted our legacy data so the model actually learned the right patterns. The customized model completely outperformed GPT-4 for our specific classification task."
Turn a general-purpose foundation model into a solution tailored to your domain, workflows, and specific business requirements.