Build AI agents powered by domain-specific fine-tuned models that outperform general-purpose LLMs on your tasks. We handle data preparation, training, RLHF/DPO alignment, evaluation, and deployment — delivering smaller, faster, cheaper models with superior accuracy.
INR 2000
Per Hour
50+
AI Agents Built
4.9/5
Client Rating
<2 Weeks
MVP Delivery
Integrates with the tools you already use
Custom-trained models that deliver domain expertise at a fraction of the cost
Fine-tune GPT-4o-mini, Llama 3, Mistral, and other models on your domain data. From medical terminology and legal language to financial analysis and technical support — your model speaks your industry's language natively.
We handle the hardest part of fine-tuning — data preparation. We collect, clean, format, deduplicate, and quality-score your training examples. We also generate synthetic training data using larger models to bootstrap datasets when real examples are limited.
Align your model's behavior using Reinforcement Learning from Human Feedback (RLHF) or Direct Preference Optimization (DPO). Teach the model your preferred tone, safety boundaries, and response style through preference-based training.
Rigorous evaluation against base models using domain-specific benchmarks. We measure accuracy, faithfulness, latency, cost per inference, and safety across hundreds of test cases — ensuring your fine-tuned model demonstrably outperforms alternatives.
A fine-tuned smaller model often outperforms a larger general-purpose model on your specific tasks — at 5-10x lower cost. We benchmark fine-tuned GPT-4o-mini against GPT-4o, and fine-tuned Llama against both, to find the optimal cost-performance sweet spot.
Deploy fine-tuned open-source models (Llama, Mistral) on your own infrastructure for complete data privacy. We handle model optimization (quantization, vLLM), GPU provisioning, and inference server setup for self-hosted deployment.
Avg Accuracy Improvement Over Base
Cost Reduction vs Large Models
Training Datasets Prepared
Fine-Tuned Models Deployed
A proven 5-step process from data audit to production fine-tuned model deployment
We analyze your use case, audit available training data, benchmark base models on your tasks, and recommend the optimal base model (GPT-4o-mini, Llama 3, Mistral) and training strategy — supervised fine-tuning, RLHF, or DPO.
Before fine-tuning, we establish a strong baseline with prompt engineering. This defines the target behavior, generates synthetic training data from larger models, and gives you a working prototype while we prepare the training pipeline.
Prepare and curate the training dataset, run fine-tuning jobs with hyperparameter optimization, implement RLHF/DPO alignment if needed, and integrate the fine-tuned model into your agent application with proper error handling and fallbacks.
Comprehensive evaluation comparing the fine-tuned model against the base model and GPT-4o across your domain benchmarks. We test accuracy, latency, cost per inference, edge cases, safety, and regression on general capabilities to ensure production readiness.
Deploy the fine-tuned model with inference optimization (quantization, batching, caching). Set up monitoring for model drift, accuracy degradation, and cost tracking. Continuous improvement through regular retraining on new data. 30-day post-launch support.
Transparent pricing for model fine-tuning and agent development
"We fine-tuned a Llama 3 model on our 50,000 customer support conversations. It now resolves 88% of tickets automatically with domain-specific accuracy that GPT-4o couldn't match out of the box. The cost per inference dropped by 80% compared to using GPT-4o, and we own the model entirely."
VO
VP of Engineering
Engineering at SaaS Platform (500K Users)
"Edesy fine-tuned GPT-4o-mini on our medical documentation data with DPO alignment for safety. The model generates clinical notes that are 94% accurate versus 71% with the base model, while following our specific formatting requirements perfectly. It saves our physicians 45 minutes per day."
CM
Chief Medical Officer
Medical at HealthTech Startup
"Our fine-tuned classification model processes insurance claims 15x faster than our rule-based system with 97% accuracy. Edesy handled everything from data preparation to on-premise deployment. The model runs on our own GPU cluster, so no data ever leaves our infrastructure."
HO
Head of Claims
Operations at Insurance Enterprise
Resources to help you evaluate and implement
Get a free consultation and detailed project estimate. Our fine-tuning specialists will assess your data, benchmark base models, and recommend the optimal training strategy.