Which foundation models do you work with?
We work with OpenAI (GPT-4/o), Anthropic (Claude), Google (Gemini), and open-source models including Llama and Mistral.
Specialized AI Services
Generative AI Models
Design and deploy production-grade generative AI solutions using the latest foundation models. We help you select, fine-tune, and operationalize the right AI models for your specific use cases.
Build with the Best AI ModelsEnd-to-End Generative AI Model Development
From model selection and fine-tuning through deployment and monitoring, we build robust AI model pipelines that integrate with your systems and perform reliably at scale.
Model Selection & Fine-Tuning
We evaluate and select the right foundation models for your use case — and fine-tune them on your data to achieve the accuracy and style your business requires.
Learn MoreModel Deployment & MLOps
Production deployment of AI models with the monitoring, versioning, and serving infrastructure needed to run AI reliably at scale.
Learn MoreWhy Eco-GreenDevelopers for Generative AI Models
Foundation models are powerful but complex. We bring the technical depth to deploy them correctly, the domain expertise to tailor them effectively, and the operational rigor to keep them performing.
Foundation Model Expertise
Deep hands-on experience with GPT-4, Claude, Gemini, Llama, Mistral, and other leading open and closed-source models.
Fine-Tuning at Scale
Efficient fine-tuning approaches including LoRA and QLoRA that adapt foundation models to your domain without prohibitive compute costs.
Prompt Engineering
Systematic prompt design, chain-of-thought techniques, and few-shot approaches that maximize model accuracy and reliability.
Evaluation Framework
Rigorous model evaluation with benchmark datasets, human evaluation, and automated scoring to ensure consistent quality.
Model Serving Infrastructure
High-performance model serving with vLLM, TensorRT, and cloud-native solutions optimized for latency and throughput.
Cost Optimization
Model quantization, caching strategies, and intelligent routing that reduce inference costs without sacrificing quality.
Frequently Asked Questions
Let's Tackle the Questions That Stir Your Mind!
When should we fine-tune vs. use prompt engineering?
Fine-tuning is most valuable when you need consistent style, domain terminology, or behavior that prompting alone can't achieve.
How do you evaluate model quality?
Through automated benchmark evaluation, human preference testing, and domain-specific metrics aligned to your business requirements.
How do you handle model costs at scale?
Through prompt caching, response caching, model distillation, and intelligent routing between expensive and cheaper models.
Can we self-host AI models for data privacy?
Yes. We deploy open-source models in your own cloud environment for complete data privacy and control.