Traditional AI classifies, predicts, and analyzes existing data. Generative AI creates new content—text, images, code, audio—that didn't exist before. While traditional AI might classify an email as spam, generative AI can write the email. GenAI represents a fundamental shift toward creative and productive AI applications.

Each model has strengths: GPT-4 excels at complex reasoning and coding; Claude offers longer context windows and safer outputs; LLaMA provides open-source flexibility; Gemini has strong multimodal capabilities. We evaluate your specific requirements and recommend the optimal model(s) for your use case.

Retrieval-Augmented Generation (RAG) combines LLMs with your proprietary knowledge base. Use RAG when you need accurate, up-to-date responses grounded in your organization's data rather than the model's training data. RAG is essential for knowledge bases, documentation systems, and domain-specific Q&A.

We implement multiple strategies: RAG for grounding responses in facts, fine-tuning on accurate data, careful prompt engineering with explicit instructions, output validation, confidence scoring, source citation requirements, and human-in-the-loop review for critical applications.

Yes. We build GenAI solutions that integrate seamlessly with your existing platforms through APIs, webhooks, and microservices architecture. Whether it's your CRM, documentation system, or custom applications, we ensure smooth integration.

Costs vary by complexity: POCs start around $25K-50K, production systems range from $75K-250K+. Ongoing costs include API usage (model inference), hosting, and maintenance. We optimize architectures to reduce operating costs by 40-70% through intelligent caching and model selection.

POCs typically take 3-4 weeks. Production-ready systems range from 2-4 months depending on complexity, required integrations, and fine-tuning needs. We use agile methodologies for rapid iteration and early value delivery.

Not always. Many use cases work well with prompt engineering alone. Fine-tuning is beneficial when: you need domain-specific terminology, have proprietary workflows, require specific output formats, or need to reduce hallucinations in specialized domains.

We offer multiple deployment options: Azure OpenAI and AWS Bedrock for enterprise-grade security, on-premise deployment of open-source models, API configurations that prevent training on your data, and comprehensive data governance frameworks. All implementations follow strict security protocols.

Our GenAI support includes prompt optimization, model updates as new versions release, performance monitoring, cost optimization, output quality improvements, adding new features, compliance updates, and 24/7 technical support for critical systems.