Beyond the Chatbot: Real GenAI Integration
Most businesses start their GenAI journey with a chatbot. But the real value lies in deeply integrating AI capabilities into your core product — intelligent search, automated content generation, predictive analytics, and personalized user experiences.
Retrieval-Augmented Generation (RAG)
RAG is the most practical pattern for enterprise GenAI. Instead of fine-tuning expensive models, you connect your LLM to your own data sources — documents, databases, APIs — so it generates accurate, contextual responses grounded in your business knowledge.
Choosing the Right Model Strategy
API-based (OpenAI, Anthropic, Google): Fastest to deploy, pay-per-use pricing. Best for most use cases.
Open-source (Llama, Mistral): Full control, self-hosted. Best when data privacy is paramount or you need deep customization.
Fine-tuned models: Train on your domain data for specialized tasks. Best when off-the-shelf models lack accuracy for your specific use case.
Production Considerations
Latency, cost, hallucination mitigation, and user experience design are the four pillars of production GenAI. We help teams navigate these tradeoffs and ship AI features that users actually trust and rely on.