Machine learning and AI

LLMs excel at understanding and generating natural language, making them ideal for conversational AI, content generation, and tasks requiring reasoning over text. Traditional ML is better suited for structured data, predictions based on historical patterns, and scenarios where you need highly interpretable models. We help you evaluate which approach - or combination - fits your specific use case.

We implement modern agentic patterns using frameworks like LangChain and CrewAI. Our approach includes MCP (Model Context Protocol) for standardized tool integration, multi-agent architectures with specialized subagents for complex workflows, and composable agent skills for reusable capabilities. We focus on reliability, observability, and graceful failure handling in production agent systems.

RAG (Retrieval-Augmented Generation) combines LLMs with your proprietary data by retrieving relevant context before generating responses. Use RAG when you need AI that's grounded in your specific documents, knowledge bases, or databases - such as customer support bots, internal knowledge assistants, or domain-specific Q&A systems. It's more cost-effective than fine-tuning and keeps responses up-to-date.

We implement comprehensive evaluation pipelines, monitoring, and guardrails for AI systems. This includes output validation, content filtering, fallback mechanisms, and human-in-the-loop workflows where appropriate. We also focus on prompt injection prevention, cost controls, and rate limiting to ensure your AI systems behave predictably in production.

Yes, we help organizations fine-tune both open-source and commercial models on domain-specific data. We evaluate whether fine-tuning is the right approach versus RAG or prompt engineering, handle data preparation and training infrastructure, and ensure the fine-tuned model meets your quality and performance requirements.

We serve a diverse range of industries including finance, healthcare, technology, retail, and professional services. Our experience spans use cases from intelligent document processing in financial services to clinical decision support in healthcare. We tailor AI solutions to address the unique challenges, regulatory requirements, and opportunities within each sector.