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An AI readiness assessment examines five dimensions: data quality and accessibility, technical infrastructure, team capabilities, process maturity, and governance readiness. The output identifies which AI use cases are feasible with your current data, which require data remediation, and which are not viable. It prevents organizations from investing in AI initiatives their data cannot support, typically saving 6-12 months of misdirected effort.

Predictive AI analyzes historical data to forecast outcomes like demand, churn, or pricing optimization. Generative AI creates new content - text, images, code, and summaries - based on learned patterns. Most business ROI today comes from predictive AI applied to operations and generative AI applied to knowledge work. The right choice depends on whether your problem is "what will happen" or "create something new."

Enterprise AI implementations range from $100K for focused single-use-case projects to $2M+ for multi-system deployments. The cost breakdown is typically 40% data preparation, 25% model development and training, 20% integration and deployment, and 15% monitoring and governance. Caxy's Data Foundation engagement at $120K-$180K addresses the data preparation phase that determines whether the remaining investment succeeds or fails.

The five most common blockers are duplicate records across systems (present in 85% of organizations), inconsistent formatting and naming conventions, missing values in critical fields, stale data that has not been updated in months, and siloed data trapped in systems without API access. Fixing these issues typically takes 6-12 weeks and is the single highest-ROI investment in any AI initiative.

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We implement retrieval-augmented generation that grounds AI responses in verified source documents, confidence scoring that flags low-certainty outputs for human review, structured output validation that checks AI responses against known constraints, and source attribution that lets users verify every claim. For customer-facing AI, we add human-in-the-loop review for responses below confidence thresholds and automated testing against known-good answer sets.

RAG is an AI architecture that retrieves relevant documents from your data before generating a response, grounding the output in factual sources rather than relying on the model's training data alone. It reduces hallucination rates by 60-80% compared to raw LLM responses. RAG requires a well-organized knowledge base with clean, current content - which is why data preparation is a prerequisite for effective RAG deployment.

Yes, with appropriate governance. We build AI systems that comply with HIPAA, SOC 2, GDPR, and industry-specific regulations by implementing audit trails, access controls, data residency requirements, and explainability features. Regulated industries require human-in-the-loop workflows for clinical or financial decisions, model versioning for reproducibility, and bias testing. These requirements add 20-30% to implementation costs but are non-negotiable.

RAG is an AI architecture that retrieves relevant documents from your data before generating a response, grounding the output in factual sources rather than relying on the model's training data alone. It reduces hallucination rates by 60-80% compared to raw LLM responses. RAG requires a well-organized knowledge base with clean, current content - which is why data preparation is a prerequisite for effective RAG deployment.

Production AI systems require model performance monitoring to detect accuracy drift, data pipeline maintenance to ensure input quality, prompt or model retraining as business context evolves, cost monitoring for API-based models, and security updates. Plan for 15-20% of the initial build cost annually for maintenance. Without active monitoring, AI systems degrade within 6-12 months as underlying data patterns shift.