Data & AI Engineering

A 10-point checklist for choosing a Databricks implementation partner, written for mid-market teams that need delivery, not advice.

What AI agent implementation services actually include, what is usually out of scope, and the price bands to expect before you sign.

When to hire a Databricks medallion architecture consultant, what a 14-day build should deliver, and what bronze to gold really costs.

The real cost to build a RAG chatbot, split into build and run, with worked numbers for internal, customer-facing and regulated use.

AI agents hallucinate on company data because of context, not the model. A symptom to cause to fix table for the four real culprits.

A data readiness checklist for AI agents covering lineage, freshness, access control, PII, schema stability and evaluation data.

Context engineering for AI agents goes past prompts: retrieval design, chunking, memory, tool context and how each one fails.

RAG guardrails that stop agents leaking or hallucinating in production, mapped to the specific risk each control removes.

A practical walkthrough to make fragmented enterprise data AI ready, from raw sources to a gold layer an agent can safely query.