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Data engineering, data governance, and agentic AI consulting are different purchases with different outputs. Each produces something concrete that your organization owns afterwards.
Data engineering consulting builds the pipelines and platforms that move data from where it is created to where AI and analytics can use it, governed and on time.
Data governance consulting puts in place the rules, roles, and records that let you trust your data, and prove it to an examiner.
Agentic AI consulting delivers AI agents that act on enterprise data and systems. The groundwork comes first, because agents built on ungoverned, undefined data give inconsistent answers.
When the agents need to run on data that cannot leave your perimeter, see private AI on Cloudera. For the inventory, validation, and controls that let a risk committee approve them, see AI governance consulting.
Every engagement starts with an AI Readiness Assessment. It tells you what your data, governance, and semantic layer need before AI can rely on them, and the plan is yours whether or not we do the work.
| Engagement | What you get | Who owns it afterwards | Typical duration |
|---|---|---|---|
| AI Readiness Assessment | An assessment of your data, governance, and semantic readiness, with a prioritized plan. | You. The plan is yours whether or not we do the work. | 2 to 4 weeks |
| Data platform and pipelines | Built pipelines, platform configuration, and runbooks. | Your data team, or us under a run agreement. | Scoped after the assessment |
| Data governance framework | Roles, policies, catalog, access model, lineage, and the evidence pack. | Your data office and stewards. | 8 to 12 weeks |
| Semantic layer | Ontology, shared definitions, and the knowledge graph. | Your data office, maintained with the teams that use it. | Scoped after the assessment |
| Agentic AI use case | Agents in production, with a decision trace for review. | Your product owner, with our support if you want it. | Scoped after the assessment |
Here's what DE sees again and again: organizations invest heavily in AI models, tooling, and infrastructure — then wonder why the results are inconsistent, unreliable, or impossible to scale.
Duczer East's senior AI leadership has made solving this the starting point of every engagement. We build ontologies that define your domain precisely, knowledge graphs that capture the relationships between entities, and vector databases that power semantic retrieval.
AI is only as good as the data behind it. Duczer East's data engineering practice — anchored by our Cloudera partnership — ensures your AI systems are fed by clean, governed, real-time data pipelines that you can trust and audit.
From change data capture to streaming architecture, DE builds the data infrastructure that transforms raw enterprise data into AI-ready fuel.
The DE AI Foundation Platform is a structured accelerator designed to integrate real-time data, event-driven architecture, and secure AI agent access into a cohesive platform your teams can build on.
Rather than treating each AI initiative as a standalone project, Duczer East helps you establish a Center of Excellence — a governed, scalable foundation.
Deploying AI without governance is building on sand. Duczer East helps you establish the frameworks, policies, and organizational structures that make AI trustworthy, compliant, and auditable.
DE's governance practice isn't a checklist exercise. We embed accountability, transparency, and ethical oversight directly into your AI workflows.
The emergence of Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols is reshaping how AI systems connect to enterprise infrastructure.
Duczer East is at the forefront of implementing these protocols, enabling AI agents to securely access enterprise tools, databases, and services through standardized interfaces.
DE is platform-aware but not platform-locked. We choose the right tool for the problem. Our Cloudera consulting practice anchors the data platform work, up to private AI on Cloudera. And when the platform you already run needs operating hands, our Cloudera support practice keeps the estate healthy and staffed.
Test five things: senior people doing the work rather than only selling it, hands-on experience with your platforms, references in regulated environments if you operate in one, clear ownership of what is delivered once the engagement ends, and whether the firm will tell you when a smaller step is enough.
An AI readiness assessment tests whether your data, governance, and semantic layer can support AI in production, and what has to change first. Ours is fixed-scope, takes 2 to 4 weeks, and ends with a prioritized plan that is yours whether or not we do the work.
Agentic AI consulting designs and delivers AI agents that take actions on enterprise data and systems. Done properly it starts with governed data and a semantic layer, then builds the agents, their connections through MCP and A2A, and a decision trace so every action can be reviewed.
Cloudera, Databricks, and Snowflake for the data platform; Kafka, NiFi, and Apache Iceberg, either within Cloudera or as open source; Airflow and dbt for orchestration and transformation; Collibra, Alation, and Octopai for catalog and lineage; Neo4j for knowledge graphs; and vector databases for retrieval. We also often work with Informatica, including in Cloudera environments.
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