For Enterprise AI Teams
Get your data ready for AI.
The information that would make AI genuinely useful is scattered, inconsistently defined, and ungoverned. We get it into a state your AI systems can reach, reason over, and be trusted with.
Contact UsEvery enterprise AI project stalls in the same place. Not the model — the data.
The information that would make an AI system genuinely useful is scattered across a CRM, a data warehouse, three operational systems, a shared drive, and the working knowledge of the six people who understand how it all fits together. Definitions disagree. Lineage is undocumented. Nobody can say with confidence which copy of the revenue number is the real one.
AI Data Alignment is the work of getting your organization’s critical data into a state where AI can actually reach it, reason over it, and be trusted with it.
The Problem Underneath the Problem
Most organizations discover this in the wrong order. They pilot an AI assistant, it returns a confidently wrong answer, and the conclusion is that the model isn’t ready.
Usually the model was fine. It was asked to reason over data that three departments define differently, with no governance layer to tell it which source is authoritative and no access controls to keep it from surfacing something it shouldn’t.
This is a data architecture problem wearing an AI costume. It has to be solved before agents, retrieval systems, or private models can deliver anything an executive will stake a decision on.
Our Approach
What We Do
Data discovery and inventory
Map where your critical data actually lives — including the systems nobody put on the architecture diagram. Identify the entities that matter, who owns them, and how they move.
Unified governance and cataloging
Establish one governed catalog across sources: consistent definitions, documented lineage, and access controls that carry through to every AI system reading from it.
A semantic layer AI can reason over
Business meaning made explicit, so an AI system knows that "active member" means what your organization means by it — not what it guesses from a column name.
Pipelines built for AI consumption
Ingestion and transformation that keep governed data current, structured, and queryable at the latency your applications need.
Readiness assessment
An honest evaluation of what your data can support today, what it can't, and the shortest path between the two.
Built on Databricks
Aptaria is a Databricks partner. Databricks unifies data engineering, governance, analytics, and AI on a single platform — which makes it the natural foundation for the alignment work this solution depends on.
The partnership means our clients get an architecture built on a platform their data team can own after we leave, rather than a bespoke pipeline only we understand.
How an Engagement Runs
Assess
Inventory sources, interview data owners, document the current state, and identify the highest-value gaps.
Design
Define the target architecture: catalog structure, governance model, semantic layer, and pipeline design.
Build
Implement incrementally, starting with the domain that unblocks a real AI use case rather than boiling the ocean.
Enable
Hand off with documentation and training, so your team operates it.
Who This Is For
Organizations whose AI pilots keep stalling. If every proof of concept works in a demo and fails in production, the gap is usually here.
Organizations with data in many places. Especially those running a CRM alongside operational systems that were never designed to talk to each other — territory Aptaria has worked in for two decades.
Organizations facing governance requirements. Finance, healthcare, and any membership organization holding sensitive constituent data, where “the AI can see everything” isn’t an acceptable architecture.