Business data integration
Connect the applications, databases, and files your workflow needs. Build repeatable ingestion and transformation steps so the same manual exports don’t become a permanent dependency.
AI data readiness & implementation
Your AI project starts
with the right data.
Magnetic North helps businesses prepare, connect, and organize data for AI. Go from an idea for an assistant, search tool, or automated workflow to a practical plan and a working data foundation.
Talk through your AI use caseBefore the build
Know what’s ready.
Know what’s missing.
An AI data readiness assessment connects the business outcome to the information needed to support it. We look beyond whether the data exists to whether it is usable for the task.
- Map the source systems and documents relevant to your use case.
- Assess quality, completeness, ownership, and how often information changes.
- Identify access requirements, inconsistent definitions, and integration gaps.
- Define a focused implementation scope and ways to evaluate the result.
From readiness to implementation
Get your business context
into the workflow.
The right approach depends on the data, the task, and how your team will use the result.
Document preparation & retrieval
Organize source documents, preserve useful metadata, and prepare content for search or retrieval-augmented generation. Define how updated or removed documents should be handled.
AI data pipelines
Move and transform data on a schedule that fits the use case. Add validation and failure visibility so your team can understand whether the inputs are current and complete.
Quality, access & evaluation
Keep business definitions consistent, account for data permissions, and evaluate the workflow with representative questions and tasks. Use the findings to guide improvements.
Need the platform underneath it? We also build the warehouses, pipelines, and models that support AI at scale.
Explore data engineering ↗AI data consulting, explained
Start with
a clearer picture.
Do we need a new data platform before using AI?
That depends on the use case and your existing systems. A focused project may be possible with the tools you already use. We assess the relevant data and constraints before recommending a platform change.
What is retrieval-augmented generation (RAG)?
RAG is an approach that retrieves relevant information from your documents or data and gives it to an AI model as context for a response. Its usefulness depends on source quality, retrieval behavior, permissions, and evaluation. It does not remove the need to check AI outputs.
How do you approach sensitive business data?
We define access requirements and data boundaries as part of the scope. The implementation should account for source permissions, which information reaches the model, and the controls available in your chosen systems.
Can we start with one workflow?
Yes. A focused workflow makes it easier to define success, identify the data you need, and test whether the implementation helps. Additional sources and use cases can follow as the foundation develops.
Find your next step
What could better
data make possible?
Tell us what you want AI to do, where your data lives, and what’s getting in the way. We’ll start with a conversation about the right next step.
Your inquiry goes directly to Magnetic North.