Data Foundation
Most AI projects fail because the data isn't ready. We fix that first.
What it is
The unglamorous work that makes everything else possible.
Your data lives in fifteen places. PDFs, spreadsheets, legacy databases, SharePoint folders, custodian portals, someone's email. No AI tool — ours or anyone else's — works on data that fragmented.
Data Foundation is the engagement where we fix that. We extract data from wherever it lives, clean it, structure it, and consolidate it into a queryable dataset your team and your automation can actually use. It's the prerequisite for everything else we build.
We do this as a fixed-price, one-time engagement. Most clients start here.
What's included
Extraction and cleaning
Structured dataset
Validation report
Handover and documentation
Three to six weeks. Predictable delivery.
Week 1 — Discovery and audit
We map your data sources, understand the use cases, and finalize scope.
Weeks 2-4 — Build
Extraction, cleaning, structuring. Weekly progress updates with sample outputs.
Weeks 5-6 — Validation and handover
You review the dataset, we adjust, then we hand over everything with full documentation.
Fixed-price engagements from $5k
Pricing depends on source count and complexity, not hourly rates. We scope it in the discovery call and quote a fixed price before anything starts. No surprises.
Simple
Includes 1-3 sources of structured data.
From $5,000
Standard
Includes 3-7 sources of mixed structured and unstructured.
From $8000 - $12,000
Complex
Includes 7+ sources of heavy unstructured data and customer integrations.
From $12,000 - $25,000
How to know if Data Foundation is the right starting point.
You've tried ChatGPT or another AI tool and it didn't work on your real data.
This is the most common reason. Off-the-shelf tools assume clean data. Yours isn't.
Your team rebuilds the same report every month.
If reporting is manual because data lives in different systems, Data Foundation fixes the source problem.
You're planning to deploy AI but don't know where to start.
Start here. Every workflow we build later depends on this layer.
You already have a data warehouse.
Sometimes you don't need this tier — we'll tell you in discovery. If your warehouse is well-structured, we skip straight to Workflow Automation.
What comes next.
Most clients move from Data Foundation directly into Workflow Automation — the cleaned data unlocks the automation. Some pause for a quarter to let their team use the dataset before building automation on top. Both work.
Find out if your data is ready for AI.
A 30-minute call. We'll ask about your data landscape and tell you honestly whether Data Foundation is the right starting point — or whether you're ready to skip ahead.
