AI is only as good as the data underneath it
Before a model can help anyone, somebody has to make sense of what you already store — millions of rows across an ERP, spreadsheets and systems that were never meant to talk to each other. That groundwork is the work we do.
Most AI projects do not fail on the model. They fail on the data.
The pilot works on a clean sample and falls apart on the real database. What breaks it is almost always the same three things.
One thing under many names
The same partner, product or account entered differently by every department and every year. Until they are one record, nothing can be counted reliably.
Fields that changed meaning
A status or category that meant one thing before a migration and something else after it. Without mapping those breaks, history is not comparable — and a model learns the wrong lesson.
Data nobody can reach
Numbers locked in attachments, personal spreadsheets and systems no one maintains. If a model cannot read it, for practical purposes it does not exist.
From raw records to a base a model can actually use
We work on your data where it lives, in steps you can follow — and we say plainly what is usable, what needs work and what should be left alone.
- 1
We survey what you have
Every table, export and spreadsheet that carries business meaning, with its volume, age and quality written down per source. You get the picture before anyone touches anything.
- 2
We clean and merge
Duplicates resolved, naming unified, missing fields derived where they can be and flagged where they cannot. Nothing is quietly guessed.
- 3
We give the data a structure
A model of your business — partners, items, documents, events — that holds together across sources and survives the next migration.
- 4
We keep it fed
The structure refreshes from the ERP on its own. It is not a one-off export that starts ageing the day it is made.
What a structured base makes possible
This is rarely the goal in itself. It is what everything else rests on.
Forecasts you can trust
A clean history is what makes a forecast worth reading — and what makes it possible to measure how far off it was.
Assistants that answer from your data
An assistant is only as useful as the records behind it. Structured data is the difference between a plausible answer and a correct one.
Reporting that agrees with itself
One definition of a customer, an order and a margin — so two reports stop giving two different numbers.
Not sure what state your data is in?
Show us a sample. We will tell you what is there, what it would take to make it usable, and whether it is worth doing at all — before you commit to anything bigger.