The single biggest excuse we hear for delaying AI initiatives is: 'Our data is too messy.'
We've seen companies spend $100K+ and 12 months hiring data cleaning consultants to organize databases, only to realize that the models didn't need the columns structured that way. The truth is, modern LLMs are incredibly good at parsing messy, unstructured text.
What Doesn't Matter
Inconsistent formatting. A mix of dates, addresses, and currency strings across columns doesn't bother an LLM. It can identify patterns and extract information regardless of syntax.
Duplicate records. Clean databases are nice, but duplicate entries don't break prompt chains. We can filter duplicates programmatically during ingestion.
What Actually Matters
Access. Can the model retrieve the files? If your data is locked behind closed legacy mainframes with no API, the system is dead. Connectivity is the real bottleneck.
Contextual Integrity. Do you actually record the variables that decide the outcome? If your agents decide client approvals based on phone conversations that are never logged, the model cannot replicate the logic. Trapped context is the enemy of automation.