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The Data Problem: Garbage in = Garbage out
Legal technology can only work with the information it is given. That sounds obvious, but it is easy to overlook when a team is implementing a contract lifecycle management system, migrating legacy documents or introducing AI-assisted review.
A system may extract a field consistently and still produce information that is incomplete, ambiguous or wrong for the decision a user needs to make. A field called “renewal date,” for example, is only useful if everyone agrees what it means: the end of the current term, the deadline to give notice, or the date on which an automatic renewal takes effect. Those dates can be different.
AI does not remove the need to define the problem. It makes clear definitions, good source data, carefully designed instructions and meaningful quality checks even more important. If the underlying documents are duplicated, if a field has been populated inconsistently, or if the instructions do not explain how to handle exceptions, a more powerful model may simply produce unreliable answers faster.
The practical response is not always to start again or extract everything from scratch. First, understand what the business needs to know and how the information will be used. Then assess the data you already have, identify the gaps that matter, define the fields and instructions precisely, and test the results against real examples — including awkward edge cases.
The objective is not perfect data for its own sake. It is information that is reliable enough for the decision or workflow it needs to support, with uncertainty and exceptions handled openly.
That is the work between the document and the answer: making legal and business knowledge explicit, structured and usable by technology.
DB LEGALTECH Solutions helps legal-tech companies and legal teams assess contract data, define extraction requirements, design AI/LLM instructions, support migrations and build practical quality controls. The goal is simple: make technology more useful by improving the information and processes behind it.