Introduction
You have the documents, not the data
Most companies have digital archives that are de facto paper binders online. PDFs, scans, files with no common structure. Finding specific information takes minutes, sometimes hours. Automating document work is not a matter of innovation - it's an operational need.
GenAI's new tools help break down the barrier between content and data. Below you will find 5 specific scenarios where you can use GenAI to organize documents, speed up processes and improve the quality of decisions.
1. Reading data from contracts and administrative decisions
This is one of GenAI's most common use cases. There is a lot of key data in documents such as contracts or administrative decisions: dates, case numbers, locations, financial terms, parties to the contract. The problem is that they do not always appear in the same place, they are not marked with headings, and their layout varies depending on the source. GenAI can “read” documents like a human - it understands context, recognizes proper names, can separate sections and extract data even from text written in ambiguous official language. This is especially useful when processing a backlog of documents that has been sitting in an archive for years.
2. Categorization of documents without structure
In many companies, the name of a file says nothing about its contents. Folders are full of files like “scan_2021.pdf” or “file123_final_ver7.pdf.” Implementing GenAI makes it possible to automate the classification of such documents based on their content, not their name. The model can determine whether a document is a contract, a protocol, a decision, an addendum or an invoice - and assign it an appropriate tag or path. Such categorization is the first step in building order and preparing the ground for further automation, such as routing to appropriate systems or departments.
3. Validate data in documents
Errors in documents can be costly. An incorrect effective date of a contract, an incorrect decision number or a discrepancy between documents are problems that are difficult to catch manually on a larger scale. GenAI can act as a validation layer: checking data against patterns, detecting anomalies and flagging discrepancies. It can also compare documents with each other and assess whether they contain the same data that should be in compliance. This is a huge time saver for operations teams and guarantees greater consistency.
4. Create metrics and reports based on documentsntów
The data that is in the documents can be automatically transformed into reports and metrics. The number of active contracts, obligations expiring in the coming months, distribution of costs by location, all of this can be generated without manually typing the data into Excel. GenAI extracts data from documents and structures it so that it can be analyzed in BI (e.g. Looker Studio, Power BI). The report is not created once - it can be updated with the influx of new documents.
5. Handling the influx of new documents
A system based on GenAI can operate continuously, reacting to new files coming in. The document goes to the cloud, the model classifies it, extracts the data, validates it, and then stores it in the database and archive. The user gets a ready-made record that he can use in his daily work. In this way, we not only process the past (backlog), but build the process of the future: current documents immediately go to the right place, in the right structure.
What's next? Summary
Each of these scenarios can be implemented in stages. It is not necessary to build everything at once. Just start with a specific problem, define the data and process, and then iteratively develop the system.
If you want to know what this might look like for you, start by preparing your team and your data. Implementing GenAI for records work doesn't start with choosing a model, it starts with auditing your documents, identifying your business goals and defining what data is critical to your organization.
At Alterdata, we can help your company through this entire process: from asset analysis, to solution design, to integration with your data environment. Instead of one-size-fits-all templates, we build systems tailored to your problem.
Want to see what this could look like for you? Check out the video below or contact us.
