01
Finding a document is often easier than knowing what it is
Organizations accumulate contracts, reports, policies, invoices, correspondence, project documents and other files. Someone eventually needs to determine:
- What type of document is this?
- Which department owns it?
- Which project does it belong to?
- What retention category applies?
- Which workflow should receive it?
These are classification decisions.
02
Train with information you already have
StayCharted AMT doesn't have to be the system that extracts document contents. Your existing application can provide text or metadata such as:
- Document title
- Filename
- Extracted text
- Description
- Sender
- Existing metadata fields
That data can be used to train the model. For example:
| Document Text / Description | Type |
|---|---|
| Mutual confidentiality agreement between... | NDA |
| Invoice No. 88724 payment due... | Invoice |
| Candidate employment terms... | Employment Agreement |
03
Your organization determines the categories
Generic document AI may recognize broad document types. But businesses frequently need more specific distinctions. For example, a legal organization might distinguish:
- Client Agreement
- Engagement Letter
- NDA
- Vendor Contract
- Amendment
- Statement of Work
Another company might care about completely different categories. StayCharted AMT learns the taxonomy represented in your training data.
04
Add it to a document workflow
StayCharted AMT can sit between document ingestion and whatever application manages the resulting file.
- Document
- Text / Metadata Extraction
- StayCharted AMT Prediction API
- Document Category
- DMS / Workflow / Repository
StayCharted AMT handles the prediction. The surrounding application handles document storage, access controls and workflow.
05
One focused role
Train and test document categories in StayCharted AMT, then use the predictions in your existing document system.