Inventory and taxonomy
We audit the document types the company handles, monthly volumes, common filing errors and search requirements. Together we define the classification taxonomy: categories, subcategories, business rules and known exceptions.
IA documental
AI Document Classification
Your company generates invoices, contracts, delivery notes, reports and emails every day. Without a system to classify them, teams waste hours searching for what they need and filing errors pile up. We deploy AI models that read, categorise and route each document to the right place — automatically, without manual intervention.
Most SMEs manage their documents the way they always have: shared folders with names that only the person who created them understands, emails with attachments that are never filed, and searches that eat into real working time. When volume grows, the chaos scales with it. A professional firm with 20 employees may handle thousands of documents a month; a distribution company, tens of thousands between delivery notes, purchase orders and invoices. Without automatic classification, that volume becomes noise.
AI document classification solves this problem at the layer where it happens: the moment the document enters the system. The models analyse the content of the file — not just its name or extension — compare it against the categories your company defines and place it in the correct folder, dossier or record. Whether the document arrives by email, scanner or via an integration with your ERP or CRM, the process is the same. The system learns from exceptions and improves over time.
At Summum IA we design the solution around your real workflow, not a generic demo. We map the document types your company handles, train or configure the model with real examples, connect it to your existing tools (Microsoft 365, Google Workspace, ERP, document management system) and set up the routing rules. The result is a pipeline your team operates without any technical knowledge: the document comes in, gets classified and is available in the right place within seconds.
The AI Document Classification process.
The process · four stagesModel design and pilot test
We select the appropriate technical architecture (lightweight classifier, language model, OCR + structured extraction) based on document type and volume. We run a pilot with real documents from your company to measure accuracy before deployment.
Integration into your workflow
We connect the classifier to your document entry points: email inbox, scanners, upload portals, your ERP or document management system APIs. We configure automatic routing rules and alerts for cases that require human review.
Go-live, tuning and support
We deploy the system in production with a supervised period to correct misclassifications and feed the model. We train the team that handles exceptions and deliver a metrics dashboard (accuracy, volume processed, documents under review) so you can measure performance.
What is included
What AI Document Classification includes.
The operational detail: what we deliver as part of the work and what we keep alive afterwards.
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Initial document audit
Map of document types, volumes, input channels and loss or error points in the company's current filing setup.
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Classification taxonomy
Structured catalogue of categories and subcategories tailored to the business, with priority rules and routing criteria for ambiguous cases.
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Configured and tested classification model
System trained or configured with real company documents, validated against accuracy metrics before production deployment.
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Integration with existing tools
Connection of the classifier with current systems: email, ERP, document management (SharePoint, Google Drive, Dynamics, Odoo, Holded or others), without replacing your core software.
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Metrics dashboard and exception review
Operational interface for the team to view documents pending review, correct classifications and feed the model back without technical intervention.
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Documentation and team training
Operations manual, updatable taxonomy guide and a training session for users who manage the document workflow.
Summum cluster
How it connects with its sisters.
AI document classification is the foundation for other Grupo Summum services: well-organised documents feed more accurate semantic searches, AI agents that act on dossiers and assisted contract reviews; when volume or regulations require it, Summum Sistemas connects the classifier to the ERP or corporate document management system.
ia
Summum ia
Once classified, documents can be located by meaning, not just by file name: semantic search lets you find in seconds what used to mean browsing through folders.
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Classified and structured documents are the ideal raw material for RAG systems that allow natural-language questions to be asked about the company's document archive.
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Metadata extracted during classification (type, date, supplier, amount) can be fed into dashboards to analyse document flow and identify operational bottlenecks.
View service →Frequently asked questions about AI Document Classification.
What types of documents can the system classify?
Invoices, delivery notes, contracts, purchase orders, quotes, payslips, technical reports, emails with attachments, scanned forms and any document in PDF, Word, Excel or image format. The system is configured for the document types your company handles; there is no fixed list.
Does the system require us to change our current software?
No. The classifier acts as a layer between entry points (email, scanner, portal) and the tools you already use. It connects via API or through native connectors to Microsoft 365, Google Workspace, Dynamics, Odoo, Holded, Sage and other common SME systems. No migration or replacement is needed.
How accurate is the automatic classification?
It depends on the document types and the quality of the training examples. In projects with well-defined taxonomies and homogeneous documents (invoices from regular suppliers, standard-format delivery notes), automatic classification typically exceeds 90% without manual intervention. Ambiguous or new documents are routed for human review, and those corrections improve the model over time. We present real accuracy metrics from the pilot before signing off on final deployment.
How long does it take to go live?
The typical lead time from initial diagnosis to first production deployment is four to eight weeks, depending on taxonomy complexity and the number of integrations required. The pilot phase runs for one to two weeks before final deployment.
What happens with documents the system cannot classify?
They are routed to a manual review queue with the system's suggestion and its confidence level. A team member reviews, confirms or corrects the classification, and that decision feeds back into the model. The goal is for the review queue to shrink progressively as the system learns the specific cases in your company.