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Automate document normalization with the power of Deep Tagger

Swiftask orchestrates Deep Tagger to transform your disparate files into structured, normalized data. Gain precision and speed.

Resultat:

Eliminate formatting inconsistencies. Turn document chaos into a structured business asset.

Manual handling of heterogeneous documents slows down productivity

Receiving documents from multiple sources (PDFs, emails, forms) creates format disparity. Teams waste valuable time manually cleaning, renaming, and filing these documents. This process is error-prone and slows down your entire value chain.

Les principaux impacts négatifs :

Unusable data: Inconsistent formats prevent seamless integration with your business tools (ERP, CRM). Data remains siloed.
High human error risk: Repeated manual processing mechanically increases the rate of entry or classification errors.
Operational bottleneck: Growing document volume overwhelms teams who can no longer keep up with the required processing pace.

Swiftask automates the normalization of your documents via Deep Tagger. The AI analyzes, extracts, and reformats each file according to your standards, with zero human intervention.

AVANT / APRÈS

Ce qui change avec Swiftask

Traditional processing

A team member receives invoices, contracts, or reports in various formats. They must manually extract data, correct fields, rename the file, and move it to the right folder. A tedious, repetitive, and costly task.

The Swiftask + Deep Tagger approach

Each incoming document is intercepted by Swiftask. Deep Tagger analyzes the content, normalizes metadata and format, then injects the clean file into your management system. Everything is processed in seconds, 24/7.

Document normalization: the 4-step workflow

ÉTAPE 1 : Define the target schema

Configure in Swiftask the expected format for your documents: structure, mandatory fields, and naming conventions.

ÉTAPE 2 : Activate Deep Tagger

Connect Deep Tagger to your Swiftask pipeline to benefit from its advanced semantic analysis capabilities.

ÉTAPE 3 : Automate the input flow

Configure triggers (email, API, cloud storage) so Swiftask automatically retrieves new documents.

ÉTAPE 4 : Process and archive

The AI normalizes, validates, and deposits the final document into your storage destination, ready for use.

Advanced normalization capabilities

The agent evaluates document structure, detects format anomalies, extracts key entities, and applies defined normalization rules.

Connecteur cible : L'agent exécute les bonnes actions dans deep tagger selon le contexte de l'événement.
Actions automatisées : Automatic file reformatting. Intelligent metadata extraction. Dynamic naming based on content. Compliance validation against your standards.
Gouvernance native : Swiftask ensures full traceability of every transformation, providing an audit history for every processed document.

Chaque action est contextualisée et exécutée automatiquement au bon moment.

Chaque agent Swiftask utilise une identité dédiée (ex. agent-deep-tagger@swiftask.ai ). Vous gardez une visibilité complète sur chaque action et chaque message envoyé.

À retenir : L'agent automatise les décisions répétitives et laisse à vos équipes les actions à forte valeur.

Why automate your document normalization

1. Instant standardization

All your documents scrupulously respect your data schema, ensuring total consistency.

2. Massive time savings

Total elimination of manual entry and cleaning tasks. Your teams focus on analysis.

3. Increased reliability

AI eliminates human entry errors and ensures constant data quality.

4. Unlimited scalability

Process thousands of documents per day without increasing your headcount.

5. Native integration

Your normalized documents are directly usable by your business applications via Swiftask.

Data security and compliance

Swiftask applique des standards de sécurité enterprise pour vos automatisations deep tagger.

Guaranteed confidentiality: Swiftask and Deep Tagger process your documents with end-to-end encryption.
GDPR compliance: Your data is processed in accordance with European data protection standards.
Full traceability: Every step of the normalization is logged, facilitating internal and external audits.
Controlled access: Granular management of access rights to normalization workflows within your workspace.

Pour aller plus loin sur la conformité, consultez la page gouvernance Swiftask et ses détails d'architecture de sécurité.

RÉSULTATS

AI normalization performance

Métrique	Avant	Après
Processing time per doc	5 to 10 minutes	Less than 5 seconds
Error rate	10% - 15% (human)	Less than 0.5%
Processing capacity	Limited by human	Virtually unlimited (AI)
Standardization	Inconsistent	100% compliant

Passez à l'action avec deep tagger

Eliminate formatting inconsistencies. Turn document chaos into a structured business asset.