Tarification

Automate database structuring with Deep Tagger

Swiftask integrates Deep Tagger to transform your raw information streams into a perfectly organized database ready for analysis.

Resultat:

Save hours of manual entry and eliminate classification errors with AI.

The chaos of unstructured data

Most enterprise data is stored in unstructured formats: emails, PDFs, reports, notes. Leveraging this information is a daily challenge that consumes valuable time.

Les principaux impacts négatifs :

Loss of critical insights: Dispersed data is impossible to analyze effectively, leading to decisions based on incomplete views.
Expensive manual entry: Your teams spend significant time copying and classifying data instead of focusing on business analysis.
Data inconsistency: Human classification is prone to error, creating disparate data silos that are difficult to reconcile.

Deep Tagger coupled with Swiftask automates the reading, extraction, and structuring of your data. The AI identifies, tags, and inserts your information into your target database.

AVANT / APRÈS

Ce qui change avec Swiftask

Manual data management

A team member receives a document. They open it, identify key fields, and manually enter them into Excel or a CRM. Typing errors are frequent and the process is slow.

Structuring with Swiftask

The document is processed instantly by Deep Tagger. Entities are extracted, normalized, and sent automatically to your database. Zero entry, 100% accuracy.

4 steps to structure your data

ÉTAPE 1 : Define target schema

Configure the fields needed to structure your incoming data in Swiftask.

ÉTAPE 2 : Activate Deep Tagger

Connect Deep Tagger to intelligently analyze your incoming documents and files.

ÉTAPE 3 : Automated mapping

Map the entities extracted by the AI to your database fields.

ÉTAPE 4 : Real-time execution

As soon as data arrives, it is processed, structured, and archived automatically.

Intelligent processing capabilities

The AI analyzes semantic context, document format, and relationships between extracted data.

Connecteur cible : L'agent exécute les bonnes actions dans deep tagger selon le contexte de l'événement.
Actions automatisées : Named entity extraction. Format normalization (dates, currencies). Automatic categorization. Direct export to SQL databases or SaaS tools.
Gouvernance native : Extraction accuracy is continuously monitored to ensure data reliability.

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.

Major operational benefits

1. Productivity gains

Free your teams from repetitive data entry tasks.

2. Increased accuracy

Eliminate human errors related to manual entry.

3. Actionable data

Turn your archives into a database ready for reporting.

4. Scalability

Process thousands of documents without increasing headcount.

5. Standardization

Apply a uniform format to all your incoming data.

Data security

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

Data encryption: All data is encrypted in transit and at rest.
Privacy: Your documents are never used to train third-party models.
GDPR compliance: Processing compliant with European data protection standards.
Access control: Granular rights management on structured data.

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

RÉSULTATS

Structuring performance

Métrique	Avant	Après
Processing time	Several minutes/doc	A few seconds
Error rate	High (human)	Negligible (AI)
Volume handled	Limited by staff	Unlimited
ROI	Costly	Fast profitability

Passez à l'action avec deep tagger

Save hours of manual entry and eliminate classification errors with AI.

Optimisez vos flux de données avec le filtrage intelligent Deep Tagger

Cas d'usage suivant.