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Automate database structuring with Deep Tagger

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

Result:

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.

Main negative impacts:

  • 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.

BEFORE / AFTER

What changes with 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

STEP 1 : Define target schema

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

STEP 2 : Activate Deep Tagger

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

STEP 3 : Automated mapping

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

STEP 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.

  • Target connector: The agent performs the right actions in deep tagger based on event context.
  • Automated actions: Named entity extraction. Format normalization (dates, currencies). Automatic categorization. Direct export to SQL databases or SaaS tools.
  • Native governance: Extraction accuracy is continuously monitored to ensure data reliability.

Each action is contextualized and executed automatically at the right time.

Each Swiftask agent uses a dedicated identity (e.g. agent-deep-tagger@swiftask.ai ). You keep full visibility on every action and every sent message.

Key takeaway: The agent automates repetitive decisions and leaves high-value actions to your teams.

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 applies enterprise-grade security standards for your deep tagger automations.

  • 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.

To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.

RESULTS

Structuring performance

MetricBeforeAfter
Processing timeSeveral minutes/docA few seconds
Error rateHigh (human)Negligible (AI)
Volume handledLimited by staffUnlimited
ROICostlyFast profitability

Take action with deep tagger

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

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