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.
AI Agents
deep tagger
Connector deep tagger · Secure OAuth 2.0
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
1
STEP 1 : Define target schema
Configure the fields needed to structure your incoming data in Swiftask.
2
STEP 2 : Activate Deep Tagger
Connect Deep Tagger to intelligently analyze your incoming documents and files.
3
STEP 3 : Automated mapping
Map the entities extracted by the AI to your database fields.
4
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
Productivity gains
Free your teams from repetitive data entry tasks.
Increased accuracy
Eliminate human errors related to manual entry.
Actionable data
Turn your archives into a database ready for reporting.
Scalability
Process thousands of documents without increasing headcount.
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
| Metric | Before | After |
|---|---|---|
| Processing time | Several minutes/doc | A few seconds |
| Error rate | High (human) | Negligible (AI) |
| Volume handled | Limited by staff | Unlimited |
| ROI | Costly | Fast profitability |
Take action with deep tagger
Save hours of manual entry and eliminate classification errors with AI.