Extract entities with BART in your workflows
Swiftask integrates BART to automatically identify and extract key entities (names, dates, locations, organizations) from your text streams.
Result:
Turn raw text into actionable structured data instantly.
AI Agents
bart
Connector bart · Secure OAuth 2.0
Manual document processing is a bottleneck
Manually extracting information from thousands of reports, emails, or contracts is a repetitive task prone to human error. Your teams waste valuable time handling unstructured data.
Main negative impacts:
Data inconsistency
Manual extraction inevitably leads to entry errors and inconsistent formats.
High operational costs
Allocating human resources to data entry is a waste of intellectual capital.
Slow processing
Document volume often exceeds manual processing capacity, delaying decision-making.
By integrating BART, Swiftask automates entity extraction with surgical precision, allowing for immediate ingestion into your databases.
BEFORE / AFTER
What changes with Swiftask
Before automation
An employee reads each document, manually identifies the necessary entities, and enters them into a spreadsheet. This process takes hours and is limited by fatigue.
With Swiftask + BART
As soon as a document is received, the Swiftask agent using BART analyzes the content, extracts the target entities, and automatically updates your information systems.
Setting up BART extraction in 4 steps
1
STEP 1 : Define entities
Specify in Swiftask the types of information to extract (e.g., order numbers, amounts, client names).
2
STEP 2 : Configure BART connector
Enable the BART model via the Swiftask interface to process incoming data streams.
3
STEP 3 : Data mapping
Link the extracted entities to the fields in your target applications (CRM, ERP, SQL databases).
4
STEP 4 : Validation and deployment
Test extraction on a sample and launch automatic processing in production.
Advanced processing capabilities of BART
BART analyzes the contextual structure of language to identify entities even in complex or ambiguous sentences.
- Target connector: The agent performs the right actions in bart based on event context.
- Automated actions: Named Entity Recognition (NER). Extraction of entity relationships. Normalization of date and currency formats. Automatic thematic classification.
- Native governance: Results are exportable directly to your business tools via API or webhook.
Each action is contextualized and executed automatically at the right time.
Each Swiftask agent uses a dedicated identity (e.g. agent-bart@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.
Why choose BART for your extraction
Superior accuracy
BART outperforms simple rule-based methods thanks to its contextual understanding.
Total scalability
Process thousands of documents per hour without adding staff.
Standardization
Obtain clean and structured data, ready for business intelligence.
Time saving
Free your teams from tedious manual entry tasks.
Seamless integration
Connect BART to your entire software ecosystem via Swiftask.
Data security and privacy
Swiftask applies enterprise-grade security standards for your bart automations.
- Data encryption: All processed data is encrypted at rest and in transit.
- GDPR compliance: Swiftask ensures that entity processing meets personal data protection standards.
- Environment isolation: Your data flows are isolated within your secure instance.
To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.
RESULTS
Automated extraction performance
| Metric | Before | After |
|---|---|---|
| Extraction speed | Minutes per document | Milliseconds per document |
| Error rate | 5% to 10% (human) | < 1% (AI) |
| Cost per document | High | Negligible |
Take action with bart
Turn raw text into actionable structured data instantly.