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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.

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

STEP 1 : Define entities

Specify in Swiftask the types of information to extract (e.g., order numbers, amounts, client names).

STEP 2 : Configure BART connector

Enable the BART model via the Swiftask interface to process incoming data streams.

STEP 3 : Data mapping

Link the extracted entities to the fields in your target applications (CRM, ERP, SQL databases).

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

1. Superior accuracy

BART outperforms simple rule-based methods thanks to its contextual understanding.

2. Total scalability

Process thousands of documents per hour without adding staff.

3. Standardization

Obtain clean and structured data, ready for business intelligence.

4. Time saving

Free your teams from tedious manual entry tasks.

5. 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

MetricBeforeAfter
Extraction speedMinutes per documentMilliseconds per document
Error rate5% to 10% (human)< 1% (AI)
Cost per documentHighNegligible

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Turn raw text into actionable structured data instantly.

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