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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.

Resultat:

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.

Les principaux impacts négatifs :

  • 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.

AVANT / APRÈS

Ce qui change avec 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

ÉTAPE 1 : Define entities

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

ÉTAPE 2 : Configure BART connector

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

ÉTAPE 3 : Data mapping

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

ÉTAPE 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.

  • Connecteur cible : L'agent exécute les bonnes actions dans bart selon le contexte de l'événement.
  • Actions automatisées : Named Entity Recognition (NER). Extraction of entity relationships. Normalization of date and currency formats. Automatic thematic classification.
  • Gouvernance native : Results are exportable directly to your business tools via API or webhook.

Chaque action est contextualisée et exécutée automatiquement au bon moment.

Chaque agent Swiftask utilise une identité dédiée (ex. agent-bart@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.

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 applique des standards de sécurité enterprise pour vos automatisations bart.

  • 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.

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

RÉSULTATS

Automated extraction performance

MétriqueAvantAprès
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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