Pricing

Automate document normalization with the power of Deep Tagger

Swiftask orchestrates Deep Tagger to transform your disparate files into structured, normalized data. Gain precision and speed.

Result:

Eliminate formatting inconsistencies. Turn document chaos into a structured business asset.

Manual handling of heterogeneous documents slows down productivity

Receiving documents from multiple sources (PDFs, emails, forms) creates format disparity. Teams waste valuable time manually cleaning, renaming, and filing these documents. This process is error-prone and slows down your entire value chain.

Main negative impacts:

Unusable data: Inconsistent formats prevent seamless integration with your business tools (ERP, CRM). Data remains siloed.
High human error risk: Repeated manual processing mechanically increases the rate of entry or classification errors.
Operational bottleneck: Growing document volume overwhelms teams who can no longer keep up with the required processing pace.

Swiftask automates the normalization of your documents via Deep Tagger. The AI analyzes, extracts, and reformats each file according to your standards, with zero human intervention.

BEFORE / AFTER

What changes with Swiftask

Traditional processing

A team member receives invoices, contracts, or reports in various formats. They must manually extract data, correct fields, rename the file, and move it to the right folder. A tedious, repetitive, and costly task.

The Swiftask + Deep Tagger approach

Each incoming document is intercepted by Swiftask. Deep Tagger analyzes the content, normalizes metadata and format, then injects the clean file into your management system. Everything is processed in seconds, 24/7.

Document normalization: the 4-step workflow

STEP 1 : Define the target schema

Configure in Swiftask the expected format for your documents: structure, mandatory fields, and naming conventions.

STEP 2 : Activate Deep Tagger

Connect Deep Tagger to your Swiftask pipeline to benefit from its advanced semantic analysis capabilities.

STEP 3 : Automate the input flow

Configure triggers (email, API, cloud storage) so Swiftask automatically retrieves new documents.

STEP 4 : Process and archive

The AI normalizes, validates, and deposits the final document into your storage destination, ready for use.

Advanced normalization capabilities

The agent evaluates document structure, detects format anomalies, extracts key entities, and applies defined normalization rules.

Target connector: The agent performs the right actions in deep tagger based on event context.
Automated actions: Automatic file reformatting. Intelligent metadata extraction. Dynamic naming based on content. Compliance validation against your standards.
Native governance: Swiftask ensures full traceability of every transformation, providing an audit history for every processed document.

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.

Why automate your document normalization

1. Instant standardization

All your documents scrupulously respect your data schema, ensuring total consistency.

2. Massive time savings

Total elimination of manual entry and cleaning tasks. Your teams focus on analysis.

3. Increased reliability

AI eliminates human entry errors and ensures constant data quality.

4. Unlimited scalability

Process thousands of documents per day without increasing your headcount.

5. Native integration

Your normalized documents are directly usable by your business applications via Swiftask.

Data security and compliance

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

Guaranteed confidentiality: Swiftask and Deep Tagger process your documents with end-to-end encryption.
GDPR compliance: Your data is processed in accordance with European data protection standards.
Full traceability: Every step of the normalization is logged, facilitating internal and external audits.
Controlled access: Granular management of access rights to normalization workflows within your workspace.

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

RESULTS

AI normalization performance

Metric	Before	After
Processing time per doc	5 to 10 minutes	Less than 5 seconds
Error rate	10% - 15% (human)	Less than 0.5%
Processing capacity	Limited by human	Virtually unlimited (AI)
Standardization	Inconsistent	100% compliant

Take action with deep tagger

Eliminate formatting inconsistencies. Turn document chaos into a structured business asset.