Pricing

Automate your content tagging with the power of AI

Swiftask integrates with Deep Tagger to classify, index, and organize your documents automatically, freeing your teams from manual data entry.

Result:

Boost productivity and optimize data accessibility through intelligent, consistent classification.

Document chaos is slowing down your growth

Manual tagging is a source of errors and inconsistencies. Without an automated system, documents pile up without actionable metadata, making information retrieval tedious.

Main negative impacts:

Inefficient information retrieval: Poorly tagged documents are invisible documents. Your teams waste valuable time searching for files lost in disorganized folders.
Data inconsistency: Every user has their own tagging logic. The result: a fragmented knowledge base that is impossible to leverage for analysis.
Hidden operational costs: The time spent manually classifying files represents a significant cost that hinders your organization's innovation and agility.

Thanks to the Swiftask and Deep Tagger integration, your documents are analyzed and tagged automatically upon creation or receipt. The AI applies a consistent taxonomy, instantly.

BEFORE / AFTER

What changes with Swiftask

Manual tagging management

A team member receives a report. They must read the document, choose tags from a list that is sometimes outdated, then apply them. If they forget or make a mistake, the document becomes unfindable.

Automation with Swiftask + Deep Tagger

The document is uploaded. The Swiftask agent sends it to Deep Tagger, which extracts key entities and applies relevant tags. The document is immediately indexed and ready for retrieval.

Enable your tagging automation in 4 steps

STEP 1 : Configure your agent in Swiftask

Create an agent dedicated to document management. Define the document types to monitor and classification goals.

STEP 2 : Integrate Deep Tagger

Connect Deep Tagger to your Swiftask workflow. Configure the taxonomy or let the AI learn your classification standards.

STEP 3 : Define the trigger

Choose the trigger: new file in a cloud, email reception, or form submission. The agent detects new content.

STEP 4 : Deployment and monitoring

Activate the flow. Track tagging performance from the Swiftask dashboard and adjust precision if necessary.

Advanced capabilities of your tagging agent

The agent analyzes semantic content, document structure, and existing metadata to ensure multidimensional classification.

Target connector: The agent performs the right actions in deep tagger based on event context.
Automated actions: Automatic entity extraction (names, dates, amounts). Application of hierarchical tags. Metadata normalization. Automatic routing of documents to the correct folders.
Native governance: The agent learns continuously from your validations to improve its classification accuracy over time.

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.

Operational benefits of automated tagging

1. Instant search

Find any document in seconds thanks to rigorous, automated indexing.

2. Data homogeneity

Apply a uniform taxonomy across your entire organization, without the risk of human error.

3. Supercharged productivity

Eliminate repetitive entry and classification tasks so your teams focus on high-value analysis.

4. Improved governance

Stay in control of your data with complete traceability of every tagging action performed by the agent.

5. Effortless scalability

Manage thousands of documents a day without adding human resources: the AI scales with your needs.

Data security and compliance

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

Secure processing: Your documents are processed via encrypted connections. Swiftask guarantees the integrity of your files.
Access control: Define granular access rights for your agents and workspace users.
Full audit trail: Every tag applied is logged to ensure total traceability compliant with audit requirements.
Guaranteed privacy: Your data is not used to train third-party models without your explicit consent.

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

RESULTS

Impact on your document performance

Metric	Before	After
Classification time	Several minutes per document	Under 5 seconds
Tagging precision	Variable (human error)	Consistent (standardized)
Search time	High (manual search)	Minimal (search by tag)
Management cost	High labor cost	Optimized and predictable

Take action with deep tagger

Boost productivity and optimize data accessibility through intelligent, consistent classification.

Automate database structuring with Deep Tagger

Next use case