Pricing

Automate spatial data cleaning in Felt

Swiftask connects your AI agents to Felt to normalize, correct, and prepare your geographic data in record time.

Result:

Eliminate geolocation errors and prepare your maps faster.

Manual spatial data cleaning is a bottleneck

Processing large geographic files in Felt requires extreme precision. Formatting errors, missing coordinates, or overlapping polygons slow down your projects significantly.

Main negative impacts:

Geographic accuracy errors: Poorly cleaned data leads to critical misinterpretations in your final maps.
Time wasted on prep: Your teams spend 80% of their time formatting files instead of analyzing cartographic results.
Non-reproducible processes: Without automation, every data update becomes a complex technical challenge prone to new errors.

Swiftask automates data cleaning before integration into Felt. Your AI agents detect anomalies and fix formats instantly.

BEFORE / AFTER

What changes with Swiftask

Manual data management

You download a file, identify errors one by one, fix formats in a spreadsheet, then attempt to import into Felt. If an error persists, you start over.

Swiftask automated workflow

As soon as data arrives, your AI agent validates it, cleans coordinates, harmonizes attributes, and sends it clean and ready to use into your Felt project.

Geospatial preparation: 4 key steps

STEP 1 : Define quality rules

Configure your Swiftask agent with the formatting standards required for your spatial data.

STEP 2 : Connect to Felt

Link your agent to your Felt account via our secure connector for seamless synchronization.

STEP 3 : Automate cleaning

The agent analyzes each data entry and applies necessary corrections automatically.

STEP 4 : Instant visualization

Your cleaned data appears instantly in Felt, ready for your analysis and presentations.

Intelligent processing capabilities

The agent checks coordinate system consistency, geometry validity, and the integrity of associated attribute data.

Target connector: The agent performs the right actions in felt based on event context.
Automated actions: Normalize GeoJSON/KML formats. Automatic correction of outlier coordinates. Merge and remove spatial duplicates. Dynamic layer updates in Felt.
Native governance: Every cleaning operation is documented in the Swiftask audit log for full transparency.

Each action is contextualized and executed automatically at the right time.

Each Swiftask agent uses a dedicated identity (e.g. agent-felt@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 with Swiftask

1. Increased precision

Drastically reduce human errors with strict validation rules.

2. Operational speed

Turn hours of manual work into seconds of automated processing.

3. Scalable projects

Manage growing volumes of geographic data without increasing your workload.

4. Data compliance

Ensure consistent and documented quality for all your map layers.

5. Focus on analysis

Free up time for strategic analysis rather than technical data entry.

Security and reliability

Swiftask applies enterprise-grade security standards for your felt automations.

Data encryption: Your spatial data is processed in a secure, encrypted environment.
Source integrity: Swiftask ensures source data remains unchanged during the cleaning process.
Full control: You validate the cleaning rules applied by the agent at all times.
Complete traceability: Detailed history of every modification made to your datasets.

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

RESULTS

Impact on your productivity

Metric	Before	After
Preparation time	Hours per dataset	Real-time
Error rate	High (manual)	Near zero (automated)
Volume processed	Limited by human	Unlimited
Map updates	Heavy process	Instant

Take action with felt

Eliminate geolocation errors and prepare your maps faster.

Optimize catchment areas with Felt and AI

Next use case