Pricing

Anticipate your results with Celonis EMS and Swiftask

Swiftask analyzes your process data from Celonis EMS to predict future performance. Make informed decisions before issues even arise.

Result:

Shift from reactive management to a proactive strategy based on real data.

The lack of visibility into future performance

Most companies analyze performance only after results are in. This delay prevents effective corrective action, turning every negative indicator into an urgent crisis to manage.

Main negative impacts:

Delayed response to deviations: Without prediction, you discover operational drifts when it is already too late to act effectively.
Untapped data silos: Your Celonis EMS data holds critical weak signals, but often remains isolated from decision-making tools.
Strategic uncertainty: The absence of predictive models forces managers to fly blind, relying on intuition rather than data.

Swiftask connects to Celonis EMS to turn your process mining data into performance predictions. Our AI agents identify emerging trends and alert you in real time.

BEFORE / AFTER

What changes with Swiftask

Traditional process management

Teams wait for the end of the monthly reporting cycle to note a drop in productivity. A manual investigation is then launched to identify the root cause, weeks after the incident.

Predictive steering with Swiftask

The Swiftask AI agent continuously analyzes Celonis EMS flows. It detects a statistical anomaly and warns you that a performance drop is likely in the next 5 days, leaving you time to act.

Deploying your predictive models in 4 steps

STEP 1 : Connect to your Celonis instance

Integrate Swiftask with Celonis EMS via secure API. Access your key process data without changing your infrastructure.

STEP 2 : Define target indicators

Select the KPIs to monitor (e.g., cycle time, compliance rate, throughput). The agent learns from your historical data.

STEP 3 : Configure AI alerts

Set prediction thresholds. Receive intelligent notifications as soon as the probability of a deviation exceeds your limits.

STEP 4 : Continuous optimization

Refine your agent's prediction models based on user feedback within Swiftask.

Predictive analytics capabilities

Swiftask processes throughput data, bottlenecks, and cycle time variations to build reliable prediction models.

Target connector: The agent performs the right actions in celonis ems based on event context.
Automated actions: Forecast completion dates, detect compliance risks, estimate workload volumes, proactive KPI alerts, optimization suggestions based on process mining.
Native governance: All predictions are correlated with Celonis logs to ensure total transparency on data origin.

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

Each Swiftask agent uses a dedicated identity (e.g. agent-celonis-ems@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.

Strategic advantages for your business

1. Decision-making agility

Anticipate market changes and performance variations before they impact your results.

2. Resource optimization

Allocate your teams where prediction indicates an imminent need for correction.

3. Reduction in operational costs

Avoid costly inefficiencies by intervening early on deviating processes.

4. Data transparency

Base every decision on rigorous predictive analysis derived from your own process mining.

5. Rapid no-code adoption

Launch complex predictive models without hiring a team of data scientists.

Process data security

Swiftask applies enterprise-grade security standards for your celonis ems automations.

Certified integration: Secure connection to Celonis EMS endpoints with strict permission management.
Data isolation: Prediction data remains within your dedicated Swiftask perimeter.
AI governance: You retain full control over the models and prediction rules applied.

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

RESULTS

Measurable performance

Metric	Before	After
Anomaly detection	Retrospective (after incident)	Predictive (before incident)
Reaction time	Several days	Real-time
Forecasting accuracy	Intuitive / Historical	AI and process mining driven

Take action with celonis ems

Shift from reactive management to a proactive strategy based on real data.