Supercharge your logistics with Tookan smart scheduling
Swiftask connects your AI agents to Tookan to automate your route planning. Analyze orders and assign deliveries in seconds.
Result:
Increase responsiveness and cut operational costs with optimized planning and zero manual effort.
AI Agents
tookan
Connector tookan · Secure OAuth 2.0
Manual route scheduling slows down your growth
Managing dozens of daily deliveries via Tookan requires complex coordination. Dispatchers spend hours juggling addresses, time slots, and driver availability.
Main negative impacts:
Underutilized resources
Routes are not optimized, leading to unnecessary mileage and increased driver working hours.
Dispatch errors
Manual data entry and human management of schedules increase the risk of omissions or scheduling conflicts.
Limited reactivity
In case of unexpected events, manual rescheduling is too slow to maintain high service standards.
Swiftask puts AI at the heart of Tookan. Our agents automatically analyze incoming orders and generate optimized schedules based on all your business constraints.
BEFORE / AFTER
What changes with Swiftask
Traditional management
A dispatcher receives an order list, manually checks driver locations in Tookan, and tries to assign tasks one by one, wasting valuable time.
Augmented planning with Swiftask
As soon as an order arrives, the Swiftask AI agent analyzes it, identifies the best available driver in Tookan, and schedules the stop instantly.
Deploy your scheduling agent in 4 steps
1
STEP 1 : Initialize the flow in Swiftask
Connect your Tookan instance to Swiftask via API to give the agent access to delivery data.
2
STEP 2 : Define optimization rules
Set your preferences: client priority, vehicle capacity, time windows, and geographical zones.
3
STEP 3 : Activate the AI agent
The agent starts monitoring Tookan entries and processing requests according to your defined rules.
4
STEP 4 : Monitor and adjust
Use the Swiftask dashboard to validate assignments and fine-tune optimization parameters in real time.
Advanced optimization capabilities
The agent considers geolocation, real-time traffic, strict delivery windows, and current fleet workload.
- Target connector: The agent performs the right actions in tookan based on event context.
- Automated actions: Automatic driver assignment. Intelligent grouping of orders by zone. Dynamic schedule updates during cancellations. Proactive alerts for potential delays.
- Native governance: All agent decisions are logged in Swiftask, ensuring full transparency in your logistics operations.
Each action is contextualized and executed automatically at the right time.
Each Swiftask agent uses a dedicated identity (e.g. agent-tookan@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.
Major operational benefits
Reduced transport costs
Denser and better-optimized routes significantly lower fuel and maintenance costs.
Increased productivity
Free your teams from repetitive planning so they can focus on exception management.
Higher customer satisfaction
Faster deliveries within promised windows directly improve the customer experience.
Business scalability
Manage 10 or 1000 orders with the same efficiency thanks to AI automation.
Seamless integration
Connect Tookan to your other tools (CRM, ERP) via Swiftask for a unified data flow.
Data reliability and security
Swiftask applies enterprise-grade security standards for your tookan automations.
- Secure Tookan API: Data exchange between Swiftask and Tookan is encrypted and authenticated.
- Access control: Precisely control AI agent permissions within your Swiftask workspace.
- Full audit trail: Every assignment made by the agent is traceable, facilitating reporting and performance analysis.
- Compliance: Robust infrastructure meeting B2B security standards.
To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.
RESULTS
Impact on your key performance indicators
| Metric | Before | After |
|---|---|---|
| Planning time | Several hours / day | Real-time (seconds) |
| Fleet utilization | 60-70% | 85-95% |
| Scheduling errors | Frequent | Near zero |
| Logistics cost per package | High | Optimized (-20% on average) |
Take action with tookan
Increase responsiveness and cut operational costs with optimized planning and zero manual effort.