Automate your V7 Go dataset sorting with Swiftask
Swiftask automates the sorting, classification, and organization of your images in V7 Go. Accelerate your training data preparation for computer vision.
Result:
Drastically reduce dataset preparation time and improve the quality of your training data.
AI Agents
v7 go
Connector v7 go · Secure OAuth 2.0
Manual dataset sorting slows down your vision projects
Data preparation is the most time-consuming step in computer vision. Manually sorting thousands of images, categorizing them by type or quality in V7 Go is a repetitive task that overwhelms your annotation teams.
Main negative impacts:
Operational bottlenecks
Increasing data volumes make manual sorting impossible to maintain without impacting model delivery timelines.
Labeling inconsistencies
Manual sorting is prone to human error, which degrades the overall quality of your training dataset.
High prep costs
Using experts or annotators for simple sorting is a waste of high-value resources.
Swiftask automates your V7 Go dataset sorting. Through AI agents, your images are automatically classified, filtered, or distributed into the correct folders based on your criteria.
BEFORE / AFTER
What changes with Swiftask
Without Swiftask
A team receives 5,000 new images. An annotator must open V7 Go, examine each image, decide its class, and move it manually to the corresponding folder. This process takes days and delays the entire development cycle.
With Swiftask + V7 Go
As soon as images are uploaded, the Swiftask agent analyzes them, sorts them automatically according to your business rules, and places them into the correct V7 Go datasets. The dataset is ready for annotation upon arrival.
Automate your data workflow in 4 steps
1
STEP 1 : Define your sorting rules
Configure sorting criteria in Swiftask (e.g., by detected object, confidence level, or metadata).
2
STEP 2 : Connect your V7 Go instance
Use the secure integration to allow Swiftask to access your V7 Go datasets with read/write permissions.
3
STEP 3 : Activate the intelligent trigger
The agent activates upon every new upload or on a regular schedule to process your new files.
4
STEP 4 : Monitor performance
View sorting logs and adjust classification rules in real-time from the dashboard.
Advanced features for V7 Go
The agent analyzes visual content or associated metadata for each file before determining its destination.
- Target connector: The agent performs the right actions in v7 go based on event context.
- Automated actions: Move images between datasets. Apply automatic tags. Filter out low-quality images. Distribute data for training/test sets.
- Native governance: All sorting actions are tracked to ensure complete reproducibility of your datasets.
Each action is contextualized and executed automatically at the right time.
Each Swiftask agent uses a dedicated identity (e.g. agent-v7-go@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.
Benefits for your ML teams
Massive speed gains
Prepare your datasets in minutes instead of days.
Increased standardization
Eliminate human error with consistent, automated sorting logic.
Resource optimization
Your annotators focus only on complex cases that require human expertise.
Unlimited scalability
Manage increasingly large datasets without growing your team.
Seamless integration
Connect Swiftask to V7 Go without changing your existing infrastructure.
Vision data security
Swiftask applies enterprise-grade security standards for your v7 go automations.
- Secure API connection: Uses V7 Go access tokens with restricted permissions (least privilege principle).
- Full audit trail: Every file movement is logged in the Swiftask dashboard.
- Compliance: Your data stays within your secure environment.
- Full control: You maintain control over all applied sorting rules.
To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.
RESULTS
Impact on your productivity
| Metric | Before | After |
|---|---|---|
| Dataset prep time | Several days | A few minutes |
| Sorting precision | Variable (human) | Constant (AI) |
| Volume processed | Limited by humans | Scalable automatically |
| Operational cost | High | Reduced by 80% |
Take action with v7 go
Drastically reduce dataset preparation time and improve the quality of your training data.