Seewise 2025 - Building Vision AI That’s Easier to Deploy, Scale, and Trust

Looking back at 2025, this year was an important turning point for us at Seewise.
As a product team, we moved away from treating every deployment as a one-off project and instead focused on building a platform that scales smoothly across customers, use cases, and environments.
Our goal was simple,
Make Vision AI easier to implement, faster to go live, and more reliable in real industrial conditions.
Here is a glimpse of how that journey unfolded

From Projects to Production-Ready Deployments

Looking back at 2025, this year was an important turning point for us at Seewise.
As a product team, we moved away from treating every deployment as a one-off project and instead focused on building a platform that scales smoothly across customers, use cases, and environments.
Our goal was simple,
Make Vision AI easier to implement, faster to go live, and more reliable in real industrial conditions.
Here is a glimpse of how that journey unfolded
What went live in 2025:

• 20+ live production projects
• 12 successful POCs
• 200+ cameras deployed
• Across on-prem, edge, and cloud environments

These deployments covered use cases like safety monitoring, process compliance, quality inspection, and operational visibility.
More importantly, several customers renewed their licenses a clear signal that the platform was delivering value beyond the initial go-live.
For us, renewals mattered as much as new projects.
They validated:

• Stable performance in real factory conditions
• Models working reliably despite lighting and environment changes
• Continuous insights that teams actually use

Product Focus: Template-Based Implementation

• One of the biggest product decisions we made in 2025 was to standardize implementations using templates.
• Instead of starting from scratch every time, we created reusable templates that pre-configure everything needed to go live faster, including:
• Vision model setup
• SOP logic
• Events and alerts
• Dashboards and reports
• Integration touchpoints

Templates delivered this year

• Safety Template – For PPE detection, unsafe acts, alerts, and compliance dashboards.
• Galvanization (Galv) Template – Designed around SOPs for dipping operations, material tracking, retention time checks, and violation logging.
• 4M Template (Man, Machine, Material, Method) – A structured way to link Vision AI outputs to root-cause analysis.

These templates helped reduce implementation time significantly and made deployments more predictable, both for customers and for our internal teams.

R&D Progress: Making Models Smarter After Go-Live

While deployments scaled, our R&D focus was on what happens after a model goes live.
We initiated work on general models that can be reused across industries and use cases.
These act as strong starting points, reducing the effort needed for customer-specific training.
In parallel, the team cracked a key challenge,
How to continuously improve models after deployment.
We built internal mechanisms to,

• Identify images that fail due to lighting, camera angles, or environment changes
• Analyze why failures happen
• Decide when data augmentation or retraining is required
• Improve accuracy without disrupting live systems

This ensures models don’t stay static, they learn and improve with real-world usage.

SIA Chatbot - Data Without the Dashboards

Another addition to the platform is SIA, our conversational assistant.

SIA allows users to simply ask questions and get the data they need safety metrics, violations, trends, or reports without navigating dashboards or reports. Based on customer needs, SIA can be enabled to fetch historical insights, making data access faster and more intuitive for every role.

Key Learnings from 2025

A few clear takeaways shaped our product thinking,

• Templates scale better than custom implementations
• General models reduce friction and speed up delivery
• Reliability at the edge matters as much as model accuracy
• R&D must run alongside real customer deployments

Looking Forward

2025 was about laying the foundation building reusable components, smarter workflows, and a platform that teams can rely on.

The road ahead is about scaling faster, adding intelligence, and making Vision AI feel less complex and more natural to use.

We’re excited about what’s next.