Seewise.AI · HCCB
In production
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Seewise.AI
By Seewise.AISeewise.AI is pioneering Vision AI Agents for the Manufacturing industries, designed to revolutionize operational efficiency.
- Stage
- In production
- Industries
- Manufacturing
- Logistics
- Automotive and mobility
- Deployment
- Hybrid
Why it is worth it
4M Intelligence Framework Unlike generic CV tools, Seewise maps visual insights to production KPIs across safety, productivity, quality, and compliance, giving plant teams a comprehensive view of losses and root causes. Real-Time AI, Not Just Reporting Unlike competitors who offer post-event analytics, Seewise delivers real-time detection and alerts for safety violations, quality issues, and process deviations — a key factor that has helped us win multiple enterprise deals. Plug-and-Play Edge+Cloud AI Runs seamlessly on NVIDIA Jetson (Orin) for low-latency edge inference, with optional cloud components. No need to overhaul factory infrastructure or install new cameras. Enterprise-Ready & Field-Proven Trusted by leaders like Omron, Panasonic, and Daikin, and successfully deployed in harsh factory environments (dust, vibration, low light) where most AI systems fail.
- Seewise.AI helps manufacturing enterprises solve high-impact operational and safety challenges using Vision AI. Key pain points we address include:
- 1. Manual Monitoring Gaps
- Traditional CCTV monitoring is reactive and error-prone. Seewise automates visual oversight, detecting safety violations, inefficiencies, and anomalies in real-time.
- 2. Unmeasured Losses Across 4Ms (Man, Machine, Material, Method)
- Hidden productivity losses often go unnoticed. We provide granular visibility into operator behavior, machine usage, material flow, and process deviations.
- 3. Safety & Compliance Risks
- Near-miss incidents (like hand intrusions near hazardous zones) often go unrecorded. Our system flags these events instantly, helping prevent accidents and meet compliance.
- 4. Inconsistent Quality Control
- Manual inspections miss visual defects or delay detection. Seewise enables inline, real-time quality inspection to catch issues early and reduce rework.
- 5. Lack of Real-time Visibility
- Leaders lack a live, actionable view of shopfloor performance. Our dashboards and alerts offer real-time insights for faster decision-making.
Opening the deck…
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Best customer profile
- Our ideal customers are mid-to-large manufacturing enterprises operating in sectors such as automotive, metals, electronics, and FMCG. These companies typically have:
- Multiple factories or production lines, generating large volumes of CCTV or machine vision data
- A strong focus on operational efficiency (OEE), quality control, safety compliance, and sustainability (carbon footprint reduction)
- Existing industrial camera infrastructure (IP/CCTV/Smart cameras) but lack advanced AI-driven insights
- A need to automate manual monitoring, detect 4M (Man, Machine, Material, Method) deviations, and generate root-cause insights in real time
Notable paying customers
- Hyundai, Daikin, Panasonic, Pepsi, Coca Cola
Key milestones
- Developed and deployed a proprietary Vision AI platform focused on the 4Ms (Man, Machine, Material, Method) for manufacturing - Secured paid deployments with enterprise customers in automotive, metals, FMCG and electronics sectors - Partnered with Omron as a go-to-market channel in Southeast Asia - Won deals through Omron with Panasonic, Daikin, and other large manufacturers - Deployed solutions at multiple factory sites, with active usage and repeat engagement - Chosen for TheIgnition.AI powered by NVIDIA inaugural cohort - Bootstrapped with customer revenue and founder investments, with plans for seed round - Developing a custom Vision-Language Model (VLM) fine-tuned on manufacturing-specific data - Built real-time AI systems capable of monitoring safety violations, process compliance, and defect detection across industrial environments - Designed a modular architecture for fast deployment, low maintenance, and minimal infrastructure disruption
Customer case
One customer deployment, shown below.