“Traditional vision systems rely on deterministic rules—thresholding, edge detection, template matching. Our AI-first systems learn defect signatures from data. This enables detection of non-linear, low-contrast, and previously unseen defects. More importantly, they combine detection, classification, localization, and automated reporting into a single pipeline, rather than stitching together multiple tools.” Dr Vinayak A Prabhu, Chief Executive Officer, Senquire Pte. Ltd.
In the evolving landscape of smart manufacturing, the transition from “customized” to “Standardized” is becoming a strategic necessity. For years, industrial vision systems have operated as fragmented, highly customized installations that were effective in isolation but practically difficult to scale, maintain and synchronize across multiple facilities. As global mandates shift from random sampling to 100% inspection, the industry is hitting a wall with traditional, rule-based legacy systems that struggle to keep pace with modern complexity.
The real value for the industry now lies in repeatability and democratized access. By moving toward standardized, AI-powered machines, manufacturers can finally bridge the gap between pilot projects and plant-wide reality. This shift isn’t just about replacing manual checks; it’s about establishing a consistent performance baseline that functions with the same precision in a Tier 3 supplier’s facility as it does in a flagship OEM plant.
In this exclusive interview, Niranjan Mudholkar, Founder & Editor-in-Chief of The Manufacturing Frontier, speaks with Dr. Vinayak A. Prabhu, CEO of Senquire Pte. Ltd., to explore how standardized AI is dismantling the barriers of high CAPEX and concerns over AI’s lack of transparency. From handling the blistering speeds of FMCG bottling lines to providing “explainable” visual heatmaps for floor managers, Dr. Prabhu outlines a roadmap where AI is no longer a luxury customization, but a modular, plug-and-play pillar of zero-defect manufacturing.
Senquire is moving from highly customized systems to standardized AI-powered machines. What has been the market motivation behind this strategy?
The shift is driven by repeatability and scale. Over the last few years, we saw customers struggling with fragmented, one-off vision systems that are hard to maintain and harder to scale across plants. By standardizing AI-powered machines, we reduce deployment time, improve reliability, and create a consistent performance baseline. This aligns with the industry’s move toward multi-plant rollouts and 100% inspection mandates rather than sampling.
Many legacy systems use traditional machine vision. What specific “AI-first” capabilities do your new machines bring to the table?
Traditional vision systems rely on deterministic rules—thresholding, edge detection, template matching. Our AI-first systems learn defect signatures from data. This enables detection of non-linear, low-contrast, and previously unseen defects. More importantly, they combine detection, classification, localization, and automated reporting into a single pipeline, rather than stitching together multiple tools.
With standardized machines, data collection becomes more uniform. How is Senquire leveraging the data collected from these units to provide predictive insights back to the end-user?
Standardization allows us to aggregate structured inspection data across machines and sites per customer. We use this to build defect libraries, drift detection models, and predictive quality insights. For the end user, this translates into early warnings—tool wear, process deviations, supplier quality issues, before they manifest as large-scale defects.
AI in manufacturing often faces scepticism regarding transparency. How do your machines provide “explainable AI” so that floor managers understand why a part was rejected?
We embed explainability at the output layer. Every rejection is accompanied by visual overlays—heatmaps, bounding boxes, and defect classification tags—along with confidence scores. This allows floor managers to audit decisions quickly and correlate them with process conditions, rather than treating AI as a black box.
How do your standardized AI machines ensuring zero-defect outcomes as assembly lines continue to become more complex to meet market demands?
“Zero-defect” is operationalized through consistency and feedback loops. The machines maintain high sensitivity without increasing false positives by continuously learning from validated inspection outcomes using human-in-the-loop approach. Closed-loop integration with upstream processes where possible ensures that recurring defects trigger corrective actions, not just rejections.
FMCG requires incredible throughput. How do these standardized machines handle the high-speed processing requirements of packaging and bottling lines without compromising inspection quality?
High throughput is handled through edge AI optimization. Models are compressed and deployed on industrial-grade GPUs/accelerators at the edge, ensuring real-time inference. The system architecture is designed for parallel processing, so inspection keeps pace with line speeds without frame drops or latency.
FMCG production lines often run at blistering speeds, sometimes processing hundreds of units per minute. How do your standardized AI machines maintain accuracy at these velocities without becoming a bottleneck on the factory floor?
Accuracy at high speeds is a function of both hardware synchronization and model efficiency. We tightly integrate lighting, optics, triggering, and compute to ensure image fidelity at speed. The AI models are trained specifically on motion-induced variations, which prevents degradation in accuracy even at several hundred units per minute.
From flexible pouches to rigid glass and recycled plastics, FMCG packaging is incredibly varied. How does the AI within your standardized machines Handling “Optical Variety” without requiring a complete system recalibration for every new SKU?
The AI is trained on diverse datasets across materials, finishes, and formats. Instead of recalibrating rules for every SKU, we fine-tune models with minimal additional data using transfer learning mechanisms. This significantly reduces changeover time and allows the same platform to generalize across flexible and rigid packaging.
FMCG environments, particularly in food and beverage, require strict adherence to hygiene standards. Are these standardized machines designed to meet specific wash-down requirements or food-grade safety certifications?
Yes. The machines are designed with industrial hygiene requirements in mind, IP-rated enclosures, food-grade stainless steel options, and compatibility with wash-down environments. We align with food and beverage compliance standards so that inspection systems do not become a point of contamination risk.
In a sector where a misaligned label or a smudged barcode can lead to massive retail penalties, how specifically does your AI-powered inspection compare with the traditional vision systems?
Traditional systems struggle with variability; print distortions, reflections, minor misalignments. Our AI models are robust to these variations while still detecting subtle defects. This reduces both false rejects and missed defects, which directly impacts retail compliance and brand protection.
From the perspective of a plant head with legacy infrastructure, how easily can these AI-powered machines integrate with the existing systems?
Integration is a core design principle. Senquire’s vision inspection modules support standard industrial protocols and can interface with PLCs, MES, and existing line controls in a retrofit configuration. Deployment typically does not require major line modifications, which lowers the barrier for plants with legacy infrastructure.
Custom systems are often CAPEX-heavy. How does standardization change the ROI calculation for your clients, and does this move make AI-powered inspection accessible to Tier 2 and Tier 3 suppliers?
Standardization shifts the model from heavy custom CAPEX to a more predictable, modular investment. Faster deployment and lower engineering overhead improve ROI. It also makes advanced inspection accessible to Tier 2 and Tier 3 suppliers who previously could not justify bespoke systems. We are also introducing Inspection-as-a-Service model whereby our customers do not spend CAPEX but run inspections in OPEX mode, minimizing their upfront investment.
As machines become smarter, the human role changes. How is Senquire simplifying the user interface so that existing floor staff can operate these AI systems without needing extensive training?
We abstract complexity at the interface level. Operators interact with simple dashboards—clear pass/fail indicators, visual defect summaries, and guided workflows. Training time is minimal because the system does not require parameter tuning or vision expertise to operate.
Looking at the next five-year horizon, do you see Senquire expanding this standardized AI model into other verticals like Pharmaceuticals or Electronics, or is the current focus strictly on FMCG and automotive?
Our roadmap is clearly multi-vertical. While FMCG and automotive are immediate focus areas due to volume and defect sensitivity, we are actively expanding into pharmaceuticals and electronics. The underlying platform is designed to be transferable, with domain-specific adaptations layered on top as we scale.
