Loopr AI

At Loopr AI, we’re proud to celebrate the women shaping the future of manufacturing. Our CEO, Priyansha Bagaria, recently shared her list of women leaders who have inspired her: “Women Who Build”. Its a tribute to the incredible leaders redefining what leadership looks like across industrial sectors. From aerospace and defense to heavy equipment, mobility, and advanced manufacturing, these women are leading some of the most complex organizations in the world and driving innovation at global scale. Here’s to the women who build systems, factories, technologies, and the future. Happy Women’s Day.

[Supplier Quality] Scorecards Should Predict. Not Just Report. Most conversations about supplier quality focus on PPM and quarterly reviews. Fewer focus on what happens weeks before those numbers turn red. A typical supplier scorecard shows last month’s defects, chargebacks, and warranty claims. By then, the factory has already absorbed the cost. Failure rarely starts with a spike. It starts with drift: small increases in variability, minor fit issues, rework rising on one shift. Across plants, teams often see: ✅ Slight defect upticks that don’t cross thresholds ✅ Batch-to-batch inconsistencies ✅ Scrap linked to one component ✅ Warranty signals that trace back months We’ve seen supplier quality evolve: Then: static PDFs, manual reviews, reactive CAPAs. Now: digital dashboards, MES integrations, real-time inspection feeds. But one challenge remains: 🔎 Most scorecards are still lagging indicators. 🔎 Variability is averaged out. 🔎 Incoming inspection isn’t linked to downstream impact. 🔎 Alerts come after damage is visible. The shift is simple: If we want fewer escapes, we must detect instability early. Supplier quality isn’t controlled by reporting. It’s controlled by prediction.

[Women in Manufacturing] Quality Is Built on the Shop Floor Most conversations about manufacturing focus on automation. Fewer focus on the people who built the systems before automation arrived. Talita Gonzaga started at 18 as the only woman in a stamping plant department of 45 men. It wasn’t easy, she had to prove herself constantly. What kept her there was impact: tracking metrics, training operators, and leading Kaizens shift by shift. Over two decades across Brazil and the U.S, she has: ✅ Led 48 Kaizens ✅ Trained 600+ people ✅ Helped teams move from paper folders to real-time quality dashboards She’s seen quality evolve: Then: hard-copy records, manual checks, reactive fixes. Now: digital boards, MES systems, real-time feedback. But one challenge remains: 🔎 Technology moves faster than training. 🔎 AI and visual inspection tools are entering factories. 🔎 Adoption is still early. 🔎 Manual inspections still dominate many lines. Her view is simple: If we bring in new technology, we must train people at the same pace. Manufacturing’s future isn’t just about smarter tools. 🎥 Watch the full conversation below.

[Whitepaper] How to Turn Manual Inspections into AI-Ready Data Most factories think inspections are about catching defects before shipment. They’re not thinking about what happens to the data after the inspection. A typical plant runs thousands of inspections every week. Operators tick boxes, type notes into the ERP, maybe add a comment. The defect is corrected. The shipment moves. But nothing is captured in a structured way: no standardized images, no consistent defect labels, no dataset you can later use for automation. When leadership decides to “add AI,” they discover something uncomfortable: there’s nothing clean enough to train it on. This whitepaper explains a different approach. Start by fixing how inspections are captured. Now things look very different: ✅ Every inspection includes photos, timestamps, and user IDs ✅ Defects are categorized consistently instead of described differently by each shift ✅ Manual inspections quietly become labeled training data ✅ AI automation is deployed only where scrap, rework, or warranty exposure justify it Instead of jumping straight to automation, the paper lays out a practical path: Digitize → Standardize → Aggregate → Automate. Identify one high-CoPQ station. We’ll show you how to digitize it and prepare it for automation. DM us for the full whitepaper.

5 signals that scrap is coming

𝗘𝗩 𝗯𝗮𝘁𝘁𝗲𝗿𝘆 𝗹𝗶𝗻𝗲𝘀 𝗮𝗿𝗲 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗿𝗶𝘀𝗸𝘀 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝘂𝘁𝗼 𝗽𝗹𝗮𝗻𝘁𝘀 𝗻𝗲𝘃𝗲𝗿 𝗵𝗮𝗱 𝘁𝗼 𝗺𝗮𝗻𝗮𝗴𝗲 EV battery production looks like automotive manufacturing but It isn’t. Battery assembly is high-voltage, configuration-driven, and tightly coupled to process conditions. Sealant beads must be continuous. Busbars must be torqued precisely. Wiring runs inside sealed enclosures. And every module must be traceable to a serial number. ➡️ Manual inspections vary by shift. ➡️ Fixed AOI systems only see predefined angles. ➡️ Inspection rules don’t change automatically when BOMs change. ➡️ Quality data lives separately across MES, QMS, and warranty systems. ➡️ Adding more inspectors increases variability. ➡️ Adding more cameras increases complexity. That’s the gap we solve at Loopr AI. Loopr AI enables: ✅ Inspection logic triggered dynamically based on BOM and work order ✅ Vision AI agents covering fixed stations and blind spots with mobile devices ✅ Structured capture of manual inspection decisions ✅ Image-backed traceability per module serial number ✅ Unified dashboard tracking scrap, rework, escapes, and supplier trends EV battery production requires continuous quality intelligence, not isolated inspection points. That’s what Loopr is built for.

Our CEO Priyansha Bagaria joined News8 Bharat Breakfast Club to discuss India’s AI moment—and what it will take to build global tech products from here. Three ideas stood out: 1) India must shift from services to IP. Global Capability Centers can’t remain “support centers.” They need to become R&D hubs that own product outcomes and intellectual property. 2) AI needs infrastructure, not just talent. The next decade will be shaped by compute + data centers + full-stack capability (chips, networks, cloud). That’s what makes AI accessible beyond a few top labs. 3) Domain AI will outperform generic AI in the real world. In manufacturing, context is everything—work orders, BOM variance, lighting, tooling, operator decisions, defect classes. You don’t solve that with a generic model. You solve it with industry-specific intelligence. Priyansha’s story is a reminder of what’s possible: a factory-floor quality problem from a small town can become a global product—when you build for reality, not demos. At Loopr AI, we’re building a Quality Intelligence System that helps manufacturers scale inspection reliability and turn inspection activity into trend intelligence and action.

We’ve just released a new whitepaper: “Beyond the Breakdown: Predictive Intelligence in Fleet Operations.” It’s a strategic deep dive into why fleets still get blindsided by failures — even with telematics, DVIRs, and preventive maintenance schedules in place. Across fleets of every size, the pattern is consistent: ✔ Data is collected, but not synthesized ✔ Alerts are triggered, but not prioritized ✔ Maintenance is scheduled by mileage, not wear ✔ Breakdowns are treated as isolated events, not predictable trajectories This paper unpacks: ✔ The hidden vulnerability points inside modern fleet operations ✔ How mechanical decay unfolds weeks before failure ✔ The true economics of reactive vs. predictive maintenance ✔ Why the odometer is no longer a sufficient planning tool ✔ What an intelligence layer above existing systems actually looks like If you’re accountable for uptime, safety, or operating margin — this is for you. Read the full whitepaper here:

AI is reshaping quality inspection in Aerospace manufacturing. As production complexity increases, aerospace manufacturers are facing new challenges: • High-mix assemblies and custom configurations • Manual MRO inspections dependent on inspector experience • Growing AS9100 and traceability requirements • Siloed inspection data across lines and facilities Traditional inspection models weren’t built for this level of variability. Loopr AI helps aerospace manufacturers modernize inspection through a unified Quality Intelligence System. With Loopr, teams can: ✈️ Deploy Vision AI for rotor blade & turbine inspections ✈️ Verify wiring harness assemblies against BOMs ✈️ Flag anomalies in NDT image datasets ✈️ Digitize OCR-based part & compliance documentation ✈️ Integrate inspection data across MES, ERP, PLC & warranty systems The result is consistent, traceable, and scalable inspection across production and MRO workflows. Quality is no longer isolated at individual stations. It becomes a connected intelligence layer across the factory. Learn how Loopr AI supports Aerospace manufacturers: 🌐 loopr.ai

AI inspection is fast in aerospace. What slows teams down is MRB. When AI flags: – a fastener issue – a wiring harness mis-route – a cabin paint defect inspection time drops fast. But then quality still has to answer: • cosmetic vs airworthiness • MRB or in-line rework • part rev applicability • supplier / lot recurrence • audit traceability years later Most vision systems stop at detection. Aerospace quality lives after detection. That’s where Loopr AI is different. Every inspection is captured with: – defect class mapped to NCR/MRB logic – part + BOM + revision context – station, shift, supplier – image + model version used – final outcome: pass, rework, scrap So when QA is asked: “Is this under control?” the answer isn’t a spreadsheet or with a person it’s the inspection record itself. At Loopr AI we are solving exactly for this. If you wanna know how? Visit: https://lnkd.in/g2DzXuHh