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NeuralFactoryAI
Industrial AI for Autonomous Manufacturing
15 followers
Industrial AI for Autonomous Manufacturing
15 followers
NeuralFactoryAI is an Industrial AI company that develops autonomous optimization solutions for manufacturing & process industries using patented Deep Learning Neural Network models that continuously learn from real-time process sensors and quality lab data to reduce yield loss, product giveaway, raw material consumption, dosing errors, and eventually manpower. All these optimizations happen in closed-loop process without any manual intervention.
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The closed-loop angle is genuinely interesting, especially for plants that lose hours to manual tuning between batches. One thing that would make adoption easier for skeptical ops teams would be a side-by-side sandbox mode where the model runs in parallel with current controls for a defined period, showing projected vs actual savings before it gets the keys. That kind of evidence on their own process tends to win over floor managers faster than any pitch deck.
@trkan7zmg Thanks, Türkan! That's a great point. Building operator confidence before enabling autonomous control is incredibly important. Running the AI alongside existing controls to compare projected and actual outcomes before moving to closed-loop operation is very much aligned with how we think about practical industrial AI adoption. Thanks for the thoughtful suggestion!
Love the closed-loop approach here, that is a real edge over dashboards that just spit out charts. One thing that would help my team is a simple "what if" simulator where I can tweak a sensor reading or feed spec and see the projected yield and raw material impact before rolling anything out live, it would make onboarding new plant managers so much faster.
@atakankubur2pd Thanks, Atakan! Really appreciate the thoughtful feedback. A "what-if" simulator is definitely aligned with our vision. Since our models continuously learn from real production data, enabling engineers to evaluate potential process changes in a safe simulation environment before deployment would be a valuable extension. Thanks for sharing this idea!
Congrats on the launch. One thing I'd love to see is a sandbox mode where we can replay historical sensor and lab data through your models to estimate the savings we could have captured, that would make it way easier to justify the rollout to plant managers before committing to a live deployment.
@muammer7fwp Thanks, Muammer! Really appreciate the suggestion. Using historical process and quality data to evaluate potential optimization opportunities before a live deployment is a compelling use case. A replay or backtesting mode could help manufacturers better understand the potential impact and build confidence before moving to production. Thanks for sharing your thoughts!
Love how the closed-loop approach removes the manual tuning bottleneck that usually kills these projects in production. The sensor-to-lab data fusion sounds like the kind of unglamorous engineering work that actually moves the needle for manufacturers.
@emredkwk Thanks, Emre! Really appreciate that perspective. We agree that practical deployment is what matters most in industrial AI. Combining real-time process sensors with quality lab data enables our models to continuously learn from actual plant conditions, helping deliver optimization that integrates with existing automation rather than simply adding another dashboard. Thanks for the thoughtful feedback!