Findability Sciences’ LactaAI targets dairy processing sector

AI solutions provider Findability Sciences aims to empower enterprises with next-generation AI-driven transformation with solutions that leverage both Discriminative and Generative AI within a governance framework. With a presence across the United States, India, the Middle East, and Japan, the company serves over 50 global clients, including those in the dairy and agriculture segments, helping them unlock the power of data-driven intelligence 

Talking to IndiFoodBev, Anand Mahurkar, founder and CEO of Findability Sciences, details how LactaAI bridges the critical data-to-decision gap, turning raw operational signals into massive yield improvements and millions in energy savings without a rip-and-replace overhaul.

According to Mahurkar, modern dairy processing plants are incredibly loud, complex, and fast-moving environments. A single facility generates millions of distinct digital data signals. These data points stream constantly from raw milk reception bays, heavy-duty evaporation and drying towers, testing laboratories, packaging lines, and underlying utility systems. Yet, despite this massive influx of information, a systemic vulnerability plagues the industry — almost none of this data reaches a human decision-maker in time to actually impact the bottom line.

Enterprise AI firm Findability Sciences has identified this phenomenon as the data-to-decision gap, a costly operational blind spot where manufacturing data exists purely as a historical record rather than a real-time asset. “In the highly margin-sensitive dairy sector, running a plant based on end-of-shift reports and reactive monitoring means that minor process drifts compound into massive financial losses before anyone realizes a problem occurred,” Mahurkar says.

To bridge this divide, the Findability Sciences has launched LactaAI, an advanced artificial intelligence platform engineered specifically for the unique demands of dairy processing. Mahurkar notes that the financial stakes of closing this gap are exceptionally high, as a minor one percentage-point improvement in yield can translate directly into crores of rupees in annual savings for a large-scale operation.

Moving beyond post-batch autopsies

Conventional industrial monitoring and automation systems excel at telling engineers exactly what happened after a batch is already completed. LactaAI is built to fundamentally shift plant operations away from these post-batch autopsies and toward live, active decision support. The platform is designed to answer three critical operational questions in real time: what is currently happening across the facility, why it is happening, and precisely what adjustments need to be made right now, he explains.

By delivering decision-ready intelligence during the production cycle itself, the system allows plant teams to isolate yield losses, process drift, and quality variations before they can damage a company’s profit margins. He said, “The platform targets yield improvements ranging from 0.5 to 1.5 percentage points. In the context of large-scale Indian dairy manufacturing, this incremental optimization yields massive financial returns, driving between Rs 2.35 crore and Rs 28.2 crore in annual value per plant.”

Real-time insights meet conversational AI

“Rather than forcing manufacturers to undergo an expensive, high-risk ‘rip-and-replace’ overhaul of their legacy hardware, the platform integrates smoothly with existing plant infrastructure. It functions as a unified intelligence layer that pulls data directly from disparate enterprise resource planning (ERP) systems, manufacturing execution systems (MES), laboratory information management systems (LIMS), and business intelligence (BI) networks,” he said, adding that the platform achieves this depth through a dual-product architecture built on a single framework.

• Lacta Insight: This operational layer delivers real-time process intelligence across milk reception, evaporation, drying, packaging, and utilities. It features purpose-built software variants designed to handle the distinct process logic of fluid milk, cheese, cultured dairy, and advanced whey processing.
• Lacta BPC: This interface serves as a conversational AI layer capable of reading across all connected plant systems. Instead of forcing a plant head, quality manager, or CFO to comb through complex dashboards or rely on a specialized data team, Lacta BPC answers highly specific operational questions in plain language. A manager can directly ask why a specific line suffered a yield drop on a given morning and receive a sourced, reasoned explanation rather than a pointer to another chart.

Aligning Profitability with Sustainability Goals

In modern manufacturing, environmental sustainability and corporate profitability are increasingly becoming the same conversation. Every instance of avoidable yield loss, every poorly calibrated evaporation run, and every unnecessary product rework cycle carries a heavy financial penalty alongside a measurable environmental cost. By directly connecting real-time process conditions to these broader outcomes, the technology helps plants catch inefficiencies before they compound into major waste.

“On the energy front,” Mahurkar adds, “the platform specifically targets a 5 to 10% reduction in power and fuel consumption during the intensive evaporation and drying phases. Furthermore, as large institutional buyers and strict international export markets demand verifiable sustainability metrics, the platform naturally builds the rigorous data foundation required to prove environmental compliance as a standard output of daily operational optimization.”

India as an AI proving ground

According to him, the Indian dairy market represents the central pillar of Findability Sciences’ long-term innovation and growth strategy. “Because the domestic sector is massive, highly variable, and exceptionally sensitive to margins, it serves as the ultimate proving ground for industrial software. An AI platform capable of successfully navigating raw material variability, legacy infrastructure constraints, multilingual workforces, and intense cost sensitivities in India is uniquely equipped to perform anywhere else in the world.”

Early adoption has shifted rapidly from general curiosity to disciplined, return-on-investment (ROI) driven deployment. To accelerate this transition, the company introduced a rapid Discovery and Readiness Assessment that diagnoses exactly where value is leaking in a plant and whether its current systems can support AI within minutes rather than months. Initial market traction has concentrated heavily on immediate yield optimization, energy management, batch performance analysis, and automated anomaly detection.

Vision for the value chain

While the processing plant floor is the ideal starting point for AI deployment due to its measurable financial impact and fast time-to-value, Findability Sciences’ long-term roadmap expands well beyond the factory walls. The company’s goal is to create a fully connected intelligence layer that spans the entire dairy value chain, linking procurement, supply chain logistics, and quality assurance.

During the upstream procurement phase, predictive analytics can identify raw milk quality patterns and anomalies before substandard supply enters the processing system. Downstream, more precise demand signals can improve supply chain planning to avoid overproduction and spoilage. In quality assurance, connecting raw material characteristics directly to finished product results creates a comprehensive traceability layer that global regulators are starting to require.

Beyond dairy, the core architecture is designed for easy adaptation across adjacent sectors such as beverages, edible oils, bakery products, nutraceuticals, and packaged foods. However, the company is maintaining a strict focus on solving the complex nuances of dairy deeply first, ensuring that domain-specific intelligence takes precedence over broad, generic applications.

“Dairy plants don’t need more dashboards. They need decision-ready intelligence. LactaAI is built for that shift — from monitoring to optimization, from reports to decisions, and from operational complexity to measurable value.”