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Agri AI from Field to Factory

Turning satellite, climate, crop and operational data into decision intelligence for farming and agri-industrial processing.

Stomata Labs

AI-Powered Solutions for Agriculture

Demand
Forecasting

Price
Optimization

Predictive
Maintenance

Retail
Analytics

Inventory
Planning

Realty
Indicators

Problem‍
A premier HVAC solutions provider in North America aimed to refine inventory planning and enhance sales predictions across more than 360 locations. The complexity was heightened by a diverse product range exceeding 6,600 SKUs, where stock-outs were leading to elevated warehousing and holding expenses.
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Solution‍
To address this challenge, a specialized forecasting solution was engineered, leveraging both machine learning and deep learning algorithms. These techniques were combined in an ensemble approach to achieve superior accuracy over traditional time-series forecasting methods, offering a strategic advantage in inventory management and sales forecasting.

95%

Accuracy

6,600

SKUs

360

Locations

‍Problem
‍A top-tier microelectronics component manufacturer aimed to refine SKU-level pricing strategies for trade sales. Their existing price prediction algorithm was plagued by bugs, leading to extended processing times. The goal was to suggest price points that enhance the chances of winning transactions, thereby maximizing revenue without sacrificing profit margins.

‍Solution
‍Findability Sciences deployed its machine learning technology to analyze historical data on revenue, quantities, price points, deal success rates, and forecasted demand. This enabled the recommendation of optimal price points for each SKU.

98%

Accuracy

24 to 1.5 Hrs

Reduced Time

MSE 0.4

.

Problem
‍A Japan-based multinational aimed to reduce employee churn and improve mentorship but struggled to identify root causes of voluntary attrition despite development investments.

‍Solution
‍Findability Sciences structured graphical IoT sensor data into CSV files, built ML models to predict breakdown timestamps, and used time series analysis to identify failure patterns and key influencing factors.
‍

90%

Accuracy

Cost Saving

Maximize

Operational

Efficiency Increased

A Top 5 retailer requires digital analytics for 4000 feature releases annually
‍
‍Their analysts would require 8 weeks to provide analytics on the impact created by a website feature release. These were manually computed, based on just a differential between impressions and conversions. The FS incrementality computation included seasonality, attribution, and statistical significance. Business users could gain instant insights through a conversational interface. Additionally, forecasts were generated with a 22% uplift compared to internal accuracies for all product conversions to detect anomalies after a release.

+22%

Accuracy

>90%

Stat Sig

4 Weeks

Time Saved

Problem
‍Traditional HVAC distributors struggle with fragmented systems, manual reconciliation, and reactive inventory planning. Poor data visibility leads to excess stock, frequent stockouts, and high fulfilment costs. Seasonal fluctuations and supply chain disruptions amplify inefficiencies, draining capital and reducing service levels across regions — making agility nearly impossible

Solution
‍Findability Sciences’ Inventory Agentic Workflow Engine (AWE) integrates real-time data from all enterprise systems to enable proactive, AI-driven inventory management. With Forecasting, Segmentation, and Planning Agents, enterprises gain SKU-level precision, simulate demand scenarios, and dynamically optimize replenishment, routing, and placement — transforming operations from reactive to predictive

$49M - 141M

Savings

+5%

Inventory Turns

20%

Lower Carrying Costs

Forecasting of 8 real estate parameters for 24 months with over 90% accuracy

‍The biggest challenge for a real estate asset management company in the US was forecasting market conditions for investment and divestiture decisions. By forecasting rent, occupancy, and value with over 90% accuracy for 24 months, the company was able to use the forecasts for making financial and investment decisions. Additionally, an economic downturn forecast was conducted to determine the probability of a recession in 6 months, 12 months, and 24 months with 94% accuracy.

>90%

Accuracy

1M+

Forecasts

Recession

Prediction

Stoma Sense‍

Farming challenges:

As crops face changing stress conditions through the season, yield potential declines. At the same time, inefficient input application leads to waste, higher costs, and inconsistent farm performance.

Key Features / Outcomes

Early detection of crop stress, moisture variation, and weed presence

Plot-level potential mapping using historical and index-based performance insights

Continuous visibility through cloud-fill enabled satellite reconstruction

Cane moisture prediction for better irrigation, ripener, and harvest decisions

TCH and TSH forecasting linked to field actions across the season

Better alignment between farm performance, harvest planning, and mill readiness

Lower wastage across water, fertilizers, and pesticides

Higher productivity, stronger yield predictability, and improved sugar outcomes

Central America Case Study: Pantaleon Sugar Holdings

Across Pantaleon’s 180,000 hectares in Central America, Stoma Sense brings AI-driven visibility to crop conditions across thousands of plots. By detecting stress earlier and improving irrigation and input decisions, it has potential to bring about improvements in yield, reduction in irrigation and chemical costs and improvements in Pol.‍

1-3% Recovery in Yield

Through earlier detection of crop stress and better field interventions

Up to +0.3 Improvement in Pol

Through better timing of irrigation and ripening decisions

5–10% Reduction in Irrigation Costs

Through more targeted water application

10–20% Reduction in Chemical Costs

Through more precise application of inputs

Across large farming areas, these improvements can translate into significant gains in productivity and operational efficiency.

Stoma Insight‍

Milling challenges:

Mill inefficiencies rarely come from one machine alone. Hidden drift across interconnected systems can reduce recovery, increase downtime, and weaken plant performance.

Key Features / Outcomes

Real-time visibility across plant operations, production performance & energy usage

Early detection of sucrose loss, equipment risk, and process deviation patterns

AI-driven alerts that help operators respond faster to emerging issues

Identification of bottlenecks and loss points across the milling system

Recommendations for process adjustments that improve control and performance

Explainable insights into variables influencing recovery, yield, and efficiency

Reduced downtime through earlier intervention and smarter operational response

Higher recovery, stronger throughput, and more efficient mill performance

India Case Study: Sugar Mills in Baramati

Findability Sciences deployed Stoma Insight in Baramati sugar mills, enabling real-time monitoring, early anomaly detection, reduced sucrose losses, improved energy efficiency, and stronger coordination across complex milling operations.

Book a live demonstration to see how leading factories have adopted digital twins, anomaly detection, and optimization

Smarter Dairy Operations‍

LactaAI™ - Industrial Intelligence for Dairy & Whey Production

Dairy production is shaped by thousands of signals across yield, quality, energy, and throughput. LactaAI™ transforms this complexity into real-time intelligence, helping dairy processors move from reactive operations to predictive, optimized performance across milk, whey, evaporation, drying, and packaging.

Key Features / Outcomes

Applied AI for dairy processors seeking higher efficiency, stronger yield, and plant-wide visibility.

Process Intelligence

Tracks milk, whey, drying, packaging, and utilities in real time

Reveals drivers of yield, stability, and efficiency

Supports better decisions across production stages

AI-Powered Anomaly Detection

Detects drift, instability, and inefficiencies early

Alerts teams before issues impact output or quality

Enables faster diagnosis and corrective action

Operational Optimization

Recommends actions to improve recovery and throughput

Helps reduce energy use and product loss

Supports more stable, efficient operations

Unified Plant Visibility

Integrates data from plant and business systems

Creates one intelligence layer across operations

Enables holistic analysis and smarter decisions

‍LactaAI is designed for rapid deployment without disrupting existing plant infrastructure.

Others

Key Issues:

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Schedule a walkthrough of our detailed case studies

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Built With Farmers.
Trusted By Enterprises.

Agri AI is shaped by people who understand agriculture at ground level where decisions affect livelihoods, not just margins. At the same time, it is engineered for enterprise environments, integrating seamlessly with industrial systems, data platforms, and governance frameworks.