Turning satellite, climate, crop and operational data into decision intelligence for farming and agri-industrial processing.
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.
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.
Accuracy
SKUs
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.
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Accuracy
Reduced Time
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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.
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Accuracy
Maximize
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.
Accuracy
Stat Sig
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
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Savings
Inventory Turns
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.
Accuracy
Forecasts
Prediction
Stoma Sense
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.
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
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.
Through earlier detection of crop stress and better field interventions
Through better timing of irrigation and ripening decisions
Through more targeted water application
Through more precise application of inputs
Across large farming areas, these improvements can translate into significant gains in productivity and operational efficiency.
Stoma Insight
Mill inefficiencies rarely come from one machine alone. Hidden drift across interconnected systems can reduce recovery, increase downtime, and weaken plant performance.
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
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
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.
Applied AI for dairy processors seeking higher efficiency, stronger yield, and plant-wide visibility.
Tracks milk, whey, drying, packaging, and utilities in real time
Reveals drivers of yield, stability, and efficiency
Supports better decisions across production stages
Detects drift, instability, and inefficiencies early
Alerts teams before issues impact output or quality
Enables faster diagnosis and corrective action
Recommends actions to improve recovery and throughput
Helps reduce energy use and product loss
Supports more stable, efficient operations
Integrates data from plant and business systems
Creates one intelligence layer across operations
Enables holistic analysis and smarter decisions
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.
Clients Worldwide
From field intelligence to factory optimization, our AI solutions help teams detect what matters, act earlier, & improve every stage of the agricultural value chain.
+1–3%
Yield Recovery
Physiology-driven irrigation decisions recover TCH lost to water stress and mis-timed cycles
5–10%
Irrigation Cost Reduction
Physiology-driven irrigation decisions recover TCH lost to water stress and mis-timed cycles
+0.3
Pol % Improvement
Aligning harvest to peak physiological ripeness. Value: $400k–$650k per million tons cane
2–4%
Yield Protection
Early weed and pest spatial anomaly detection prevents spread before visible damage occurs
10–20%
Chemical Cost Reduced
Targeted intervention vs. blanket application. Typical saving: $70–$100 per hectare
In Days
Earlier Stress Detection
Water and nutrient stress identified days to weeks before it is visible when intervention still counts
+0.5–1.2%
Sugar Recovery
AI-driven setpoint optimization across milling, juice treatment, and pan house. Validated across live deployments.
5–15%
Reduce Steam Consumption
Physiology-driven irrigation decisions recover TCH lost to water stress and mis-timed cycles.
+5–12%
Power Export Uplift
Cogeneration optimization improves bagasse-to-energy conversion and grid export yield.
20–40%
Less Unplanned Downtime
Early anomaly detection eliminates surprise failures. Every recovered hour goes directly to crushed cane.
1–3%
Throughput Gain
Bottleneck identification and crushing rate optimization. Typical value: $300k–$1.0M per season.
1–2%
Uptime Improvement
Reduced corrective maintenance and fewer emergency stops. Value: $400k–$2.0M per season.
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