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Manufacturing AI

Struggling to plan sales and inventory? Factory facing downtime and operational losses? Customer support too time-consuming and expensive yet not retaining them?

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Trusted by enterprises leading the
future of AI-driven business

A Solution for Every Manufacturing Challenge

Proven value across sales planning, factory operations, and customer experience one unified AI platform.

SIOP Planning Suite

Forecast demand, optimize inventory placement, and price with precision across thousands of SKUs and hundreds of locations.

15–25% higher forecast accuracy
15–20% lower carrying costs
3–6% revenue improvement
Factory Suite

Visualize processes, detect anomalies, predict equipment failures, and optimize setpoints across yield, energy, quality and throughput.

6–8% reduction in downtime
3% yield improvement
10–15% profitability increase
Customer Suite

Automate warranty claims, enable conversational spec comparisons, and deliver AI-powered customer support at scale.

60–70% fewer manual tickets
18% increase in cross-sales
85% customer satisfaction

Manufacturing Solutions by Suite

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.
‍
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

Sales, Inventory & Operations Planning (SIOP) Suite

Predictive Planning

AI-powered planning for demand, inventory, pricing, and operational alignment across complex distribution networks.

Key Features / Outcomes

Improves SKU-level demand forecasting using external variables, constraints, and real-time planning signals.

Optimizes inventory placement across branches, reducing carrying costs and minimizing stockout risk.

Enhances pricing decisions with real-time bid prediction, margin improvement, and faster response times.

Aligns sales, logistics, and supply planning across products, parts, channels, and regional networks.

Enables conversational access to sales trends, promotions, forecasts, and customer performance insights.

Case Study:

A global HVAC leader used the suite to forecast ~5,000 SKUs, optimize inventory across 350 branches, improve forecast accuracy by 15%, and drive 3% higher sales through smarter rebate and distributor planning.

Factory Suite

Intelligent Factory

AI-driven process intelligence for yield improvement, downtime reduction, maintenance, and factory-wide optimization.

Key Features / Outcomes

Unifies sensor, lab, and operational data for real-time process visibility and KPI monitoring.

Detects anomalies early to improve quality, stabilize processes, and reduce unplanned downtime.

Predicts equipment issues with severity, repair guidance, and maintenance prioritization recommendations.

Optimizes yield, throughput, energy, quality, and byproducts through real-time process balancing.

Improves workforce scheduling against production targets, labor constraints, and productivity goals.

Case Study:

A leading Latin American sugar producer used the suite to visualize operations in real time, detect anomalies, optimize process setpoints, reduce sugar losses by 6%, and cut downtime by 8% across mill operations.

Customer Suite

Connected Customer

AI-powered service, support, and product guidance to improve customer experience and commercial outcomes.

Key Features / Outcomes

Resolves customer inquiries instantly while escalating complex issues with auto-generated engineering tickets.

Enables conversational specification lookup and comparisons to simplify product discovery and selection.

Automates warranty claim classification with rapid fault attribution and high decision accuracy.

Recommends repairs using issue history, parts availability, and proven resolution pathways.

Improves retention, conversion, and support efficiency through faster, more intelligent customer interactions.

Case Study:

An American electronics manufacturer used the suite to enable specification lookup and product comparison, reduce customer tickets by 63%, improve click-through rates by 12%, and increase conversion rates by 4%.

Revolutionizing
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