SmartPath AI
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SmartPath AI
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Stop Reacting. Start Predicting.
We build predictive models that turn your historical data into forward-looking intelligence — forecasting demand, identifying churn risk, detecting anomalies, and surfacing the signals your team needs to act before problems become costly.
91%
Forecast Accuracy
High
Model Confidence
12 mo
Data Points
89%
Average forecast accuracy within 10% margin
3.4×
Improvement in inventory planning efficiency
34%
Average churn reduction in first 12 months
14d
Days to first live predictive model in production
Select a model below and run the simulation — see exactly how we ingest your data, engineer features, run inference, and surface actionable predictions step by step.
TRIGGER: Weekly model run triggered — Monday 02:00 UTC
Data pipeline executed
Feature engineering
Model inference run
Confidence intervals built
Recommendations published
Each model is trained on your proprietary data, validated against held-out historical outcomes, and deployed with explainability outputs your team can act on.
Predict future demand, revenue, and volume with models trained on your historical data — giving planning, operations, and finance teams a quantified view of what's coming, not just what happened.
Identify customers or employees at high risk of churn before they leave — with enough lead time for your team to intervene, and the specific signals that triggered each high-risk flag.
Monitor operational metrics in real time and surface statistically significant deviations before they become incidents — with root cause signals to guide rapid response.
Model optimal inventory levels, reorder points, and supplier lead times based on predicted demand — reducing both stockouts and excess inventory simultaneously.
Score every customer by predicted lifetime value — enabling your marketing and sales teams to allocate acquisition spend, retention investment, and account management resources to the highest-value opportunities.
When off-the-shelf analytics don't fit your business problem, we develop bespoke predictive models trained on your proprietary data — for any outcome your business needs to forecast.
Three predictive analytics deployments — the business problem, the model we built, and what it achieved.
The Challenge
A multi-brand retailer with 12,000 active SKUs was running inventory planning on 13-week rolling averages and buyer intuition. Stockout rate was 8.3% across key lines during peak periods, while overstock was costing $2.8M annually in markdown and holding costs — both problems occurring simultaneously across different categories.
What We Built
We built a SKU-level demand forecasting system that models historical sales, promotional calendars, seasonality, and external signals. The system generates weekly replenishment recommendations per SKU per location, with confidence intervals that adjust automatically for forecast uncertainty — surfaced in a buyer dashboard integrated with the existing ERP.
8.3%→1.4%
Stockout rate during peak periods
$2.1M
Annual overstock cost reduction
91%
Forecast accuracy within 10% margin
4.2×
Faster replenishment decision cycle
"We stopped arguing about gut feel in the buying meeting and started reviewing model recommendations. Our inventory position has never been cleaner."
— Chief Merchandising Officer
The Challenge
A B2B SaaS company with 3,400 accounts was experiencing 22% annual churn. Customer success managers had no systematic way to identify at-risk accounts before the renewal conversation — by the time churn intent was visible, it was usually too late to address the underlying causes.
What We Built
We built a customer health scoring model that ingests product usage data, support ticket history, engagement signals, and contract data to generate a real-time churn probability score for every account. Accounts above risk threshold trigger automated CS manager alerts with the top contributing risk factors — giving the team 60–90 days of intervention lead time.
22%→13%
Annual churn rate in 12 months
60–90d
Intervention lead time per at-risk account
84%
Model accuracy on held-out test set
$3.4M
ARR retained in first year post-deployment
"We went from finding out about churn risk at the renewal call to knowing 90 days in advance. That lead time is everything in customer success."
— VP of Customer Success
The Challenge
A national third-party logistics provider operating 14 distribution centres was monitoring operations through daily summary reports — meaning most operational anomalies weren't detected until the following morning, after significant impact had already accumulated.
What We Built
We deployed a real-time anomaly detection system across all 14 sites that monitors 40+ operational metrics continuously — detecting statistically significant deviations from expected patterns as they occur, classifying anomaly type and severity, and alerting the operations duty manager within minutes of detection.
4hr→8min
Time from anomaly onset to alert
76%
Reduction in incidents reaching full impact
14
Sites monitored from single dashboard
Zero
False positive alerts in first 60 days post-tuning
"We used to find out about problems in yesterday's report. Now we know within minutes and can act before the impact compounds."
— Head of Operations Technology
Dashboards show you what happened. Predictive analytics show you what's about to happen — with enough lead time to act.
Historical reporting tells you what happened, not what's coming
Predictive models give operations and leadership a quantified view of future outcomes with confidence intervals
At-risk customers are only visible at renewal — when it's too late
Churn models surface risk signals 60–90 days before they become visible, when intervention is still possible
Anomalies are detected in yesterday's summary report
Real-time anomaly detection alerts your team within minutes — before impact compounds across the operation
Forecasts are built on gut feel and rolling averages
ML models trained on your historical data produce quantified forecasts with explicit accuracy metrics
From data assessment to live predictions — a 4-phase process that validates accuracy before any model touches production decisions.
We work with your team to define the specific outcome to predict, assess the historical data available, and validate that the signal required for accurate prediction exists in your data.
We engineer predictive features from raw data, train and evaluate multiple model architectures, and validate accuracy against held-out historical data before any live deployment.
Predictions are surfaced where your team works — CRM, ERP, dashboards, or direct system integrations — with explainability outputs so users understand what's driving each prediction.
We monitor model accuracy in production, recalibrate on new data as your business evolves, and expand the predictive layer to additional outcomes as confidence is established.
Tell us the outcome your business most needs to predict. We'll assess your data and show you what a predictive model can achieve within 14 days.