Forecasting& Predictions

Spreadsheet forecasts go stale the moment you save them. Capacity planning is a guess. Cash flow surprises surface weeks after anyone could have acted.

We build custom forecasting and machine learning models for revenue, capacity, margin, and demand, deployed in production, integrated into your dashboards, updated daily.

25-40%
improvement in forecast accuracy vs spreadsheet baseline

Early warning on margin and capacity risk, weeks before it hits the P&L. Built on your data, tuned to your business.

The Challenge

Why forecasting fails most businesses

01

Forecasts built in spreadsheets

The moment a spreadsheet forecast is saved, it starts going stale. Every update requires manual effort and the model breaks when someone changes a formula.

02

Capacity planning is a guess

Teams get over-allocated and under-utilized because nobody has a forward view of demand until it is already too late to respond.

03

Cash flow surprises hit the P&L

Working capital crunches and margin compression surface weeks before anyone in leadership sees them coming.

04

Pipeline-to-revenue is opaque

Sales pipeline exists in the CRM but converting it to a revenue forecast requires assumptions nobody has made explicit or validated.

Capabilities

What we build

01

Revenue & Pipeline Forecasting

Stage-weighted pipeline models that connect CRM data to revenue projections. Updated daily, not monthly.

02

Capacity & Workforce Forecasting

Staffing need projections 30 to 90 days out, built on signed contracts, pipeline probability, and historical utilization patterns.

03

Demand Forecasting

SKU and location-level demand models for supply chain and manufacturing. Reduces stockouts and overstock simultaneously.

04

Margin & Project Profitability Modeling

Forecast-at-completion models that flag margin risk on active projects before it hits the P&L.

05

Cash Flow & Working Capital Models

13-week rolling cash forecasts that pull from AR, AP, payroll, and project billing schedules automatically.

06

Risk & Anomaly Detection

Statistical models that flag outliers, cost overruns, and schedule slips as soon as they appear in the data.

07

Optimization Models

Scheduling, routing, pricing, and resource allocation models that improve operational decisions with real constraints.

08

Predictive Maintenance & Asset Utilization

Equipment failure prediction models built on historian and sensor data. Know before something breaks.

Our Process

How we deliver

01

Assess

We audit your data landscape, systems, and gaps to build a clear picture of where you are.

02

Design

Architecture, data model, and delivery plan scoped to your goals and timeline.

03

Build

Pipelines, dashboards, models, or applications built and tested in your environment.

04

Validate

Every output is verified against your business rules before it reaches a decision maker.

05

Operate

Monitoring, alerting, documentation, and ongoing support so nothing breaks silently.

Results

Outcomes you can expect

Daily
Forecast refresh cadence
Not monthly. Not weekly.
25-40%
Improvement in forecast accuracy vs spreadsheet baseline
Weeks
Early warning on margin and capacity risk
Before P&L impact
Optimized
Scheduling, pricing, and allocation decisions
Use Cases

Where this applies

01

Engineering firm: staffing 90 days out

A 150-person engineering firm had no visibility into staffing needs beyond 30 days. Hiring was reactive and utilization swings were costly.

Outcome

We built a capacity forecasting model that pulls from Deltek Vantagepoint, CRM pipeline, and HR data. Leadership now sees staffing needs 90 days out and hires proactively.

02

Manufacturer: equipment failure 14 days early

A food manufacturer experienced frequent unplanned downtime that cost an average of $40K per incident. Maintenance was calendar-based, not condition-based.

Outcome

We built a predictive maintenance model on vibration, temperature, and cycle count data. The plant now receives alerts 14 days before likely failure with 87% accuracy.

03

Insurance carrier: loss ratio by underwriting segment

An insurance carrier could only review loss ratios at quarter-end. By the time a problem segment was identified, significant losses had already occurred.

Outcome

We built a rolling loss ratio model segmented by product, geography, and underwriting cohort. Problematic segments now surface within 30 days of emerging.

Integrations

Some of the tools and frameworks we deploy

These are some of the platforms we connect. We integrate with almost any system. If you run it, we can likely work with it.

Python
scikit-learn
XGBoost
Prophet
TensorFlow
PyTorch
Databricks ML
Snowflake Cortex
Azure ML
Vertex AI
MLflow
Airflow
Python
scikit-learn
XGBoost
Prophet
TensorFlow
PyTorch
Databricks ML
Snowflake Cortex
Azure ML
Vertex AI
MLflow
Airflow
FAQ

Frequently asked questions

Get Started

Know what is coming before it hits.

Book a free call and we will assess whether your data is ready for forecasting models today.