DEMAND FORECASTING MODEL — ML SPRINT

Jake McMahon
Jake McMahon — ProductQuant
B2B SaaS · Product · Growth
A deployed forecasting model integrated into your planning workflow — giving your team a defensible number to plan around instead of a spreadsheet guess.

Demand Forecasting Model

Custom ML model that predicts future demand from your historical data — inventory, capacity, revenue, or resource planning.

Book a Scoped Call → See Deliverables ↓

Free diagnostic · Blueprint sprint with money-back guarantee · Full handoff

WHAT YOU HAVE AT THE END

Forecasting Model Trained on your historical data, deployed in your environment
Planning Dashboard Forecast vs. actual, confidence intervals, trend drivers
Integration Package API, export pipeline, or direct BI tool connection
Model Documentation How it works, what drives it, how to maintain it
Retraining Pipeline Model updates automatically as new data arrives

Fixed price · Everything stays with you

Demand Forecasting Model — in plain terms.

Here’s what this looks like in practice:

INVENTORY PLANNING

You know how much stock to order before you run out

Your model ingests historical sales, seasonality, and external signals. It outputs a demand forecast by SKU and location — with confidence intervals your procurement team can plan around.

CAPACITY PLANNING

You staff for actual demand — not last year's pattern

Operations teams use the forecast to schedule staff, reserve infrastructure, or allocate production capacity. The model accounts for seasonality, campaigns, and external events.

REVENUE FORECASTING

Finance presents a number they can defend

The model generates a bottom-up revenue forecast by product line or segment — with attribution to leading indicators, so finance can explain it to the board.

RESOURCE ALLOCATION

Budgets follow predicted demand — not gut instinct

Teams that allocate marketing spend, engineering capacity, or support resources use the forecast as the input — replacing spreadsheet assumptions with a model that updates automatically.

ENTRY POINT
Free Diag.

Every engagement starts with a free 45-minute diagnostic. We map your situation and tell you whether this sprint is the right fit before you spend a dollar.

GUARANTEE
Money-Back

Blueprint sprint has a money-back guarantee. If the agreed deliverable isn’t met, you pay nothing. No conditions, no argument.

OWNERSHIP
Full Handoff

Everything built during the engagement — code, models, documentation — is yours. No lock-in, no ongoing dependency.

THE PROBLEM

Spreadsheet forecasts fail at scale

“"Our Excel model is 4,000 rows. Every month someone breaks a formula. We spend three days fixing it before the board meeting."”

OPERATIONS DIRECTOR

Overstock and stockout cycles

“"We either have too much or too little. The inaccuracy has a real cost and we can't explain it in the post-mortem."”

SUPPLY CHAIN MANAGER

Revenue surprises

“"We missed by 18% last quarter. Finance is furious and we can't explain what drove the miss."”

VP FINANCE

Forecast without attribution

“"We have a number but nobody trusts it because nobody can explain where it came from."”

CEO

WHAT THE BLUEPRINT SPRINT UNCOVERS

The gap between where you are and where you need to be.

Historical data quality determines model quality

Before building, we audit what data you have, identify gaps, and define the minimum viable history needed for a reliable model.

Confidence intervals matter more than point estimates

A forecast without uncertainty bounds is a guess. The model outputs a range — your team makes decisions with a known margin of error.

External signals improve accuracy

Seasonality, campaigns, and market events are often stronger predictors than internal history alone. The sprint maps which signals improve your specific forecast.

Models degrade without retraining

A model trained once and left alone drifts from reality. The retraining pipeline ensures the model stays accurate as your business changes.

WHY THIS IS DIFFERENT

A forecasting model without a confidence interval is a spreadsheet with extra steps.

Most demand forecasts fail not because the model is wrong, but because teams can't communicate its uncertainty. A point estimate without confidence bounds gets treated as a commitment — and when it misses, nobody knows why.

Every model we build includes explicit confidence intervals, attribution to key drivers, and a live forecast vs. actual dashboard. Your team can see why the forecast moved, present it to stakeholders with defensible numbers, and identify when the model needs attention.

THE METHODOLOGY

The AI Build System

Four phases. Every AI engagement, every time.

PHASE 1

Ingest

Map and clean your data sources. Define accuracy targets and query patterns before writing a line of code.

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PHASE 2

Build

Train, fine-tune, and test the model on your corpus. Iterate until the target accuracy is hit.

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PHASE 3

Deploy

Ship to your environment — cloud or on-prem. Integrate with your product or internal tools.

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PHASE 4

Monitor

Live dashboard tracks performance from day one. You see what's working and what needs attention.

After handoff: your team updates data, the system retrains — no ongoing dependency on ProductQuant.

WHAT YOU GET

Everything your team needs to launch and maintain the system.

FULL ENGAGEMENT
Complete Demand Forecasting Model

A deployed forecasting model integrated into your planning workflow — giving your team a defensible number to plan around instead of a spreadsheet guess.

  • Production forecasting model deployed in your environment
  • Planning dashboard with forecast, confidence intervals, and driver attribution
  • API or BI integration connecting model output to your planning tools
  • Automated retraining pipeline triggered by new data
  • Model documentation: assumptions, drivers, and maintenance guide
  • Full source code and IP handoff

FIT CHECK

Is this the right sprint for your team?

GOOD FIT
Operations, supply chain, finance, or product leaders at com

The situation

Operations, supply chain, finance, or product leaders at companies where inaccurate forecasts cost real money

What changes

  • Production forecasting model deployed in your environment
  • Planning dashboard with forecast, confidence intervals, and driver attribution
  • API or BI integration connecting model output to your planning tools

You have a deployed forecasting model integrated into your planning workflow — giving your team a defensible number to plan aroun.

Jake McMahon

Jake McMahon — ProductQuant

Jake McMahon
B2B SaaS · Product & Growth · Behavioural Psychology & Big Data (Master’s)

I work with B2B SaaS product and operations teams to build and deploy the systems they need — without consuming their engineering capacity or waiting 18 months for the roadmap.

Every engagement starts with a free diagnostic and a scoped blueprint sprint with a money-back guarantee. If the sprint doesn’t hit the agreed target, it costs you nothing.

What does my team need to provide?
Data access, a point of contact, and a clear picture of the outcome you need. No engineering time required during the build phase.

Teams Jake has worked with

FormDR
Virtuagym
HackingHR
Scale Insights
Gainify

PRICING

Start with a free diagnostic. Commit when you’re confident.

STEP 1

Free Diagnostic

Free

45-minute scoped call

  • We map your current situation and data
  • We tell you whether this sprint fits your problem
  • You get a written summary of what we’d build
  • No commitment required to book
Book Diagnostic →
START HERE

STEP 2

Forecasting Blueprint Sprint

$2,500–$3,500

Fixed scope · 2 weeks · Money-back guarantee

  • Audit of your historical data: quality, coverage, and usable signals
  • Feature engineering report: which variables drive your specific demand patterns
  • Baseline model trained on your data with confidence intervals
  • Forecast vs. actual dashboard to evaluate model performance
  • Technical architecture for the full production model
  • Full handoff: code, documentation, and retraining guide
Start Sprint →

STEP 3 (OPTIONAL)

Full Engagement

$15K–$40K

Scope-dependent · Full production build

  • Production forecasting model deployed in your environment
  • Planning dashboard with forecast, confidence intervals, and driver attribution
  • API or BI integration connecting model output to your planning tools
  • Automated retraining pipeline triggered by new data
  • Model documentation: assumptions, drivers, and maintenance guide
  • Full source code and IP handoff
Discuss Scope →

If the blueprint sprint doesn't deliver a baseline model with documented accuracy metrics and a forecast vs. actual dashboard, the sprint is free.

Questions.

Or book a call →
How much historical data do we need? +
Minimum 12 months for seasonal patterns; 24+ months preferred. We audit your data in the Discover phase and tell you upfront if the volume is insufficient for a reliable model.
What if our data has gaps or quality issues? +
The blueprint sprint includes a data audit. We identify gaps and recommend preprocessing steps. Some gaps can be imputed; others require you to collect more data before the full model can be reliable.
What does 'deployed in your environment' mean? +
We deploy to your cloud environment (AWS, GCP, Azure) or on-premise infrastructure. The model runs on your infrastructure with no ongoing dependency on us.
How do you handle seasonality and one-off events? +
Seasonality is modelled as a feature. One-off events (campaigns, product launches, external shocks) are incorporated as override inputs your team controls in the planning dashboard.
How is this different from forecasting tools like Anaplan or Relex? +
Those tools require your data to fit their structure. This is a model built specifically on your data, your patterns, and your business drivers — with full ownership of the output and no vendor licensing.

Replace your spreadsheet forecast with a model your team trusts.

Blueprint sprint with money-back guarantee. Planning dashboard included.

Book a Scoped Call → Email Jake →