ML SPRINT — FORECASTING SYSTEM

Jake McMahon — ProductQuant

B2B SaaS · Product · Growth

Stop planning next quarter from a spreadsheet nobody trusts.

We build a production forecasting model from your historical data, connect it to your planning workflow, and show the confidence range behind every number.

Book a Scoped Call → See Deliverables ↓

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

FORECAST OUTPUT

Demand forecast + driver attribution

confidence range

ActualBaselineForecast

Seasonality

82%

Campaigns

64%

Pipeline

51%

External

38%

Output: forecast, range, drivers, planning action

Where spreadsheet forecasts break down.

Most forecast misses come from hidden assumptions, missing demand drivers, and single-point estimates that look more certain than they are.

SEASONALITY

Spikes are treated as patterns, not surprises

The model separates recurring seasonal movement from real demand shifts, so the team stops overcorrecting after every spike or dip.

CAMPAIGNS

Launch impact stops being a manual adjustment

Promotions, lifecycle campaigns, pricing changes, and sales pushes get modeled as demand drivers instead of side notes in a spreadsheet tab.

CONFIDENCE RANGE

Leadership sees risk, not false certainty

Every forecast includes a planning range so ops, finance, and leadership do not treat uncertain estimates as fixed commitments.

ATTRIBUTION

The number comes with an explanation

Driver attribution shows what pushed the forecast up or down, so the team can explain the plan instead of defending a black-box number.

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 — 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

Hacking HR

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.