Node AI.09

Machine learning and predictive analytics

Forecasts and predictions you can act on, with the uncertainty shown.

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Machine learning for forecasting, classification, recommendations and anomaly detection, built on your data and deployed into the tools your team uses.

The problem

Demand planning in spreadsheets, churn noticed only after customers leave, fraud found in the monthly audit. The data to see these coming usually exists; it is not being used.

Models that live in a notebook do not change decisions. Predictions need to arrive where decisions are made, with an honest indication of how sure they are.

What we build

Demand, sales, enrolment and cash-flow forecasting with confidence ranges
Classification and scoring: churn risk, lead scoring, credit risk, ticket routing
Recommendation systems for products, content and next best action
Anomaly detection for transactions, sensor data, operations and data quality
What-if scenario modelling for pricing, staffing and demand decisions
Model monitoring for drift, with retraining pipelines

How it works

Historical data is cleaned into features, models are trained and backtested, and predictions are served to dashboards, apps or APIs, with monitoring for drift.

Step 1Frame the decisionAgree what decision the prediction supports, how it will be used and how success is measured.
Step 2Prepare featuresBuild reliable features from your history, with care for leakage and gaps.
Step 3Train and backtestCompare simple baselines with more complex models on held-out periods.
Step 4DeployServe predictions to the dashboard, CRM or app where the decision is made.
Step 5Monitor and retrainTrack accuracy and drift; retrain on a schedule or when performance drops.

Deliverables

Model with documented features, backtest results and limitations
Prediction pipeline deployed to your environment
Integration with the dashboard, app or system that uses it
Monitoring and retraining setup

Typical stack

Python
SQL
Snowflake
Google Cloud

We choose tools to fit your environment; this is where we usually start.

Ways to work with us

Build and HandoverA defined system, delivered, documented and handed to your team.Engagement Forward Deployed EngineersOur engineers inside your team until it runs in production.Engagement Managed AI OpsOngoing monitoring, evaluation and improvement of your AI systems.Engagement

Questions

How much data do we need?

It depends on the problem. Forecasting usually needs at least two full seasonal cycles; classification needs enough examples of each outcome. We assess this in the first week and say plainly if the data will not support a reliable model.

Why not just use an LLM?

For numeric forecasting and scoring on tabular data, classical machine learning is usually more accurate, cheaper and easier to explain. We use LLMs where the input is text.

Can people see why a prediction was made?

Yes. We favour models whose drivers can be shown, and add explanations of the main factors behind each score.

Adjacent nodes

Part of AI & Agents.

Data engineering and modern data platformsPipelines and a warehouse your team stops arguing about.DATA.01 Executive and analytics dashboardsDashboards people open every morning, because the numbers are right.DATA.03 AI-ready data foundationsClean, modelled, governed data, so AI systems give answers you can use.DATA.02

Talk to us about machine learning and predictive analytics for your business.

Send a short brief. We reply by email, usually with questions before we propose anything.