Demand forecasting
Predict inventory, staff, energy, or call-centre volume hours, days, or months ahead — with confidence intervals you can plan against.
AI LAB · 03
Production ML, not science projects
Custom machine learning that moves the metric you actually care about.
Forecasting, scoring, recommendations, fraud detection, pricing — built for production, not Kaggle. We pick the simplest model that solves the problem, ship it behind an API, monitor it for drift, and retrain it when the data shifts.
What we build
Models that move metrics.
Six common ML problems we solve every quarter — each one paying back the build cost in months, not years.
Lead & customer scoring
Score leads, predict churn, identify upsell candidates, and segment by lifetime value. Sales and CS teams stop guessing.
Anomaly & fraud detection
Catch fraud, payment risk, system anomalies, and unusual user behaviour in real time — with explainable alerts.
Recommendation engines
Product, content, and next-best-action recommendations using collaborative filtering, content models, and modern transformer recsys.
Pricing & revenue models
Dynamic pricing, demand elasticity, willingness-to-pay, and discount optimisation models grounded in your transaction data.
Customer segmentation
Behavioural clustering and propensity models that drive targeted marketing, product, and retention strategies.
Use cases
Three deployments worth talking about.
Where the math worked out — and the operators got their evenings back.
Demand forecasting at SKU level
Forecast 14,000 SKUs across 80 stores at daily granularity. Reduced stockouts and dead inventory simultaneously by feeding orders straight into the replenishment system.
−32%stockouts
−18%holding cost
Churn prediction & save
Gradient-boosted model that scores active subscribers daily for churn risk. CS team gets a queue of high-risk accounts and a recommended action each morning.
12%save rate
$2.4Mannual ARR saved
Predictive maintenance
Sensor-data model on a fleet of CNC machines. Predicts component failure 5–10 days ahead, replacing weekly preventative maintenance with conditional servicing.
−47%downtime
+22%asset lifespan
The stack we use
Tools, not religion.
Most of our projects pick from this stack. The right answer for your problem might be different, and we'll say so.
Frameworks
- PyTorch
- TensorFlow / Keras
- scikit-learn
- XGBoost / LightGBM
Specialised models
- Prophet, NeuralProphet
- Hugging Face Transformers
- DeepAR, NBEATS
- CatBoost
Data & features
- Pandas, Polars
- Feast feature store
- dbt for transforms
- Great Expectations
Tracking & eval
- MLflow
- Weights & Biases
- Optuna
- SHAP, LIME
How we work
Six steps to a production model.
Most clients start with a 2-week ML Discovery Sprint — fixed price, real data, baseline model, and an honest assessment of feasibility.
01
Problem framing
Pin the business outcome. Frame as classification, regression, ranking, or sequence — and define the metric that matters.
02
Data audit
Inspect data quality, leakage, label drift, and class balance. The boring step that determines whether the project will work.
03
Baseline
Always ship a baseline first. Sometimes a simple model wins; if not, the baseline becomes the bar.
04
Model iteration
Architecture search, hyperparameter tuning, feature engineering — measured against a clean holdout.
05
Productionise
Package the model, build the prediction service, set up batch or streaming inference depending on the use case.
06
Monitor & retrain
Drift detection, performance dashboards, automated retraining pipelines. ML you deploy and forget is ML that quietly fails.
Frequently asked
Questions about custom ML.
What is custom machine learning development?
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Custom machine learning development is the process of building, training, and deploying machine learning models tailored to a specific business problem and dataset, instead of using off-the-shelf APIs. At Appsmediaz, we own the full lifecycle: data prep, model training, evaluation, deployment, and monitoring.
When should I use custom ML instead of an LLM?
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Use custom ML when you have structured data and need fast, cheap predictions at scale (forecasting, scoring, classification), or when explainability and regulatory compliance matter. Use LLMs for unstructured text, reasoning, and tasks where natural language is involved. Many of our projects combine both.
How much data do I need to train a custom model?
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It depends on the problem. Classical models can perform well with hundreds to thousands of examples. Deep learning typically wants tens of thousands. If you don't have enough, we look at transfer learning, synthetic data, or starting with an LLM-based solution while you accumulate training data.
How long does it take to build a custom ML model?
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A focused model with clean data ships in 4 to 8 weeks. Projects with significant data engineering, feature work, or multi-model systems run 10 to 16 weeks. We always start with a 2-week data audit and baseline before committing to the full build.
How much does custom ML development cost?
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Custom ML projects at Appsmediaz typically range from $12,000 for a focused single-model deployment to $80,000+ for full ML platforms with data pipelines, feature stores, and monitoring. We provide fixed quotes after a discovery sprint.
Explore the rest of the AI Lab
Have a prediction problem in mind?
Book a free 30-minute call with a senior ML engineer. We'll tell you honestly whether ML is the right tool, and what it would cost to find out.
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