Dreamware / Services / AI & Intelligent Systems / Machine Learning & Data Science
Machine Learning & Data Science
Predictive models, NLP, computer vision, recommendation engines. Full MLOps lifecycle.
About this service
Machine learning works when the problem is right, the data is real, and the deployment pathway is clear from the start. We've seen too many ML projects produce impressive notebooks that never make it to production — we build models that run in your systems and improve your outcomes.
Our data science work covers the full spectrum: classification and regression models for prediction and risk scoring, NLP for document understanding and text classification, computer vision for inspection and quality control, and recommendation engines for personalisation. We select techniques based on what your data can actually support — not what's fashionable.
MLOps is built in from the start. Model versioning, automated retraining pipelines, drift detection, and monitoring are part of the deliverable — not an afterthought. We design for the reality that models degrade over time and need to be maintained like any other software system.
How Dreamware approaches this
We start with a data audit — understanding what you have, its quality, and whether it can actually support the model you need. If the data isn't ready, we'll tell you that early and help you build a plan to get there. We avoid the trap of building models on convenient data that doesn't reflect the real problem.
Model development follows a rigorous experimental process: baseline models first, then systematic improvement. We use cross-validation, held-out test sets, and business-metric evaluation — not just accuracy scores. Before deployment, we work through failure modes and edge cases with you so there are no surprises in production.
What you get
- Trained and validated ML model — with documented performance metrics and known limitations
- MLOps pipeline — automated training, evaluation, and deployment workflow
- Model monitoring setup — drift detection and alerting
- API or integration layer — so your model is accessible from your existing systems
- Model card — documentation of training data, intended use, limitations, and performance across segments
- Retraining runbook — instructions for when and how to retrain
Investment guide
Data science projects range from $20,000–$80,000 NZD depending on data complexity, model sophistication, and MLOps requirements. Exploratory data analysis and feasibility assessments can be scoped separately at $5,000–$12,000 NZD. Ongoing model monitoring and retraining support available on retainer.
All pricing in NZD excluding GST. Fixed-price engagements where scope allows — we'll confirm pricing after a free scoping conversation.
Ready to get started?
Book a free conversation. We'll tell you honestly what's realistic, what it costs, and how we'd approach it.