Machine Learning Consulting
Machine learning that reaches production.
We audit your data, choose the method that actually fits the problem, and ship models with pipelines, evaluation, and monitoring attached — in your cloud, documented for your team.
Engagement start
2–3 weeks
Feasibility, data audit, and a costed delivery plan
First model live
6–10 weeks
Pipeline, evaluation harness, deployment, monitoring
Deployment
Your cloud
AWS, GCP, Azure, or self-hosted — no forced migration
Handover
Documented
Runbooks, retraining scripts, and team walkthroughs
What the engagement includes
Data audit and feature engineering
We inspect the sources you actually have, quantify quality and leakage risk, and design features and labels that survive production conditions.
Forecasting and demand models
Time-series and hierarchical forecasting for revenue, volume, staffing, and inventory, with confidence intervals decision-makers can act on.
Classification, ranking, and scoring
Churn, credit and risk scoring, lead prioritisation, fraud and anomaly detection using gradient boosting and calibrated probability outputs.
MLOps and training pipelines
Reproducible training, versioned datasets and models, CI for retraining, and automated promotion gates so releases are boring.
Evaluation and monitoring
Offline eval sets, shadow deployment, drift and data-quality alerts, and dashboards tied to the business metric the model exists to move.
Governance and explainability
Feature attribution, audit logs, access controls, and documentation suited to regulated finance, healthcare, and public-sector reviews.
Problems we are usually brought in for
Risk and credit scoring
Replace static rule sheets with calibrated models that keep an audit trail for every decision, including the features that drove it.
Demand and capacity forecasting
Forecast call volume, order flow, or staffing needs so operations planning stops relying on last month's spreadsheet.
Churn and retention
Rank accounts by risk with reason codes attached, so the retention team gets a queue instead of a probability column.
Anomaly and fraud detection
Detect abnormal transactions, sensor readings, or process deviations in streaming data, with tunable precision and recall targets.
Document and text intelligence
Classify, extract, and route incoming documents and tickets, combining ML models with retrieval where free-form language is involved.
Model rescue and review
Audit a stalled or underperforming model, find leakage, drift, or evaluation flaws, and get it to a defensible production state.
How we differ from a generic ML vendor
| Aspect | Premium Robots | Generic vendor |
|---|---|---|
| Starting point | Data audit and feasibility before any model | Model-first proposal |
| Delivery | Production pipeline, evals, and monitoring | Notebook and slide deck |
| Method choice | Classical ML or LLM, whichever fits | Whatever the vendor sells |
| Ownership | Your cloud, your repo, documented handover | Locked to a hosted platform |
| Team | Senior engineers on the actual build | Junior staffing behind a lead |
Frequently asked questions
- What do your machine learning consulting services cover?
- We work end to end: problem framing and feasibility, data audit, feature and label design, model selection, offline and online evaluation, deployment, monitoring, and handover to your team. Where a simpler approach wins, we say so instead of shipping a model you have to maintain.
- Do we need a data team already in place?
- No. Many engagements start with a data audit: we map the sources you have, the quality issues in them, and what needs to exist before a model is worth training. If your data is not ready, the first milestone is the pipeline, not the model.
- How do you decide between classical ML and an LLM?
- Tabular prediction, forecasting, ranking, and anomaly detection usually go to gradient boosting or classical statistical models — cheaper, faster, easier to explain. Unstructured text, documents, and conversation go to LLM or retrieval architectures. Most production systems we ship use both.
- How is model quality measured before launch?
- We agree on the business metric first, then build an offline eval set plus a shadow or A/B rollout path. Every model ships with baseline comparison, drift monitoring, and a rollback plan.
- Can you work with our existing cloud and stack?
- Yes. We deploy on AWS, GCP, Azure, or self-hosted infrastructure, and integrate with Postgres, Snowflake, BigQuery, Airflow, dbt, MLflow, and Kubernetes. We do not require you to migrate to a new platform.
- How long is a typical engagement?
- A scoped feasibility and data audit runs 2–3 weeks. A first production model, with pipeline, evals, and monitoring, is typically 6–10 weeks depending on data readiness.
Start with a data audit
Describe the decision you want a model to improve. We will tell you whether your data supports it, and what the first production milestone should be.