Data Modeling Services for Reliable Enterprise Analytics

We only build a model if it beats today’s method on a decision someone owns. Every use case gets a named decision owner, kill criteria and a baseline before any build starts. Models are built with ML Studio library components on 1Platform, and each one is placed where the decision is made, whether that’s a plan, a price list, a campaign or an alert.

We score every candidate use case on six lenses before any model is built: decision value, history and granularity, feature availability, today’s baseline, decision integration, and operability. You get a scored shortlist with kill criteria and a named decision owner, plus a value case and build plan for the top use case.

We agree acceptance thresholds before training, not after seeing the results. We run naive and statistical baselines against today’s method, then AutoML challengers that are backtested against that baseline, all on your own data. An agent writes the comparison report, and your data science lead signs it off.

We stop, and that’s a good result. A model nobody uses is a cost, not an asset. Agreeing kill criteria up front means teams don’t keep tuning a model the data can’t support.

Enough history, at the right level and frequency, for the model to learn from. Pricing work, for example, needs price history. We profile an approved extract for history, gaps, granularity and outliers, and score its fitness before any build. If data history or quality is the blocker, we start with a Data & Analytics Maturity Assessment.

An agent does the first pass at every step. It profiles your data, runs baselines, proposes features, trains challenger models and drafts the value case. A named person checks and signs off each step before the next one starts: your data owner, data science lead, domain expert, and business owner with finance.

We track accuracy, drift and value, and retrain on triggers agreed with you. A drift monitor flags changes and drafts the case for retraining. You also get a monitoring and retraining runbook covering drift triggers, owners and retraining steps.

No. Agents run in your own cloud tenancy on Azure, AWS, GCP or Databricks. They read schemas, configs, logs and documents where they are, and sample raw records only with your written approval. They call only the LLM endpoints you’ve already approved, with logging you can audit.

Model work runs on your own ML platform, for example Databricks or Azure ML. Model output then feeds the rest of your AI stack. A forecast can feed a financial or demand plan, a prediction can become a tool for an agent, and business users can query model results in plain language.

Yes. You can choose onsite, hybrid (the default), AI-led remote, co-delivered or continuous managed delivery. In the co-delivered model, your team runs the method with our agents while Polestar Analytics coaches and checks quality. It suits a client building its own data science team.