Dataset Engineering & Enrichment

We turn clean data into datasets a model can learn from — structured, labeled, enriched and reproducible.

The problem

Clean data is not the same as modeling-ready data.

Most AI projects stall here: no labels, no historical depth, no features, no way to rebuild the same dataset twice.

What we do

  • Design the dataset around the decision it has to support, not around what is easy to export
  • Engineer features: recency, frequency, value, behavioral sequences, seasonality
  • Build and maintain labels, including historical outcomes for supervised learning
  • Enrich with large-scale US data sources and contextual signals (to confirm: which sources, under which licenses, and what is permitted for EU clients)
  • Balance, sample and split datasets so models train and validate honestly
  • Version every dataset, so any result can be reproduced months later

How it works

  1. Define the target: the exact question the model will answer
  2. Assemble the raw material from your foundation layer
  3. Engineer features and labels, then test them for leakage and bias
  4. Package, version and document the dataset for the modeling team

What you get

Versioned, documented, modeling-ready datasets, a feature catalog your team can reuse, and a pipeline that refreshes them on schedule.

Where AI comes in

We use models to do what manual work cannot: automatic labeling and classification at scale, embeddings that turn text and behavior into usable features, and generated data to fill gaps where real examples are too rare.

Related: First-Party Data Foundation · AI Audience Modeling

FAQ

It depends on the question. Twelve to twenty-four months is a comfortable starting point for most predictive use cases.

Only as enrichment on top of your first-party data, and only where licensing and privacy allow it.

You do, including the features and documentation we build.

Yes. Everything is delivered in standard formats with documentation, not locked in a black box.

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