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ClearFeature

Enterprise

Bring ClearFeature into production with direct technical support.

Every ML stack is different. We can work with your team on architecture, deployment, migration, and the first production feature workflows.

Start with the open-source platform

Evaluation does not require a sales process. ClearFeature is Apache-2.0: clone the repository, run the quickstart, read the architecture docs, and put it in front of your engineers before you ever put it in front of us.

When direct support is useful

Some adoptions are straightforward. These usually are not:

Existing custom feature pipelines

Years of batch SQL and bespoke services encode real business logic that has to survive the migration.

Migration from batch SQL + online service

Two implementations must converge into one Feature Project without breaking live decisions.

Credit or fraud decision systems

Decision workflows where correctness, reproducibility, and rollout order carry real risk.

Self-hosted deployment design

Fitting the platform services into your network, storage, and operational standards.

First Feature Project design

Getting entity keys, source modeling, and registry structure right the first time.

Point-in-time migration

Moving historical training data onto availability-aware semantics without invalidating existing models.

What we can discuss

Concretely, a working engagement can cover:

  • Architecture review
  • Deployment planning
  • Migration from existing feature pipelines
  • First production Feature Project
  • Point-in-time migration
  • Focused production pilot
  • Rollout planning
  • Technical support

Production adoption is architecture-specific.

Production deployments are scoped individually because architecture, integration, and support requirements differ across teams. Tell us about your environment and we can discuss the right deployment and support model.

A typical first step is a focused architecture conversation around one real feature workflow — not a generic enterprise sales process.

A typical evaluation path

No two adoptions are identical, but the shape tends to repeat:

1

Architecture conversation

You describe your current feature pipelines and production ML systems; we map where ClearFeature fits — and where it does not.

2

Targeted pilot

One real feature workflow, one Feature Project, historical and live execution — a scoped proof on your data, in your infrastructure.

3

Production adoption

Expand from the pilot workflow with a deployment and support model agreed along the way.

Tell us about your feature infrastructure.

How do features get from research to production today? That one answer is usually enough to start a useful conversation.