Service

Data & ML Engineering

Pipelines, fine-tuning, evaluation, and model deployment — the infrastructure real AI runs on.

Data & ML Engineering

SCORPBIT · Data & ML Engineering

Real AI runs on real infrastructure: pipelines that don't silently fail, features that stay fresh, models that are versioned, evaluated, and observable. We build the data and ML backbone your agents and products depend on.

What's included

  • 01Pipelines, feature stores & vector search
  • 02Fine-tuning & model deployment
  • 03Continuous evaluation & monitoring

What you can expect

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Pipelines with lineage and SLAs

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Vector search and feature stores done right

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Continuous evaluation in production

How we engage

  1. 01

    Discovery sprint

    One week with our strategists and discovery agents — mapped workflows, ranked opportunities, a costed plan.

  2. 02

    Pilot in production

    A working system on real data in weeks — with guardrails, evals, and human checkpoints from day one.

  3. 03

    Scale & operate

    We harden, expand, and run it 24/7 — or hand it to your team with the playbooks to own it.

Where teams put it to work

Reliable data pipelines

Pipelines with lineage, SLAs, and fresh features — the backbone your agents and products actually depend on.

Vector search & feature stores

Retrieval and feature infrastructure done right, so RAG and models get the data they need, when they need it.

Model deployment & monitoring

Versioned, evaluated, observable models — with continuous evaluation so quality is measured, not assumed.

Frequently asked questions

Do we need this before doing AI?

Real AI runs on real infrastructure. Pipelines that don't silently fail and evaluation in production are what separate an impressive demo from a system you can trust.

Can you work with our existing data stack?

Yes. We build on your warehouse or lake and your existing tools, adding lineage, feature stores, and continuous evaluation where they're missing.

How do you keep models from silently degrading?

Continuous evaluation against golden datasets plus production-traffic sampling, so model quality is a monitored metric that alerts when it drifts.

Ready to Put AI to Work?

Tell us about your business and we'll show you exactly where autonomous AI can move the needle — in a free 30-minute strategy call.

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