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Data that arrives on time, in shape, every time.

Ingestion, transformation and integration pipelines with tests, contracts and monitoring, so downstream AI and reports stop breaking.

Let's Build Together

One call with a senior engineer. A straight answer on what it would take.

Illustration: Data engineering & pipelines

Where you are. Where you’ll be.

You need this if

  • Someone fixes a broken pipeline every week
  • Teams export spreadsheets because they don't trust the warehouse
  • Your AI pilot spent most of its time on data wrangling

What changes for your business

  • Fresh, reliable data feeding every report and model
  • Breaking changes caught before they reach consumers
  • Data engineers building, not firefighting

What we hand over

  1. Ingestion from SaaS, databases, files, events and APIs
  2. Tested transformation layer (dbt, Spark or equivalent)
  3. Data contracts with source-system owners
  4. Pipeline observability: freshness, volume, quality alerts

What it is

Data engineering builds and runs the pipelines that move data from source systems into warehouses, analytics and AI applications, on time and correct. Pipelines are infrastructure: when they break quietly, every report, model and agent downstream inherits the error, usually without anyone noticing for days.

Brittle pipelines are the silent tax on every data team: a source schema changes, a job fails at 4am, and the dashboard the CEO reads shows yesterday's numbers. We build pipelines like software, with version control, automated tests, data contracts with source owners, and observability that alerts on freshness, volume and quality. Batch, streaming or change-data-capture, chosen per source.

Why now
60% of AI projects unsupported by AI-ready data will be abandoned through 2026. Gartner, 2025 (opens in a new tab)
Last reviewed

How it runs

  1. 01

    Diagnose

    Typically 2–4 weeks

    We map the problem, your data and your systems, and agree the one number that defines success.

  2. 02

    Prove

    Typically 4–8 weeks

    A working pilot on your real data, measured against that number. Not a slide demo.

  3. 03

    Ship

    Scoped to the outcome

    Production build with security, monitoring, cost controls and documentation included, not upsold.

  4. 04

    Run

    Ongoing, optional

    We operate what we built against clear service levels, or train your team to. Your call. No lock-in.

Questions you’ll ask

What is a data contract?
An agreement between the team that owns a source system and the teams that consume its data, covering schema, meaning and freshness. Changes are versioned and announced, so an upstream edit no longer breaks a pipeline at 4am.
Batch, streaming or change-data-capture: which do we need?
Each source gets what its use case needs. Daily finance reporting is fine in batch; fraud detection or operational agents need streaming or change-data-capture. Mixing them per source keeps cost proportional to the value of freshness.
How does this help our AI projects?
Most AI pilots spend the bulk of their time wrangling data. Reliable, tested pipelines with clear ownership turn that into a solved problem, and Gartner expects 60% of AI projects without AI-ready data to be abandoned through 2026.

Sound familiar? Let’s fix it.

One call with a senior engineer. You’ll leave with a straight answer on what it would take.

Let's Build Together

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