Data Engineering and Integration Services

Most data problems are not analysis problems. They are plumbing problems: the data exists, it is in the wrong place, in the wrong shape, or in a format only one piece of software can open.

We do the plumbing. Pipelines, extraction, transformation, reconciliation between systems that disagree, and the reporting layer on top once the numbers can be trusted.

The work we are proudest of here is the awkward kind. Pulling structured quantities and scheduling data out of building models — proprietary formats, no convenient export, enormous files — and turning it into something a platform can query. That is not a category most agencies compete in, and it is the one we would most like to be judged on.

What We Build

Not every project needs all of this. These are the parts we go deep on when a product calls for them.

  • Extraction from difficult formats

    Proprietary files, legacy exports, industry formats with no public specification, and documents that were never meant to be parsed. Where the data exists but nothing will give it to you in a usable shape.

  • Pipelines and ETL

    Scheduled and event-driven jobs that move data between systems, with transformation, validation and a record of what ran. Built to be re-runnable safely, because the first version of any pipeline is wrong somewhere.

  • Reconciliation between systems

    Two systems, one truth, different numbers. We find out which is right, why they diverge, and build the process that keeps them aligned. More often a definition problem than a technical one, and we say so when it is.

  • Data modelling and warehousing

    Getting data into a shape that answers questions quickly, rather than a shape that mirrors however it happened to arrive.

  • Reporting and analytics layers

    The query layer and the API behind the reports. Where the interface is the hard part, see dashboard and data visualisation design.

  • Integration with third-party and in-house systems

    ERPs, CRMs, industry platforms, and the in-house system with no documentation and no one left who built it. See backend and API development.

  • Data quality and validation

    Rules, checks and alerting, so bad data is caught where it enters rather than discovered in a board report.

  • Migration

    Moving years of data from an old system to a new one, including deciding what to do with the records that do not fit either model.

When Teams
Come to Us

  • The data is trapped in a format nothing reads

    It exists, you own it, and you cannot get at it. This is the problem we most want to hear about.

  • Two systems disagree and both are official

    Finance says one number, operations says another, and both can show you where theirs came from.

  • Reporting runs on an export and a spreadsheet

    Somebody spends two days a month producing a report by hand. That is a pipeline with a person inside it.

  • A product needs a reporting layer

    The application holds the data and there is no good way to ask it questions.

  • A migration that has to be right

    Replacing a system where the data is the valuable part and losing a field is not recoverable.

Do You Have a Project Idea?

Tell us what you want to build. We come back with a rough estimate and an honest view of what it takes, before anyone signs anything.

Let's discuss

How We Work

What data exists, where, at what grain, how fresh, and how much of it is trusted. This stage frequently changes what the project turns out to be.

For extraction work, a small proof against your real files before anyone commits to a full scope. We would rather find the hard part in week one.

The target model, the pipeline shape and the failure behaviour, agreed before building.

Built incrementally, with each stage runnable and inspectable.

Reconciliation against the source, with the differences explained rather than rounded away.

Monitoring, alerting and a documented handover. Every project includes one month of bug fixing after launch at no extra cost.

Stack

  • Languages: Python, Node.js with TypeScript, PHP
  • Pipelines: scheduled and event-driven jobs, queues and workers
  • Databases: relational, with the warehouse model designed for the questions being asked
  • Formats: industry and proprietary formats handled case by case, which is most of the skill
  • APIs: REST and GraphQL for the layer that sits on top
  • Delivery: CI pipelines, staged deployment, monitoring and alerting on every scheduled job

Selected Work

  • Planit. Construction planning and scheduling platform. We extract structured quantity and schedule data out of building models and turn it into something the platform can query — the work behind its 4D scheduling. The downstream users are among the largest contractors and architecture practices in the industry.
See all work

Industries We Build For

Construction and PropTech is where this work goes deepest, because the industry runs on formats designed for drawing rather than for querying. We also do this work for healthcare platforms, booking and scheduling systems, and travel and mobility.

How We
Work Together

  • Feasibility study

    A short, fixed engagement against your real data, ending in a written answer about whether and how the extraction or integration can be done. The sensible first step on anything unusual.

  • Fixed scope

    A defined build, priced per module, once feasibility is settled.

  • Ongoing team

    Engineers working to your roadmap as the data estate grows.

  • Support retainer

    Pipelines need looking after. See
    We price every project individually. There are no packages, because the work does not arrive in packages.

Questions We
Get Asked

  • Often, and it is worth a feasibility study rather than a guess. We work against your real files first, for a fixed price, and tell you honestly what is achievable before anyone commits to a build.

  • Reconciliation against the source, with the differences listed and explained rather than tidied away. A migration that matches on totals and differs on records is a migration that has not been checked.

  • Less often than it is proposed. A well-modelled database answers most questions most companies have. We will tell you when the extra layer is genuinely earning its cost.

  • Yes. Inheriting pipelines is similar to inheriting any other system and starts the same way, with an audit. See project takeover and modernisation.

  • Rough estimate once we understand the data, detailed per-module estimate once the scope is agreed. For extraction work we would usually propose a feasibility study first, because an estimate before anyone has looked at the real files is not an estimate.

Related Services

Let’s talk about your project

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