Connect
Data & Integration
Pipelines, APIs and analytics that get your systems talking — usually the unglamorous prerequisite for everything else on this page.
Most organisations do not have a data problem so much as a plumbing problem. The numbers exist; they are in four systems that disagree, and the reconciliation happens in a spreadsheet somebody maintains by hand.
That spreadsheet is worth paying attention to. It is a working specification of a process the business depends on, written by the person who understands it best, and it is usually the fastest way to find out what the integration actually needs to do. We tend to start there rather than with an architecture diagram.
We build the connective tissue: ingestion that handles the messy cases, transformations you can trace, and warehouses whose numbers survive an audit. Then reporting on top, so people stop asking analysts for screenshots. The test of a good pipeline is not that it runs — it is that when a number looks wrong, someone can find out why without asking us.
This is also the work that determines whether an AI project succeeds. A model is only as good as what you can feed it, and retrieval quality is a data-engineering outcome long before it is a prompt-engineering one. Teams that skip this step and go straight to the model reliably end up doing it afterwards, more expensively.
Integrations fail. Third-party APIs go down, change their schema without warning, and rate-limit at inconvenient moments. The difference between a system that survives that and one that quietly corrupts your data is whether the failure modes were designed for or discovered.
What is included
- Mapping the systems, the flows between them, and where the manual steps are
- Ingestion pipelines with retries, backoff and dead-letter handling
- Transformations under version control and tested
- Warehouse and data model design
- API and webhook development, documented
- Data quality checks that alert before anyone downstream notices
- Dashboards and reporting for the people who make decisions
- Backfill and historical migration
What we do not do
- Dashboards nobody has agreed to act on
- Warehouse builds with no named owner afterwards
- Integrations that silently drop records on failure
- Collecting data with no defined use for it
How the work runs
- 01
Follow the spreadsheet
We trace how the numbers are produced today, including the manual steps. That is where the real requirements are.
- 02
One pipeline, end to end
A single flow from source to report, including the failure handling. It settles the architecture questions with a working example rather than a debate.
- 03
Reconcile against the old way
The new numbers run alongside the existing process until they agree — and where they disagree, we find out which one is wrong. This step is what earns trust in the output.
- 04
Hand over the monitoring
Quality checks and alerts owned by your team, so a broken feed announces itself instead of being discovered at month end.
Where we have done this
One project you can read in full.
Capabilities
- Data engineering and ETL/ELT pipelines
- Systems integration and middleware
- API and webhook development
- Data warehousing and modelling
- Analytics and business intelligence dashboards
- Workflow automation
- Identity and access management
- Data quality monitoring
- Change data capture
- Event-driven architecture and message queues
- Third-party and legacy system integration
- Reporting automation
Questions we get asked
Our systems do not have proper APIs. Is this still possible?
Usually. Options range from database replication to file-based exchange to, in the worst case, a carefully built scraper against a legacy interface. Each carries different fragility, and we will be explicit about which one we are proposing and what will break it. Legacy integration is normal work, not a blocker.
How do you handle systems that disagree?
By establishing which one is authoritative for each field, in writing, before building anything. Most reconciliation pain comes from that question never having been answered — two systems both believing they own the customer record. This is a business decision more than a technical one, and we will push for it to be made explicitly.
Do we need a data warehouse?
Only if you are combining sources or need history your operational systems do not keep. If you have one main system and want better reports out of it, that is a reporting problem and considerably cheaper. We would rather point that out than sell you a warehouse.
Can you work with our existing BI tool?
Yes. The warehouse and pipelines are the part that matters and are tool-agnostic; whichever BI layer your team already knows is usually the right one to keep. Switching tools rarely fixes a problem that originates upstream in the data.
How long does an integration take?
A straightforward two-system integration is typically a few weeks. What extends it is almost never the code — it is access, credentials, undocumented behaviour in the third-party system, and edge cases nobody remembered. We front-load discovery of those precisely because they dominate the timeline.
How is this different from buying an integration platform?
Off-the-shelf platforms are a good fit for common connections between popular tools, and if that is your situation you should use one. Custom work earns its cost when the logic is specific to your business, the systems are unusual, or per-record pricing at your volume exceeds the cost of building it. We will say which case you are in.