Getting the numbers out of wherever they are sitting — scraped, extracted and cleaned into dashboards and reports that answer a question somebody actually asked.
Most reporting problems are not tooling problems. They are definition problems and plumbing problems: two systems disagree about what "active customer" means, or the same figure is keyed in twice and one of them is a week behind. We start by settling what each number means and where it comes from, then build the shortest path from source to screen. That often means scraping public data that has no API, or pulling from an ERP that has one but no usable reports. The deliverable is something a person opens on a Monday morning and acts on — plus the documentation to keep it running when a source changes underneath it.
The specifics, rather than the adjectives.
A small number of screens organised around decisions — margin by product, collections by branch, pipeline by stage — instead of one page carrying every metric the business can produce. A dashboard nobody opens is a reporting cost with no return.
Every metric gets a written definition: what counts, what does not, which source wins when two disagree, and how often it refreshes. Most disputed numbers in a business are a definition problem wearing a data problem costume.
Pulling data from public pages that offer no API — listings, prices, availability, tenders, directories — into a clean structured table. Where an official API exists, we use it; scraping is for when there is no better option and the data is genuinely public.
robots.txt and rate limits respected, no personal data collected without a basis for it, and polite request patterns that do not degrade the source. Plus change detection, so a redesign upstream raises an alarm instead of silently writing wrong numbers into your reports.
Watching the sources that matter — competitor pricing, stock availability, listings, tenders — on a schedule, with the changes that are worth acting on surfaced rather than an email containing everything that moved. The value is in the alert, not the archive.
Taking marketplace exports and turning them into whatever the next system needs: cleaning, deduplicating, reconciling against stock, and producing the upload file or the picking list. Removes the afternoon of spreadsheets that currently sits between an order arriving and it being fulfilled.
The numbers arrive where the decision gets made — a scheduled email, a WhatsApp summary, a PDF for a board pack — rather than waiting for somebody to remember to open a dashboard and rebuild a spreadsheet by hand.
Extraction from ERPs, accounting systems, CRMs and databases, with the transformations written down rather than living in one person head and three macros. Feeds into SQL Server, Oracle, MySQL or a reporting tool you already pay for.
Freshness, duplicate records, missing values and outliers flagged on every refresh, with a visible last-updated time on each dashboard. Trust is built by making the gaps obvious, not by hiding them behind a confident-looking chart.
The copy-paste-and-reconcile routine that consumes a day a week and produces a different answer each time. Automating it is usually the fastest measurable return in the whole engagement, and it is the part that makes a controller trust the rest.
A concrete list of outputs. If something here is not relevant to your project, it comes out of the scope and the price — we would rather agree that in writing now.
Five steps. You always know which one you are in.
We start with the decisions you make and how often, not with the data you hold. A metric that does not change a decision is a metric that should not be on the dashboard.
Where every number actually comes from, who owns it, how fresh it is and where two sources disagree. Usually the point at which a long-standing dispute turns out to be a definition problem.
Scrapers, API clients or direct database reads, whichever the source supports. Built with validation and change detection from the start, because sources break and that failure has to be loud.
Cleaning, joining and shaping into the tables the questions need, then the smallest dashboard or report set that answers them. Iterated with the people who will use it, not demonstrated to them once.
Documentation of every source, rule and refresh interval, and what to do when a source changes. Plus monitoring so you find out from an alert rather than from a wrong number in a board pack.
The platforms and frameworks we build, integrate and maintain on. We would rather work in a stack your team can hire for and hand over than pick whatever is interesting this year — and where an existing platform does the job better than custom code, we will say so.
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