Reporting people can rely on, built on data you can trust.
Two teams pull the same metric and get different answers. Meetings turn into arguments about whose numbers are right.
Numbers arrive days late, assembled by hand from exports, spreadsheets and copy-paste.
When the person who knows the spreadsheet is away, reporting stops.
Decisions get made on last month's picture, not what's happening in the business right now.
Built once, never trusted, quietly abandoned. The screens are there, the confidence is not.
By the time the pack is ready, the call it was meant to inform has already been made.
Building 8 took our client's Power BI estate from manual desktop publishing to a fully governed deployment pipeline, delivered and proven end to end on the licensing they already owned. The test plan passed in full with no open defects, and the documentation and handover were strong enough that the client's own data engineering team now operates the pipeline and onboards new reports themselves.
72 offices and four systems reduced to one live number, updated continuously rather than collected quarterly.
A global construction consultancy could not say how the business was performing without a quarter-long data-gathering exercise. We built the integration layer that blends its four systems into real-time executive KPIs covering every office worldwide.
Confidential client
One consolidated cost view for executives and government, refreshed automatically each cycle.
Multi-billion-dollar rail programs report cost from many delivery phases in many formats, and assembling that by hand consumes the reporting window before anyone can act on it. We built a purpose-built cost breakdown structure and an automated dashboard that consolidates every phase.
Confidential client
Six systems and several currencies reconciled into one live view of trial cost and revenue.
Global clinical trials scatter trial, cost and revenue data across systems and currencies, and reconciling it by hand is a job that is never quite finished. We combined them into a single real-time view for Saluda Medical in a common currency, and trained their team to run and extend it.
Saluda Medical
We start by understanding what people actually need to see, what decisions the reports feed, and where the current numbers fall over.
We sort out the source data so the numbers hold up: consistent definitions, one version of each metric, quality checks built in.
We build dashboards and scheduled reports on top of that foundation, so the numbers arrive without anyone assembling them.
We start by understanding what people actually need to see, what decisions the reports feed, and where the current numbers fall over.
We sort out the source data so the numbers hold up: consistent definitions, one version of each metric, quality checks built in.
We build dashboards and scheduled reports on top of that foundation, so the numbers arrive without anyone assembling them.
First dashboards are typically live in about 30 days, built inside the tools you already use.
Most AI projects fail on the data underneath, not the model. Once the reporting foundation holds up, the same governed data can do more than fill a dashboard. It can forecast, flag and prompt. That is where we put AI to work.
Every model runs on data that has been validated and governed first. No guessing from a generic dataset, no surprises from a source nobody checked.
We start from the metric that matters and work back. Churn caught before it shows up in revenue. A forecast that lands a week earlier. A fleet sized to the contract, not the habit.
Your team keeps the decision. The model does the reading, the ranking and the repetitive work nobody had time for.
A prototype proves the idea. Production proves the value. Every deployment ships with a way to track what changed, so you know whether it is working and not just whether it is running.
Gross profit margin up 35% within three months.
With tens of thousands of B2B customers, no sales team can read that pattern manually, so the quiet churn goes unnoticed until it shows up in the numbers. We built an automated recency-frequency-monetary model over Spandex's Salesforce data that predicts each customer's next purchase and prompts the right approach at the right time.
Spandex
Merchandising and marketing get the reasons behind the ratings, not just the averages.
A five-star average tells you customers were happy, not what to stock more of. We mined product reviews across Tell Me Baby's 60,000-member loyalty program to surface the sentiment behind the scores.
Tell Me Baby
$1.2 million off the bid - six fewer prime movers, same contracted service levels.
Tender pricing turns on how many trucks you actually need, and over-specifying quietly loses the work. We modelled a supermarket distribution contract in detail and showed its service requirements could be met with 88 prime movers rather than 94.
Confidential client
We structure work in phases, so you can assess progress and value at each stage rather than at the end.
You are not signing up to an open ended retainer to start working with us.
Pricing is shaped by the scope and complexity of the work, and we are clear about what you are committing to before you commit to it.
More questions about how we work? Read our FAQ
Tell us what's getting in the way and we'll tell you honestly whether we can help.