Data Science & Engineering
Build trusted data foundations, analytics layers and pipelines that turn scattered operational records into decisions people can defend.
Every organisation already has the data. Almost none of them can act on it on a Monday morning.
Acaiberry builds the layer underneath data-driven work: the architecture, pipelines, models and governance that turn records scattered across a dozen systems into numbers your leadership team can argue over with confidence. Engineers do the work, so what we hand over holds up when volumes double.
Sources -> Pipelines -> Foundation -> Decisions
A number nobody trusts is worse than no number
Data becomes an asset at the point where people stop checking it. Until then it is a cost: hours of reconciliation, meetings that stall on whose figure is right, decisions made on instinct because the report arrived too late to matter. Getting to trust is an engineering problem, and it is the one we solve first.
You probably recognise at least three of these
Three teams produce three different revenue figures for the same quarter.
The monthly report takes four days and one person who knows where the files live.
Nobody can say where a number came from, so nobody will defend it in a board meeting.
Analysts spend most of the week preparing data and the remainder analysing it.
A new dashboard request means another spreadsheet export, by hand, every month.
Your AI or machine learning project stalled at the data stage.
Eight places machine learning earns its keep
Grouped by the business capability rather than the algorithm, because that is how budgets and mandates are actually organised.
Predictive analytics
- Which customers, machines, shipments or loans need attention before something goes wrong.
Forecasting
- Demand, revenue, cash position, staffing and stock, projected with ranges rather than a single flattering line.
Customer segmentation
- Groups defined by behaviour rather than assumption, described in terms your marketing and service teams can use.
Recommendation systems
- Next product, next article, next best action, tuned against your own conversion data rather than a generic model.
Optimisation models
- Routes, schedules, pricing, inventory levels and resource allocation, solved against your real constraints.
Statistical modelling
- Cause and effect separated from coincidence, with experiment design where a controlled test is possible.
Scenario simulation
- What happens to the plan if the price moves, the supplier fails or the intake doubles. Answered before it happens.
Decision intelligence
- Models wired into the workflow so the insight arrives at the person making the call, at the moment they make it.
Reporting people actually open
A dashboard is a design problem before it is a technical one. We start from the decision the viewer has to make and cut everything that does not serve it.
Executive dashboards
One screen, the handful of measures that govern the business, and a route to the detail behind each one.
Operational dashboards
Built for the shift, the ward, the branch or the warehouse floor, refreshed at the pace the work moves.
KPI frameworks
Agreed definitions, owners and calculation logic for every measure, so the same metric means one thing across departments.
Self-service analytics
Governed datasets and a semantic layer that let analysts answer new questions without opening a ticket.
Business intelligence layer
Implementation on Power BI, Tableau, Looker, Superset or whatever you already own, with the modelling done properly underneath.
Real-time monitoring
Live views and threshold alerts for the processes where finding out tomorrow is finding out too late.
Interactive reporting
Filterable, drillable reports that replace the monthly PDF and the manual export behind it.
From scattered sources to decisions people trust
Every data platform has to move through these five stages. Pick a stage to see how we turn messy operational records into governed analytics, models and data products.
Sources
We start by finding out what you actually have. Core banking, ERP, CRM, hospital records, point of sale, sensors, field apps, third-party feeds, and the twelve critical spreadsheets nobody has admitted to yet. Each one gets assessed for quality, coverage, refresh frequency and access rights before it is connected to anything.
The hard part is usually the domain
Knowing what a patient episode, a farm plot or a beneficiary household actually is in your data matters more than knowing the tooling. These are the sectors where we already do.
Healthcare
Patient records across systems, clinical reporting, bed and staffing forecasts, claims and billing analytics.
Government
Records digitisation, revenue and expenditure analytics, service delivery dashboards, open data platforms.
Banking
Regulatory reporting, credit risk data, fraud signals, single customer view across products and channels.
Retail
Sales and stock consolidation across outlets, demand forecasting, basket analysis, supplier performance.
Manufacturing
Sensor and MES data capture, yield and downtime analysis, quality tracking, supply chain visibility.
NGOs
Field data collection, monitoring and evaluation, donor reporting, beneficiary deduplication across programmes.
Education
Student information consolidation, attendance and retention analysis, assessment reporting, resource planning.
Agriculture
Farm, weather and market data integration, yield analysis, input planning, traceability along the supply chain.
Your sector
Not listed? The assessment comes first either way. We would rather learn your domain than assume it.
Your sector. Not listed?
Bring the problem. We start with a readiness assessment before promising anything.
Book a discovery callHow we deliver
Eight stages, and you own the output at every one
Each stage ends in something you keep: a document, a running pipeline, a reviewed dashboard. No phase finishes with a promise about the next one.
Data assessment
An inventory of sources, their quality and their owners, plus an honest read on what your current data can and cannot support.
SOURCE MAP • READINESS REPORT
WHAT CHANGES
The meeting stops being about the numbers
Common questions
Before you get in touch
Start with an assessment, not a proposal
Give us two weeks with your systems and we will come back with a map of your data, the gaps that matter, a target architecture and a costed first phase. If the answer is that you need three fixes rather than a platform, that is what the report will say.
Book a data assessment