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Data & Machine Learning

The data foundation every AI system depends on, and the predictive models that turn your history into better decisions.

Overview

AI is only as good as the data under it.

Every AI system we build depends on data that is connected, trustworthy and available under the right controls. In most businesses that data is scattered across a CRM, an ERP, spreadsheets, inboxes and chat apps, each with its own identifiers and gaps.

We build the foundation first: pipelines that bring sources together, models that recognise the same customer or product across systems, quality checks that catch problems early, and a governed layer that analytics, machine learning and generative AI can all use.

On top of it we build predictive models for decisions that repeat every day: which leads will convert, how much stock to order, which customers are about to leave, what to quote. Each model is tracked against reality once it is live.

When it fits

Signs your data is holding AI back.

  • Reports disagree

    Finance, sales and operations quote different numbers for the same thing.

  • Decisions run on gut feel

    Forecasts, prices and priorities are set by experience because the data is too hard to use.

  • Critical numbers live in spreadsheets

    Key figures are rebuilt by hand every week and nobody trusts the latest version.

  • Every AI idea starts with clean-up

    Each new project spends its first months fixing the same data problems.

What ships

What we deliver.

  • Data platform

    A cloud warehouse or lakehouse with automated pipelines from your core systems.

  • Entity resolution

    One view of each customer, product and supplier across every system.

  • Analytics and dashboards

    Self-serve reporting built on agreed metric definitions.

  • Predictive models

    Forecasting, propensity, churn and pricing models running in production.

  • Computer vision

    Image and video models for inspection, counting, document capture and safety.

  • MLOps

    Versioning, monitoring, retraining and drift alerts for every model.

Use cases

Machine learning that pays its way.

Demand and sales forecasting

Models trained on your sales history, seasonality, promotions and external signals such as holidays and events produce forecasts with ranges rather than single numbers, refreshed automatically as new data arrives.

  • Product, branch and channel forecasts
  • Ramadan, holiday and event effects
  • Forecast accuracy tracked over time

Process

How the engagement runs.

  1. 01

    Audit

    Inventory sources, quality and ownership; agree priority metrics.

  2. 02

    Connect

    Pipelines from core systems into a governed platform.

  3. 03

    Model

    Clean, resolve and model data around your business entities.

  4. 04

    Predict

    Train, validate and explain models against a baseline.

  5. 05

    Deploy

    Serve predictions into dashboards, apps and workflows.

  6. 06

    Monitor

    Track accuracy and drift; retrain on schedule or trigger.

Deliverables

What you keep.

  • Data platform and pipeline code
  • Metric definitions and data catalogue
  • Trained models with validation reports
  • Dashboards and monitoring
  • Runbooks for retraining and support

Stack

Technology we work with.

Data

  • Python
  • SQL
  • dbt
  • Airflow
  • Kafka

Platforms

  • BigQuery
  • Snowflake
  • Databricks
  • PostgreSQL

Machine learning

  • scikit-learn
  • XGBoost
  • PyTorch
  • MLflow

BI

  • Power BI
  • Looker Studio
  • Metabase

FAQ

Frequently asked questions

Do we need a data warehouse before we can use AI?

Not always. Some generative AI use cases work directly on documents. But anything that predicts, measures or acts on business records benefits from a clean, connected data layer, and we often build the two in parallel.

How much historical data do predictive models need?

Enough past examples of the outcome to learn from, across the seasons that matter. That varies a lot by use case, so we check it in the audit before promising a model.

Can predictions feed our existing tools?

Yes. Scores and forecasts are written back to your CRM, ERP or dashboards so people use them where they already work.

Who maintains the models?

We set up monitoring and retraining, then either run it for you under a support agreement or train your team to own it.

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