Service

Data your organization can act on

Most organizations do not have a data shortage; they have data spread across systems that were never designed to be read together. We build the pipelines, models and interfaces that make that data usable, and apply AI where it changes an outcome rather than where it demonstrates well.

Discuss a Data Problem

Business problems

What clients come to us with

If one of these matches your situation, we have a starting point.

Reporting takes days and arrives too late to act on

We consolidate sources into a governed model and put current numbers in front of the people who make the decision.

Every team reports a different version of the same number

We define the metrics once, in one place, with the lineage visible so the definition can be audited.

Staff spend hours on manual review and data entry

We automate the repeatable part — classification, extraction, routing — and keep a human decision point where accountability requires one.

An AI pilot cannot move into production

We address the parts pilots usually skip: data quality, evaluation, monitoring, cost and access control.

Capabilities

AI & Data capabilities

Data engineering

Pipelines, warehouses and integration across operational systems, with validation and lineage built in.

Analytics and reporting

Dashboards and reporting built around the decision being made, not around what the source system happens to expose.

Applied AI and machine learning

Document processing, classification, forecasting and retrieval systems, scoped to a measurable task.

Process automation

Automating repetitive workflows end to end, including the integration and exception handling that make it hold up.

Data governance

Access control, retention, quality rules and audit trails appropriate to regulated environments.

Evaluation and monitoring

Accuracy benchmarks, drift detection and cost tracking so model behaviour stays observable after launch.

How we work

Our delivery approach

Locate

Identify the decision or workflow the work must improve, and how it is measured today.

Prove

Validate feasibility against real data before committing to a build.

Engineer

Build the pipeline, model and interface as a supportable production system.

Monitor

Instrument accuracy, cost and usage, and tune against what production shows.

Technology

Technologies we work with

Technology is chosen to fit the client environment and the people who will maintain it, not the other way round.

Platforms

Azure
AWS
Google Cloud
Databricks
Snowflake

Data

Python
SQL
dbt
Airflow
Kafka

Analytics

Power BI
Tableau
Looker

PyTorch
scikit-learn
LLM APIs
Vector databases

Experience

Relevant experience

Manufacturing

Product traceability and production management

Challenge: Production and supply records were held in systems that could not be reconciled, so a unit could not be traced back through its production history.
Approach: Built a shared traceability record spanning production and distribution events, with each step written once and readable by every downstream system.

Distributed ledger
Systems integration
Web application

Operations

Online warehouse management system

Challenge: Stock movement, locations and fulfilment status were tracked manually, leaving inventory accurate only as of the last count.
Approach: Modelled the real warehouse process first — receiving, put-away, picking, dispatch — then built to it rather than to a generic inventory template.

Web application
Relational database
Role-based access

Applied AI

Voice-driven AI reservations and ordering

Challenge: Orders and reservations arrived by phone and had to be transcribed by staff, which capped throughput at busy periods.
Approach: Applied speech recognition and intent handling to the narrow, high-volume part of the conversation, leaving staff to handle exceptions.