Enterprise Data & AI
Fix the data foundations blocking AI, analytics, and growth.
We help enterprises build production-grade data platforms that are reliable, scalable, and ready for AI workloads. No hype — just engineering that works in complex environments.
Most organisations don't have an AI problem. They have a broken data foundation problem.
AI initiatives fail when built on unreliable, ungoverned, or inaccessible data. We fix the foundations first — so that everything built on top of them actually works.
What we deliver
Measurable outcomes, not theoretical roadmaps
Reduced operational cost
Through platform consolidation, pipeline optimisation, and elimination of redundant processing.
Improved data reliability
Via automated validation, observability, and self-healing pipelines that catch issues before they reach consumers.
Faster data availability
Reducing time-to-insight from weeks to hours for operational and analytical use cases.
AI-ready architecture
Governed, accessible, high-quality data foundations designed for machine learning and AI workloads.
Engagements
How we work with enterprises
Data Platform Modernisation
Modernise legacy data platforms into scalable, cloud-native architectures that reduce cost, improve reliability, and support modern analytics and AI workloads.
Data Engineering & Pipelines
Build production-grade, observable data pipelines that deliver trusted, consistent data across the organisation. Designed for maintainability, not just functionality.
AI Data Foundations
Prepare enterprise data systems for real AI adoption by improving data quality, governance, and accessibility for AI workloads. Foundations first — then AI delivers.
Our approach
From fragmented systems to AI-ready data products
We don't build pipelines in isolation. We architect governed data products that serve the entire organisation — from operational reporting to AI.
Fragmented Systems
Siloed data, manual processes, inconsistent definitions. The typical starting point.
Trusted Data Products
Governed, reliable, documented data products with quality contracts and observability.
AI & Decision Layer
Machine learning, analytics, and AI capabilities built on foundations that actually support them.
Underpinned by data governance, quality frameworks, and platform engineering at every layer.
Process
Diagnose. Architect. Engineer. Deliver.
Every engagement follows a structured process designed to reduce risk and deliver working systems — not slide decks.
01
Diagnose
Assess current state, identify root causes, and define success criteria for the engagement.
02
Architect
Design target architecture, data models, and platform decisions grounded in your specific constraints.
03
Engineer
Build production-grade systems with observability, testing, and operational runbooks from day one.
04
Deliver
Hand over working systems with documentation, knowledge transfer, and ongoing support options.
Enterprise environments
Aviation, logistics, financial services — complex, regulated, high-stakes.
Production-grade delivery
Systems built with observability, testing, CI/CD, and operational runbooks.
Cloud-native expertise
AWS, Snowflake, Databricks, dbt — modern tooling for modern problems.
Independent and vendor-neutral
We recommend what works for your context, not what earns us a referral fee.
Platforms & Technologies
Built with modern, production-grade tooling
Proof of delivery
Example Engagements
Representative projects demonstrating the type of work we deliver.
Enterprise Data Platform Assessment
AWS + Snowflake
Challenges
- — High pipeline failure rates
- — Poor data quality across reporting
- — Excessive cloud spend with no visibility
Outcomes
- 40% reduction in processing costs
- Pipeline reliability improved from 72% to 99.1%
- Delivered prioritised modernisation roadmap
Data Pipeline Modernisation
AWS + dbt + Airflow
Challenges
- — Legacy ETL scripts with no tests or monitoring
- — Data arriving late or inconsistently
- — Manual intervention required daily
Outcomes
- Fully automated, observable pipeline architecture
- Data freshness reduced from 24hrs to 45 minutes
- Zero manual interventions post-delivery
AI Data Foundations Programme
Snowflake + Python + Terraform
Challenges
- — AI initiatives blocked by ungoverned data
- — No single source of truth for ML features
- — Data science team spending 80% time on data wrangling
Outcomes
- Governed feature store serving 12 ML models
- Data scientist productivity improved significantly
- Copilot deployment successful on trusted data
Cloud Cost Optimisation
AWS + Snowflake
Challenges
- — Snowflake costs growing 30% quarter-on-quarter
- — No warehouse governance or query optimisation
- — Over-provisioned compute across environments
Outcomes
- Annual spend reduced by £180k
- Implemented resource governance framework
- Auto-scaling architecture for variable workloads
FAQ
Frequently Asked Questions
What size organisations do you work with?
We typically work with mid-market and enterprise organisations — usually with 50+ employees and existing data systems that need modernisation, not greenfield startups.
Do you only work with AWS?
We have deep expertise in AWS, Snowflake, and Databricks, but also work with Microsoft Azure and GCP. We recommend what fits your context, not what earns us a referral.
How long does a typical engagement last?
Assessment engagements are typically 2–4 weeks. Engineering engagements range from 6 weeks to 6 months depending on scope and complexity.
Do you provide ongoing support?
Yes. We offer retainer-based support for organisations that need ongoing data engineering capacity or platform management after initial delivery.
What does a Data Platform Assessment involve?
We review your current architecture, data flows, quality issues, cost structure, and AI readiness. The output is a prioritised action plan with clear recommendations.
Can you help with AI readiness if we have no AI experience?
Absolutely. Most AI readiness work is about data foundations — governance, quality, and accessibility. We prepare the ground before any AI tooling is introduced.
Not sure where to start?
Take our Technical Diagnostic
Answer a few questions about your current situation and receive a tailored engagement recommendation. Takes 3–4 minutes.
Start Diagnostic Assessment