Data architecture, lakehouses and real-time pipelines
Data foundations that feed analytics and AI with current, trusted data
AI is only as good as the data beneath it
Most institutions keep their data in silos: ERP tables, core systems, spreadsheets and documents that nobody has joined up. Reports arrive late, numbers disagree and every new project starts by copying data again.
Language models and agents make the gap more costly. They need current data, unstructured documents and clear access rules, delivered fast enough to act on. A slow or fragile database underneath puts all of it at risk.
What you get
Data as it happens
Data that arrives as events happen, not in an overnight batch.
One governed platform
One governed lakehouse that analytics, reporting and AI all read from.
Data ready for AI
Structured and unstructured data prepared for retrieval, vectors and knowledge graphs.
Databases you can recover
Databases that stay fast and available, with recovery proven before it is needed.
What we deliver
ETL, ELT and unstructured AI data pipelines
Pipelines that extract, load and transform data from your systems, and turn documents, emails and scans into text and metadata models can use.
Real-time event streaming
High-velocity ingestion with Apache Kafka and Apache Flink, so orders, payments and sensor readings reach analytics and agents as they happen.
Warehouses and lakehouses
Cloud, hybrid and on-premises data warehouses and lakehouses on Snowflake, Databricks and Apache Iceberg, designed around how your teams query the data.
Vector databases, knowledge graphs and semantic layers
Vector stores, knowledge graphs and a shared semantic layer, so every report and every model uses the same definitions and relationships.
Database design and performance
Relational and document databases designed from how your applications read and write, with slow queries found and fixed through plans, indexes and configuration.
High availability, backup, recovery and migration
Replication and failover sized to your recovery targets, encrypted backups restored on a schedule, and migrations rehearsed with data reconciled before and after.
How it works
Map the sources
We review your systems, databases, data volumes and reports, and agree which questions and models the platform must serve first.
Design the target
We design the lakehouse, the streaming layer, the data models and the recovery targets, and record each decision with its reason.
Build in slices
We bring sources in one domain at a time, with quality checks and lineage, so useful data arrives early.
Migrate and reconcile
We move legacy reports and databases in rehearsed steps, reconciling counts and totals before anything old is switched off.
Run and hand over
We monitor pipelines and databases, prove failover and restores, and run the platform or hand it to your team with runbooks.
Technologies and standards
The modern data foundation runs on Snowflake, Databricks and Apache Iceberg, with Apache Kafka and Flink streaming and permission-aware GraphRAG on top. Row-level and role-level access rules are enforced before any model reads the data.
- Languages
- SQL
- Data and analytics
- Apache Kafka
- Snowflake
- Databricks
- Apache Spark
- Apache Airflow
- dbt
- BigQuery
- Amazon Redshift
- Databases
- pgvector
- Elasticsearch
- Methods and practices
- ETL and ELT pipelines
- Real-time event streaming
- Vector databases
- Knowledge graphs
How to start
Clients with a clear scope go straight to a proposal. Where sources, schemas or volumes are not yet mapped, we start with a data audit under the Blueprint Studio.
What happens next
Pre-consulting session
A free 30-minute online pre-consulting session: we go through your goals, systems and constraints.
Readiness review
In a free 48-hour readiness review, a Principal Architect checks your project against the 6-point Definition of Ready and recommends the track that fits.
Choosing the track
Scope-ready projects receive a binding proposal within 3–5 business days. Concept-stage projects start in the Blueprint Studio. A live system that is failing, undocumented or needs integration starts with a diagnostic of 1–2 weeks. The Blueprint Studio and the diagnostic each have a fixed fee, quoted in the proposal.
Delivery in five stages
Mobilisation, sprints with live demonstrations, integration and hardening, acceptance and go-live, then managed services. After launch comes a warranty period, then optional support and maintenance.
AI-native product teams
Managed, cross-functional pods of senior engineers, equipped with agentic coding and QA harnesses.
Fixed-outcome projects and round-the-clock managed services
Fixed-scope, milestone-driven delivery with named delivery leads, and round-the-clock application managed services under SLA governance.
Questions and answers
Which data platform should we use?
It depends on your data, your skills, where the data must stay and how you will query it. We compare Snowflake, Databricks and an open Apache Iceberg lakehouse against your workloads and residency rules, and recommend the simplest one that fits.
Do we need real-time data?
Only where a decision depends on it, such as fraud checks, stock levels or agent actions. We stream those events with Kafka and Flink, and keep batch loads where daily figures are enough, because batch is cheaper to run.
Can you bring data out of our ERP without slowing it down?
Yes. We read changes through the ERP’s supported interfaces or its database logs rather than heavy queries, and schedule bulk loads outside busy hours. We measure the load on the ERP before and after.
How do you make sure the numbers are right?
Every pipeline has quality checks on volumes, keys, totals and freshness, and failures stop bad data from reaching reports. We reconcile key figures against your source systems with your finance and operations owners.
How much downtime and data loss should we plan for?
That is a business decision, made as two targets. The recovery point objective is how much recent data you could lose. The recovery time objective is how long a system may be down. We design replication, failover and backups to meet them, then test them.
How do you migrate a database without losing data or stopping the business?
We rehearse every migration on a protected copy and reconcile row counts and checksums between source and target. For systems that cannot stop, we keep both in sync and switch over in a short planned window, with a tested rollback ready.
Which kinds of database do you work with?
Relational databases, and document, key-value and search databases, on your own servers or as managed cloud services. At the start we confirm the engines and versions you run, and assign engineers who know them.
Who owns the platform and the pipelines?
You do. The code, data models, pipelines and documentation we produce for you are yours once each milestone is paid, and your data remains yours throughout.
Start with a conversation, not a contract.
Tell us what you need to build, connect or secure. An architect goes through it with you, then recommends where to start.
What happens next
Agreeing a time
Our team emails you to agree a time for the session.
Pre-consulting session
A free 30-minute online pre-consulting session: we go through your goals, systems and constraints.
Readiness review
In a free 48-hour readiness review, a Principal Architect checks your project against the 6-point Definition of Ready and recommends the track that fits.
Choosing the track
Scope-ready projects receive a binding proposal within 3–5 business days. Concept-stage projects start in the Blueprint Studio. A live system that is failing, undocumented or needs integration starts with a diagnostic of 1–2 weeks. The Blueprint Studio and the diagnostic each have a fixed fee, quoted in the proposal.