Services
Data and AI consultancy services
Advise, architect, build, deploy, govern. Engagements are led hands-on by a senior practitioner — you will speak to the person who does the work.
six services, one delivery spine.
Most engagements begin at one point on it and move along it. That is the point: advice you cannot build is just an opinion, and a build without governance is a liability with good uptime.
Data & AI Strategy
A defensible target architecture, a costed roadmap and a clear sequence — so the next twelve months of investment survive contact with reality.
Read more about Data & AI Strategy
Cloud Data Platforms
Lakehouse and warehouse platforms designed for the workloads you actually have — and for the people who will run it after we leave.
Read more about Cloud Data Platforms
Agentic AI Workflows
Agentic workflows with explicit tool boundaries, human review points and audit trails — integrated into the systems where the work actually happens.
Read more about Agentic AI Workflows
AI Adoption Services
Working AI is not adopted AI. We design the controls, the enablement and the measurement that turn a deployed capability into a used one.
Read more about AI Adoption Services
Data Engineering & Integration
Batch, streaming and event-driven pipelines that hold up in production — with the data contracts, quality checks and observability that keep them holding up.
Read more about Data Engineering & Integration
Build & Deployment of Client Services
Cloud-native services designed, built and deployed to a production standard — with the infrastructure as code, pipelines and engineering practice that make the next release boring.
Read more about Build & Deployment of Client Services
How we help
Five verbs, and we mean all five.
Most consultancies can credibly own one or two of these. Being able to take a problem from a whiteboard through to a governed, running service is the whole proposition.
- Advise
- An independent read on direction, architecture and sequencing — with the trade-offs named and the rejected options written down.
- Architect
- Target-state design, HLD and LLD, data models and platform patterns your team can apply without us in the room.
- Build
- Hands-on delivery of platforms, pipelines, AI workflows and services — to a production standard, not a demonstration standard.
- Deploy
- Infrastructure as code, CI/CD and observability, so releases become routine rather than events with a rollback plan nobody has tested.
- Govern
- Access, lineage, audit, quality and cost — enforced in the system rather than described in a document.
Platforms and technologies
The platforms we build on.
We hold no reseller agreements and no partner quotas, and take no margin on the platform you choose. Recommendations are made on the merits — including when the merits point at something we would rather not use.
- Cloud
Architecture, networking and infrastructure across the major providers.
- AWS
- Azure
- GCP
- Data platforms
Lakehouse and warehouse platforms, modelled for the workloads you actually run.
- Databricks
- Snowflake
- Redshift
- BigQuery
- PostgreSQL
- Apache Iceberg
- AI
Retrieval, agentic workflows and assistants, designed to be governed.
- Claude
- Amazon Bedrock
- RAG & embeddings
- pgvector
- Pipelines & streaming
Batch, streaming and event-driven movement of data, with quality checks that fire.
- Apache Airflow
- DBT
- PySpark
- Apache Kafka
- Kinesis
- AWS Glue
- Delivery
Infrastructure as code and pipelines that make the next release boring.
- Terraform
- CloudFormation
- Docker
- Kubernetes
- GitHub Actions
- GitLab CI
Engagement models
How engagements are usually shaped.
So you know roughly what you are asking for before the first call — and so we can both tell quickly if this is not a fit.
Advisory sprint
2–4 weeksA focused engagement on a decision you are stuck on — platform selection, an architecture you inherited, or an initiative that has stopped moving and nobody can say why.
You end up with. A costed recommendation and a written decision log.
Architecture assessment
3–6 weeksAn independent review of an existing platform, pipeline estate or AI system: what it does well, where it will break, what it will cost, and what to do about it.
You end up with. A findings report, a risk register and a prioritised remediation plan.
Proof of concept
4–8 weeksA narrow, honest test of a specific technical question — a RAG pattern, a streaming approach, a platform migration path — built to production standards so the result actually means something.
You end up with. A working proof, an evaluation against the original question, and a clear recommendation.
Production build
Project-basedHands-on delivery, alone or embedded alongside your engineers, taking a design through to a governed service running in production.
You end up with. A deployed, governed, documented service — and a team who can run it.
Fractional architecture leadership
Ongoing, part-timeSenior technical leadership for organisations that need the judgement and the accountability without the permanent headcount.
You end up with. Architecture ownership, standards, technical governance and honest counsel in the room.
Team enablement and training
Programme-basedHands-on mentoring and role-based enablement on production-ready pipelines, APIs, event-driven architecture, CI/CD and AI workflows — built around your systems, not a generic curriculum.
You end up with. A team that can build and operate this without us, and the standards to keep doing so.
Not sure which of these you need?
That is a perfectly normal place to start. Describe the problem and we will tell you honestly which service fits — or whether none of them do.
30 minutes, no pitch deck.