Pillar 01
Artificial Intelligence & Automation
We move organisations from AI ambition to production systems that carry measurable operational load — with the evaluation, guardrails and human oversight that make them defensible to a risk committee.
Service lines in this discipline
AI Readiness Assessment & Enterprise AI Strategy
A structured assessment of where artificial intelligence will repay investment in your organisation, and where it will not. We examine data estate, process maturity, regulatory exposure and internal capability, and return a sequenced roadmap with costed options rather than a list of possibilities.
- Python
- dbt
- Snowflake
- BigQuery
- Azure AI Foundry
Agentic AI & Autonomous Agent Architectures
We design and build agent systems that take real actions inside your estate — and we build the containment around them first. Tool boundaries, permission scoping, human approval gates and full action audit trails are part of the architecture, not a later hardening pass.
- Foundation model APIs
- Model Context Protocol
- LangGraph
- Temporal
- Redis
LLM Fine-Tuning & Private Model Deployment
Where a general model is insufficient or your data cannot leave your boundary, we fine-tune and deploy models inside infrastructure you control. We are equally willing to tell you that retrieval augmentation would meet the requirement at a fraction of the cost.
- PyTorch
- Hugging Face
- vLLM
- Amazon SageMaker
- Azure ML
Enterprise Copilots & Conversational Assistants
Assistants grounded in your own systems of record, scoped to what each user is permitted to see. We treat permission inheritance as the hard problem it is: a copilot that answers from documents a user could not otherwise open is a data breach with a friendly interface.
- Foundation model APIs
- Azure AI Search
- pgvector
- Elasticsearch
- .NET
AI-Driven Workflow Automation & Process Optimisation
We instrument the process before we automate it. Automating a workflow nobody has measured usually encodes the inefficiency permanently, so our engagements begin with observation and end with a system whose throughput is reported against the baseline we established.
- Python
- Temporal
- Camunda
- Azure Document Intelligence
- PostgreSQL
Adjacent disciplines
Most programmes draw on more than one.
Cloud & DevOps
Cloud estates that are auditable, reproducible and cost-governed.
Pillar 03Enterprise Software
Multi-tenant products and platforms built to survive their own success.
Pillar 04FinTech & Payments
The money-movement layer: rails, reconciliation and audit-ready scope.
Pillar 05Frameworks & API
Modernisation of systems you depend on, exposed through hardened APIs.
Pillar 06Web3 & Blockchain
Distributed ledger applied where it outperforms a database — and only there.
Request for Proposal
Begin with a scoped conversation, not a sales call.
Our intake is structured so that the first response you receive is technical. Tell us which of the following describes your position and we will route your enquiry to the engineering lead who owns that practice.
Enquiries are acknowledged within one business day and answered substantively within two business days. All submissions are treated as confidential; a mutual non-disclosure agreement is available before disclosure of scope.