/ Services
Production agentic AI,
end to end
Four practices, one delivery team for US, UK, and EU buyers. Most engagements start in AI and pull in the others as the product takes shape.
Practices
4
Delivery
One team, end to end
Typical start
Discovery sprint
Engagement
Fixed scope or retained
/ Practices
00
AI Solutions
Agentic systems, machine learning and LLM platforms taken from prototype to production.
Learn more →Agentic AI supervisors and specialist agents
Multi-agent orchestration and MCP tool connectors
Human-in-the-loop approval workflows
LLM applications with retrieval (RAG)
Assistants, copilots and support agents
Document intelligence and extraction
Model fine-tuning and prompt engineering
Evaluation suites, guardrails and red-teaming
Forecasting, ranking and recommendation models
Computer vision and geospatial ML
Data pipelines, feature stores and labelling
MLOps: deployment, monitoring and retraining
AI strategy, feasibility and roadmapping
01
Software Development
Web, mobile and API engineering with the cloud infrastructure to run it.
Learn more →Web application development (React / Next.js)
SaaS and multi-tenant platforms
iOS and Android apps
Backend services and microservices
REST, GraphQL and event-driven APIs
Third-party and enterprise system integrations
Data engineering and warehousing
Cloud architecture on AWS, GCP and Azure
DevOps, containers and CI/CD
Legacy modernisation and migration
Automated testing and QA
Observability, security and compliance
Support, SLAs and managed operations
02
Digital Marketing
Growth strategy, performance media and automation measured against pipeline.
Learn more →Growth and demand strategy
Performance media and paid search
SEO and content programmes
Lifecycle and marketing automation
Analytics, attribution and reporting
AI-assisted campaign operations
Product UX and UI design
Design systems and component libraries
Interaction and motion design
Brand identity and visual language
Prototyping and usability testing
Accessibility and design QA
/ How we engage
011–2 weeks
Discovery
We map the workflow, the data and the decisions a system would have to make. Output is a scoped plan with a build estimate.
021–2 weeks
Architecture
Agent topology, model choices, evaluation criteria and the integration surface. Reviewed with your engineering leads before build.
036–12 weeks
Build
Two-week increments, deployed to a staging environment you can use from the first cycle. Evaluation runs on every change.
04Ongoing
Run
Monitoring, retraining and iteration once the system carries live volume. Handover or retained support, your call.
/ Capabilities
Agentic & LLM
MCP tool connectors
Supervisor / sub-agent orchestration
Retrieval pipelines
Eval harnesses
Guardrails
Machine learning
Forecasting
Ranking
Computer vision
Feature pipelines
Drift monitoring
Application
React / TypeScript
Node & Python services
iOS / Android
REST & event APIs
Platform
AWS / GCP / Azure
Containers & CI/CD
Observability
Data warehousing
/ Engagement models
Discovery sprint
Fixed fee
A scoped assessment of one workflow, ending in an architecture and a build estimate.
Workflow and data audit
Agent or model architecture
Build plan with estimate
Build engagement
Fixed scope
A defined system delivered in two-week increments against agreed acceptance criteria.
Dedicated delivery team
Staging from cycle one
Evaluation on every release
Retained run
Monthly
We operate and improve what is live: monitoring, retraining and a steady iteration cadence.
Monitoring and on-call
Model retraining
Quarterly roadmap review
Not sure which practice you need?
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