Services
End-to-end engineering services, delivered by experts
AI engineering, identity and access management, cloud, DevSecOps, software engineering, API integration, QA automation and FinTech engineering — combined into end-to-end delivery across the full SDLC, or engaged individually where you need specialist depth.
Reference architecture
How our services fit together
Modern platforms are layered: applications and AI agents on top, identity and APIs controlling access, AI and application services on a governed cloud platform, and data underneath — with observability, security and a CI/CD pipeline running through every layer.
Identity & API layer
Identity & Access Management · API & Integration
AI & application services
AI Engineering · Software Engineering
Cloud & data platform
Cloud & Platform Engineering · Modernization
Across every layer
DevSecOps · QA & Test Automation · Support
Logs · Metrics · Traces
Zero Trust · Secrets · Scanning
AI Engineering & Intelligent Automation
Production-grade generative AI, retrieval-augmented applications, AI agents and MLOps — engineered with security, evaluation and governance from day one.
Business problem
AI pilots that never make it safely into production
Most organisations have run AI experiments. Far fewer have shipped AI features that are reliable, secure, cost-controlled and governed. Prototypes stall on data access, hallucination risk, identity and permissions, evaluation, and the question of who is accountable when the model is wrong.
What we deliver
- Generative AI applications and copilots built into your products and workflows
- Retrieval-augmented generation (RAG) over your documents and data, with permission-aware retrieval
- AI agents and tool-calling workflows with human-in-the-loop controls
- LLMOps / MLOps: evaluation suites, prompt and model versioning, monitoring and cost tracking
- AI security and governance: guardrails, data protection, red-teaming and audit logging
- AI-assisted engineering: test generation, code review assistance and developer productivity tooling
Typical technologies
- Azure OpenAI
- Amazon Bedrock
- OpenAI / Anthropic APIs
- LangChain / LangGraph
- Semantic Kernel
- pgvector
- Azure AI Search
- MLflow
- Python
Engagement examples
- Building an internal knowledge assistant over policy and product documentation with role-based retrieval
- Automating document intake and classification with LLM extraction and human review queues
- Introducing an evaluation and monitoring pipeline so AI features can be released with confidence
Identity & Access Management
Customer and workforce identity, SSO, MFA and passwordless, OAuth 2.0 / OIDC, Zero Trust and privileged access — the control plane for every system, user and AI agent.
Business problem
Identity sprawl is now the largest attack surface
Users, partners, APIs, workloads and now AI agents all need access — usually through a patchwork of directories, custom login code and over-privileged service accounts. The result is friction for customers, audit findings, and credential-based breaches that bypass every other control.
What we deliver
- Customer identity (CIAM): sign-up, login, passwordless, social and federated identity at scale
- Workforce IAM: SSO, MFA, conditional access and lifecycle automation (joiner-mover-leaver)
- OAuth 2.0 / OpenID Connect architecture for APIs, mobile apps and partner ecosystems
- Zero Trust access design, least-privilege roles and fine-grained authorisation (RBAC / ABAC / ReBAC)
- Privileged access management, secrets management and workload / machine identity
- Identity for AI agents: scoped, auditable, revocable access for automated and agentic systems
Typical technologies
- Microsoft Entra ID
- Entra External ID
- Okta / Auth0
- Keycloak
- Ping Identity
- OAuth 2.0
- OpenID Connect
- SAML 2.0
- SCIM
- FIDO2 / Passkeys
- OPA
Engagement examples
- Replacing custom login code with a standards-based CIAM platform and passkey support
- Consolidating multiple directories into a single workforce identity with conditional access
- Designing token-based authorisation for a partner API ecosystem and AI agent integrations
Cloud & Platform Engineering
Azure and AWS architecture, Kubernetes platforms, landing zones and infrastructure automation built for reliability and audit.
Business problem
Cloud estates that grew faster than the platform behind them
Many teams moved to the cloud quickly and now carry inconsistent environments, manual provisioning, unclear network boundaries and rising spend. Product teams wait on infrastructure, and reliability depends on a few individuals who know how everything is wired.
What we deliver
- Cloud landing zones with identity, networking, policy and cost guardrails
- Kubernetes platform design, hardening and operational runbooks
- Infrastructure as Code modules with review and promotion workflows
- Environment strategy for dev, test, staging and production
- Reliability engineering: autoscaling, backup, disaster recovery and SLOs
- Cost visibility, tagging standards and right-sizing recommendations
Typical technologies
- Azure
- AWS
- Kubernetes (AKS / EKS)
- Terraform
- Bicep
- Helm
- Docker
- Azure Monitor
- CloudWatch
Engagement examples
- Designing an Azure landing zone for a regulated workload with private networking and policy-as-code
- Migrating containerised services from VMs to a managed Kubernetes platform with GitOps deployment
- Codifying an existing hand-built AWS environment into reviewed Terraform modules
DevSecOps & Security Engineering
CI/CD pipelines, GitHub Actions, security automation, vulnerability management and infrastructure as code with controls built in.
Business problem
Security reviews that arrive too late to change anything
When security checks happen after the code is written, findings pile up, releases slip, and teams learn to work around controls. Auditors ask for evidence that is hard to produce, and pipelines differ from team to team.
What we deliver
- Standardised CI/CD pipelines with reusable workflow templates
- SAST, dependency, secret and container image scanning in the pipeline
- Policy-as-code and infrastructure scanning before deployment
- Secrets management and workload identity patterns
- Vulnerability triage process with ownership and SLAs
- Release evidence and audit trails suitable for compliance reviews
Typical technologies
- GitHub Actions
- Azure DevOps
- GitHub Advanced Security
- Trivy
- OWASP ZAP
- SonarQube
- Key Vault
- OPA / Conftest
Engagement examples
- Consolidating a dozen bespoke pipelines into a governed template library
- Introducing dependency and secret scanning with a triage workflow that teams adopt
- Producing release evidence packs to support SOC 2 or internal audit requirements
Software Engineering
Backend, frontend and microservices engineering for enterprise applications, delivered with review discipline and test coverage.
Business problem
A roadmap that outpaces the team available to build it
Product commitments keep growing while local hiring is slow and expensive. Adding contractors one at a time spreads senior attention thin and rarely produces predictable delivery.
What we deliver
- Backend services and APIs in Java, .NET, Node.js or Python
- Web front ends in React or Angular with accessible, tested components
- Microservice decomposition where it reduces coupling, not by default
- Code review, branching and definition-of-done standards
- Automated unit, integration and contract tests
- Technical documentation and architecture decision records
Typical technologies
- Java / Spring Boot
- .NET
- Node.js / TypeScript
- Python
- React
- Angular
- PostgreSQL
- Redis
Engagement examples
- Delivering a self-contained product module end-to-end alongside an in-house team
- Building customer-facing portals backed by secure service APIs
- Taking ownership of a backlog area so internal engineers can focus on core platform work
API & Integration Engineering
REST APIs, event-driven architecture, payment and banking integrations and middleware that connect systems reliably.
Business problem
Point-to-point integrations nobody wants to touch
Over time, systems get connected through scripts, file drops and undocumented endpoints. Each new partner or channel takes months, and a change in one system quietly breaks another.
What we deliver
- API design standards, OpenAPI specifications and versioning strategy
- API gateway configuration, throttling and OAuth 2.0 / OIDC security
- Event-driven integration using Kafka or cloud messaging services
- Payment, banking and third-party platform integrations
- Idempotency, retry and reconciliation patterns for critical flows
- Integration monitoring, alerting and partner onboarding guides
Typical technologies
- REST / OpenAPI
- Kafka
- Azure Service Bus
- Azure API Management
- AWS API Gateway
- OAuth 2.0
- OpenID Connect
- Webhooks
Engagement examples
- Replacing batch file transfers with an event-driven integration and reconciliation reporting
- Exposing core-system capabilities through a secured, documented partner API
- Integrating a payment processor with idempotent transaction handling
QA & Test Automation
Automation frameworks, API testing, performance testing and regression automation that shorten release cycles.
Business problem
Manual regression that turns every release into an event
When regression testing is manual, release frequency is capped by how many people can click through test scripts. Defects escape to production and confidence in each deployment stays low.
What we deliver
- Test strategy aligned to risk, covering unit, API, UI and performance layers
- Maintainable automation frameworks integrated with CI/CD
- API and contract testing for services and integrations
- Performance and load testing with baseline and trend reporting
- Regression suites prioritised by business-critical journeys
- Quality gates and test reporting visible to the whole team
Typical technologies
- Playwright
- Cypress
- Selenium
- REST Assured
- Postman / Newman
- k6
- JMeter
- Pact
Engagement examples
- Automating a manual regression pack so releases move from monthly to on-demand
- Adding API contract tests between teams that deploy independently
- Running performance baselines ahead of a high-volume product launch
FinTech Engineering
Digital banking, payments, identity, onboarding, transaction processing and financial integrations engineered for regulated environments.
Business problem
Financial products where correctness and security are non-negotiable
Financial platforms combine strict security expectations, regulatory scrutiny and integrations with legacy cores and external networks. Generalist teams often underestimate the engineering rigour that transaction flows, identity and audit require.
What we deliver
- Digital banking and customer-facing financial application engineering
- Payment flows, transaction processing and reconciliation
- Customer onboarding and KYC workflow integration
- Identity, authentication and fine-grained authorisation
- Core banking and financial data integrations
- Audit logging, data protection and security controls by design
Typical technologies
- Java
- .NET
- Kafka
- PostgreSQL
- OAuth 2.0 / OIDC
- FAPI patterns
- ISO 20022 concepts
- Azure / AWS
Engagement examples
- Engineering a digital onboarding journey integrated with identity verification services
- Building transaction-processing services with idempotency and full audit trails
- Modernising a banking integration layer behind well-defined APIs
Application Modernization
Incremental modernization of legacy applications — re-platforming, refactoring and decomposition without a risky big-bang rewrite.
Business problem
Legacy systems that are expensive to change and risky to replace
Critical applications built on ageing frameworks slow every change, are hard to staff, and carry security exposure. A full rewrite is risky and long; doing nothing keeps compounding technical debt.
What we deliver
- Application portfolio assessment and modernization roadmap
- Strangler-pattern decomposition with clear migration milestones
- Framework and runtime upgrades with regression safety nets
- Re-platforming to containers or managed cloud services
- Data migration planning and validation
- Decommissioning plans so legacy components are actually retired
Typical technologies
- .NET Framework → .NET
- Java EE → Spring Boot
- Containers
- Azure App Service
- AWS ECS
- Kubernetes
- PostgreSQL
Engagement examples
- Upgrading a business-critical .NET Framework application to modern .NET in phases
- Carving high-change capabilities out of a monolith behind new APIs
- Moving on-premises workloads to managed cloud services with minimal downtime
Managed Application Support
Ongoing support, maintenance and continuous improvement for business applications, with defined service levels and reporting.
Business problem
Senior engineers pulled away from roadmap work to keep the lights on
Support tickets, patching and minor enhancements consume time that should go to new capability. Knowledge sits with a few people, and response quality varies.
What we deliver
- Defined support scope, service levels and escalation paths
- Incident triage, root-cause analysis and problem management
- Security patching and dependency upgrades on a regular cadence
- Minor enhancements and backlog maintenance
- Monitoring, alerting and runbook upkeep
- Monthly service reporting and improvement recommendations
Typical technologies
- Jira Service Management
- ServiceNow
- Azure Monitor
- Datadog
- Grafana
- PagerDuty
- Opsgenie
Engagement examples
- Taking over level 2/3 support for a portfolio of internal business applications
- Running a monthly patching and dependency-upgrade programme with change reporting
- Reducing recurring incidents through problem management and automation
Not sure where to start?
A short conversation with our experts is usually enough to identify where an engineering pod or an assessment would make the biggest difference.