Platforms that hold at scale.
AI is only as good as the systems underneath it. We design and build the backends, data platforms and infrastructure that high-volume businesses run on: multi-tenant SaaS, payments, event streaming and analytics, engineered for failure from the start.
Build
What we build
Cloud-native backends
Services on Kubernetes or managed platforms, designed for horizontal scale and graceful failure.
Event-driven architecture
Kafka-based streaming, event sourcing and change data capture for systems that react in real time.
Multi-tenant SaaS
Tenant isolation, per-tenant configuration, metering and billing built into the platform.
Payments and monetisation
Ledgers, payment orchestration, subscriptions and reconciliation, with idempotency and audit trails throughout.
AI-ready data platforms
Pipelines, warehouses and real-time analytics stores with the governance and lineage AI work depends on.
DevOps and reliability
Infrastructure as code, CI/CD, observability and SLOs, with on-call runbooks your team can use.
How it runs
From first week to handover.
Durations are typical. We agree the actual plan with you during scoping.
Architecture review
1 to 2 weeksLoad, failure modes, data flows and cost reviewed against where the business is going. You get a prioritized plan.
Foundations
3 to 6 weeksThe pieces everything else depends on: environments, pipelines, observability and the core services.
Build out
2 to 6 monthsFeatures and migrations delivered in increments, each with load tests and a rollback path.
Operate
OngoingWe hand over with runbooks and SLOs, or keep running and improving the platform with your team.
AI in the loop
Where AI changes the work
Architecture analysis at speed
AI-assisted review of code, configuration and traces surfaces bottlenecks and risky dependencies early.
Generated infrastructure, reviewed
Agents draft Terraform, pipelines and test harnesses; engineers review every change before it applies.
Smarter operations
Anomaly detection and AI-assisted incident triage point on-call engineers at likely causes.
Data built for models
Lineage, quality checks and access controls that let you put data in front of AI safely.
What you own at the end
- Architecture documented with decision records
- Infrastructure as code in your repositories
- Dashboards, alerts and SLOs
- Load and failure test results
- Runbooks and on-call guides
Ways to engage
Readiness sprint
A defined question answered: an assessment, an architecture review or a scored use-case portfolio.
Forward-deployed pod
A small senior team embedded in your business that owns an outcome end to end.
Team extension
Senior engineers who join your team, work to your plan and report to your leads.
FAQ
Questions we hear.
Which clouds and stacks do you work with?
AWS, Azure and GCP; Java and Spring Boot, Node.js, Go and Python; Kafka, PostgreSQL, ClickHouse, Redis and the major warehouses. We work in your stack before proposing a new one.
Can you take over an existing platform?
Yes. We start with a review and a short shadowing period, then take on changes and operations in stages.
Do you do cost optimization?
Yes. Architecture reviews usually find savings in compute, storage and data transfer, and we size them before recommending changes.
Can you work alongside our platform team?
That is the usual setup. We agree ownership by service or capability so responsibilities stay clear.
Related
Often paired with.
Transform
AI Modernization of Legacy Workflows
Digitize paper, email and spreadsheet processes and modernize the legacy systems behind them, using AI to read old code, recover business rules and automate the work.
Extend
Staff Augmentation
Senior engineers who join your team, your tools and your rituals, with the AI-assisted habits that make them productive from the first sprint.
What are we building?
Tell us about the problem. An engineer, not a sales team, will reply.