Engineering that outlasts the roadmap it was built for.
We design, build and modernize the software, platforms and infrastructure that carry the business — engineered for change, evaluated on operational reality.
Executive summary
Every meaningful transformation eventually becomes an engineering problem. Systems have to be built, integrated, modernized, secured and operated — often across a landscape that is part legacy, part cloud-native, part vendor-supplied.
Laminin's Technology & Engineering practice runs the full arc: platform strategy, architecture, hands-on engineering, integration, and the operational discipline that keeps systems reliable at scale. We work with CTOs, CIOs and engineering leaders on the systems that carry the business.
We favor evolutionary architectures, disciplined boundaries, and engineering practices that let teams keep changing the system without breaking it.
The problems we address
Legacy systems that block strategic moves
Every new initiative eventually hits the same twenty-year-old core, and the workaround tax compounds.
Cloud migrations that did not deliver the promised economics
Lift-and-shift complete, cost curve rising, and the operating model still on-prem.
A landscape of tightly coupled point-to-point integrations
Every change is expensive, every failure is diffuse, and no one owns the middle.
Engineering teams delivering slower than they should
Talent is strong, throughput is not — usually a platform and practices problem, not a headcount problem.
Cybersecurity posture out of step with exposure
Controls designed for a smaller attack surface than the one the business now presents.
Vendor stacks that no longer fit the strategy
Contracts, coupling and switching costs quietly determining what the business can and cannot do.
What Laminin does
Enterprise and solution architecture
Target architectures, transition plans, domain boundaries and reference designs that survive contact with reality.
Software and platform engineering
Product engineering, internal developer platforms, and the reusable capabilities that lift every team's throughput.
Cloud and infrastructure engineering
Cloud landing zones, infrastructure-as-code, cost engineering, and the FinOps discipline to keep the bill in line.
Systems integration and API architecture
Event-driven and API-led integration, canonical models, and the middle of the stack that decouples the edges.
Legacy modernization
Strangler-based re-platforming, domain extraction, and phased retirement plans that keep the business running.
Cybersecurity and resilience engineering
Threat-informed controls, identity and secrets, secure SDLC, and resilience patterns designed into the architecture.
Twelve engineering disciplines
Composed by engagement, not billed as a menu.
Digital Transformation
End-to-end programs that reset how technology serves the business.
Software Engineering
Product and platform engineering with practices designed for change.
Platform Engineering
Internal developer platforms that compound engineering throughput.
Cloud
Landing zones, cloud-native services, and cost engineering.
Enterprise Architecture
Target states, transition roadmaps, and disciplined domain boundaries.
Systems Integration
API-led and event-driven integration across heterogenous estates.
API Architecture
API strategy, contracts, gateways and lifecycle management.
Automation
Process, workflow and infrastructure automation with clear ownership.
Modernization
Legacy retirement, strangler patterns and phased migrations.
DevOps
CI/CD, observability, SRE practices and paved paths.
Cybersecurity
Threat-informed controls, identity, secure SDLC and resilience.
Emerging Technology
Selective, disciplined bets on technologies with a credible route to production.
Four phases from architecture to operation
Assess
Technology and architecture assessment against strategy — where the constraint really is.
Architect
Target architecture, transition plan and platform choices tied to concrete outcomes.
Engineer
Delivery in production-grade environments with paved paths, observability and rollback from day one.
Operate
Run, evolve and secure the system — the phase that determines the ROI of everything before it.
The engineering stack we work in
Selected per engagement — we are cloud, language and vendor pragmatic.
- CloudAWS, Azure and GCP — landing zones, cloud-native services, hybrid patterns where the workload demands it.
- LanguagesTypeScript / Node, Python, Go, Java / Kotlin and .NET, selected by domain and team fit rather than fashion.
- PlatformsKubernetes, serverless (Lambda, Cloud Run, Functions), managed data services, and internal developer platforms built on top.
- IntegrationAPI gateways, event streaming (Kafka, Kinesis, Pub/Sub), workflow orchestration and canonical data models.
- DevOpsGit-driven CI/CD, infrastructure-as-code (Terraform, Pulumi), progressive delivery, and observability across metrics, logs and traces.
- SecurityIdentity-first architectures, secrets management, secure SDLC, threat modelling and controls mapped to recognized frameworks.
- ReliabilitySRE practices, SLOs, error budgets, chaos and resilience testing designed into normal delivery.
Related industries, work and insights
Relevant industries
Related work
Questions engineering leaders ask us
Do you deliver engineering, or advise on it?
Both. Advisory work is grounded in what we know breaks in delivery, and delivery work is anchored in an architecture we would defend.
How do you approach legacy modernization?
Strangler patterns, domain extraction, and phased retirement — with a value case that closes at each phase, not only at the end. Big-bang rewrites are a last resort.
How do you engage with existing engineering teams?
Frequently as an embedded accelerator — bringing platform, architecture or specific technology depth without displacing ownership.
Which clouds and stacks do you work in?
Pragmatic across AWS, Azure and GCP; language- and framework-agnostic. We optimize for team fit and long-term operability rather than a preferred house stack.
Have an engineering problem that keeps repeating?
If the same constraint is showing up across programs, it's usually an architectural or platform question. Let's look at it together.