Capabilities

The engineering behind the system.

We connect product requirements with the backend, data, integrations, and infrastructure needed to make the system work. These are the four areas we work in, each connected to work we have done.

01

Product and Backend Engineering

Applications, APIs, business rules, and system boundaries.

Most of the difficulty in a business system lives in its rules and its boundaries: what the system is responsible for, what it hands to other systems, and how it behaves when the inputs are messy. We build the applications and APIs around those rules so that they can be changed deliberately later.

This is also where product thinking matters. The backend has to serve how people actually use the software, which means understanding the workflow before deciding the data model.

Typically covers

  • Backend services and APIs
  • Business rules and domain models
  • Web applications and interfaces for several roles
  • System boundaries and ownership of data

Supporting technologies

  • Java
  • Kotlin
  • Go
  • Spring Boot
  • Angular
  • PostgreSQL

Related work: ScanBar, LandGrader, EnzeMeta, EnzeTorque

02

Data Platforms and Algorithmic Work

Domain-specific data, analysis, research tooling, and decision support.

Data-intensive products succeed or fail on whether the data can be trusted and whether the analysis can be explained. We build platforms that bring domain data together, apply analysis to it consistently, and present the result in a way people can act on.

Algorithmic work needs the same discipline. An algorithm that detects a mutation or scores a property has to be reasoned about in terms of the domain, checked against data the experts trust, and kept transparent enough that its output can be questioned.

Typically covers

  • Data platforms for domain-specific data
  • Analysis and scoring applied consistently at scale
  • Research tooling
  • Decision support for expert users

Supporting technologies

  • PostgreSQL
  • Data processing
  • Algorithms

Related work: Bio, LandGrader

03

Integrations and Operational Automation

Connected systems and coordinated business workflows.

Operational software is rarely one system. It is a workflow that crosses a system of record, people in the field, devices, and external services. We build the integrations and the automation that make those parts behave as one coordinated process.

The hard part is never only the API. Real operations have exceptions, retries, reassignments and second attempts, and the workflow has to represent those faithfully while staying consistent with the records it depends on.

Typically covers

  • Integration with systems of record and external services
  • Automated workflows across roles and systems
  • Device and hardware integrations
  • Tracking work through to completion

Supporting technologies

  • Java
  • Kotlin
  • Go
  • Distributed systems

Related work: Property management operations, ScanBar, Moot

04

Production Platforms and Reliability

Deployments, infrastructure, observability, and maintainability.

A system that is not running is not finished. Several of our engineers have practical experience managing production Kubernetes clusters, infrastructure, CI/CD, deployments, monitoring and observability, and they bring that experience into how we build: the deployment and operational documentation are part of the delivery.

We do not treat operations as a separate department. The people who designed the system are the people who know what it needs to run.

Typically covers

  • Deployment pipelines and environments
  • Infrastructure and Kubernetes operations
  • Monitoring, observability and reliability practices
  • Operational documentation and handover

Supporting technologies

  • Kubernetes
  • CI/CD
  • Monitoring and observability
  • Infrastructure as code

Related work: ScanBar, LandGrader

Engagement options

Ways to work together

Three common shapes for an engagement. The right one depends on where your system is today; we agree the scope and terms with you before work starts.

  • Understand and de-risk

    A short, bounded piece of work to understand the constraints, make the important decisions explicit, and give you a basis for deciding what to build and how.

  • Design and build

    Senior engineers design and build the system in working increments, with architecture decisions documented and the deployment prepared alongside the code.

  • Improve and operate

    Work on a system that already exists: improving how it is built, how it runs, and how it is observed, with the documentation to keep it maintainable.

Supporting detail

Team experience by technology

This describes the experience across the team. It is not a list of technologies used on every project; case studies name a technology only where its use on that project is confirmed.

Core engineering

  • Java
  • Kotlin
  • Go
  • Spring Boot
  • Angular
  • PostgreSQL
  • Distributed systems

Production platforms

  • Kubernetes
  • Infrastructure
  • CI/CD
  • Deployments
  • Monitoring and observability
  • Reliability

Systems and blockchain

  • C++
  • Rust
  • Blockchain systems

Further experience

  • Algorithms
  • Data processing
  • Complete product development

What is difficult about the system you are building?

You do not need a complete specification to start the conversation.

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