hb.dev

Services

Engineering that works
after we leave.

We embed with your team to build AI systems, data platforms, and engineering capabilities your organisation can run and evolve independently. Every engagement starts with your constraints and ends with outcomes that last.

Most organisations have run AI pilots. Far fewer have AI reliably creating value in production. We bridge that gap, working with your teams to identify the right use cases, build responsibly, and operate with confidence.

Use case discovery and business case validation
Model selection: classical ML, deep learning, LLMs
RAG pipelines, prompt engineering, and guardrails
Computer vision (object detection, classification, segmentation)
Privacy by design, data governance, and audit trails
Production deployment, monitoring, and drift detection
EU AI Act compliance and responsible AI frameworks

AI is only as good as the data behind it. We design and build data platforms reliable enough for production ML, with the observability, testing, and access controls that teams actually need.

Modern data stack design and implementation
Feature stores, experiment tracking, and model registries
Automated pipeline testing and data quality checks
Real-time and batch data platform engineering
Geospatial data processing pipelines
Data contracts and ownership across teams
Cost visibility and query performance optimisation

The best platform is one your engineers actually use. We design internal developer platforms that reduce cognitive load, enforce sensible defaults, and remove bottlenecks - so your team ships faster with fewer surprises.

Internal developer platform design and implementation
CI/CD pipelines, environment parity, and deployment automation
Container orchestration with Docker and Kubernetes
Infrastructure as code and self-service provisioning
Service boundaries, API design, and event-driven architecture
Observability: metrics, logs, distributed tracing, and SLOs

Reliability is a product decision, not just an ops concern. We help engineering and product teams agree on what good looks like, measure it honestly, and put the right practices in place to protect it.

SLI and SLO definition with error budget policies
Incident management and structured post-incident learning
On-call process design and runbook development
Progressive delivery and automated rollback
Capacity planning and availability modelling

Cloud spend tends to grow faster than the value it delivers. We give you a clear picture of where money is going, eliminate waste, and put governance in place so costs stay predictable as you scale.

Cloud cost analysis and waste identification
Workload right-sizing and autoscaling strategy
Storage tiering and network egress optimisation
FinOps practices, tagging strategy, and cost allocation
Multi-cloud and reserved capacity planning

Security in the EU regulatory landscape is no longer optional, but it should not slow teams down. We help you build secure systems from the start and prepare for frameworks like the EU AI Act, GDPR, and NIS2.

Threat modelling and secure architecture reviews
Secrets management, access controls, and key rotation
GDPR data protection impact assessments and retention policies
NIS2 and EU AI Act readiness assessments
Security testing integration in CI/CD pipelines

Technical foundation

Our core stack is Python-based scientific computing, real-time data platform engineering, and containerised deployment with Docker and Kubernetes. We bring deep experience in geospatial data processing, MLOps infrastructure, and production ML systems.

PythonPyTorchTensorFlowDockerKubernetesTerraformFastAPIPostgreSQLApache KafkaTensorRT

How we engage

We embed with your team rather than handing over a document and leaving. Engagements are scoped to deliver a clear outcome - whether that is a working system, an operating model, or a team that can carry the work forward independently.

1
Discovery
A short sprint to align on goals, constraints, and what success looks like. We can usually tell within an hour whether we are the right fit.
2
Embedded delivery
We work alongside your engineers, not in a silo. Code reviews, architecture decisions, and debugging happen together.
3
Knowledge transfer
Capability building throughout, not a handoff at the end. Documentation your team will actually use.
4
Independent operation
The engagement ends when your team can run and evolve the system without us. That is the goal from day one.

Not sure where to start?

Most engagements begin with a short discovery conversation. No commitment, no sales pitch.

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