Services

How we can work together

Three engagement models and six service modules. Start small with a sprint, or bring us in to build and run.

Engagement model

How we work

A simple engagement model that keeps things clear and keeps momentum.

012–3 weeks

Blueprint Sprint

We map the problem together: blueprint, plan, risks and estimates.

026–10+ weeks

Delivery Build

Iterative delivery of the platform, pipelines and models, with working software every few weeks.

03Monthly

Operate & Improve

Reliability, performance, cost, and enabling your team to run it themselves.

Service modules

Combine the modules you need. Each one is delivered by the same senior team, with the same production standards.

01

Cloud-Native Telecom Engineering

Kubernetes platform patterns for operator workloads: security controls, observability, CI/CD.

  • Container orchestration and microservice architecture
  • Infrastructure as Code and GitOps workflows
  • Security policies, network policies, and RBAC
  • Monitoring, alerting, and distributed tracing
  • Automated build, test, and deployment pipelines
02

AI/ML Engineering & MLOps

Reproducible training/evaluation, deployment patterns, monitoring and governance.

  • Feature engineering and model training pipelines
  • Experiment tracking and model versioning
  • Automated model deployment and A/B testing
  • Performance monitoring and drift detection
  • Governance, auditability, and lineage tracking
03

Connectivity Analytics

Measurement pipelines, spatiotemporal feature engineering, connectivity KPIs and predictive connectivity.

  • Network measurement ingestion and processing
  • Spatiotemporal feature engineering
  • Connectivity quality scoring and prediction
  • Coverage analysis and planning tools
  • Real-time monitoring dashboards and APIs
  • UAV corridor connectivity as an extension of the same models
04

Data Engineering & Data Management

Streaming/batch pipelines, quality gates, metadata and governance alignment.

  • Real-time and batch data pipeline design
  • Data quality validation and monitoring
  • Metadata management and data cataloging
  • Schema evolution and versioning
  • Data governance and compliance alignment
05

Big Data Platforms

Lakehouse/query engines, performance tuning, cost controls.

  • Lakehouse architecture design and implementation
  • Query engine selection and optimisation
  • Storage layout, partitioning, and compaction
  • Cost monitoring and resource optimisation
  • Multi-tenant data access patterns
06

AI Systems Observability & Intelligence

Understanding, monitoring, and improving AI systems in production.

  • Model and data performance tracking across time and conditions
  • Comparative analysis of AI pipeline variants and configurations
  • Failure analysis and debugging workflows
  • Interactive visualisation of predictions, features, and system behaviour
  • LLM evaluation including prompts, outputs, and RAG pipelines

Let's scope your project

Start with a Blueprint Sprint and ship in weeks.

Book a Call