Specialised engineering partner
We modernise legacy systems, put AI into production, and train the engineers who have to run it afterwards. We work alongside your team, in your codebase — not from a deck.
Typical results across modernisation engagements.
What we do
Enterprise Architecture
Technical direction that reduces architectural debt, aligns capital expenditure with performance requirements, and improves application reliability.
AI & MLOps Architecture
Moving AI from experimentation to production deployment: resilient MLOps pipelines and data-driven agents integrated securely inside corporate networks.
Corporate IT Enablement
Customised, lab-intensive curriculum. Instruction rooted in real deployment work rather than slideware.
The Ajna methodology
Technical audits of existing middleware, cloud expenditure and systemic bottlenecks.
Resilient, decoupled cloud-native architecture on Kubernetes and serverless frameworks.
CI/CD, MLOps and FinOps pipelines that keep operational excellence self-sustaining.
Upskilling internal teams so the capability stays after we leave.
Insights
Neither is universally better. The answer depends on your workload profile, your team's operational maturity, and — honestly — your tolerance for cold starts.
Read itChaos engineering injects controlled failures to validate that systems actually behave as designed under real-world failure conditions. The gap between the architecture diagram and production behavior is where reliability lives.
Elastic infrastructure didn't kill capacity planning — it changed what gets planned. Demand modeling, the utilization-vs-headroom trade, quota and constraint archaeology, and the quarterly loop that replaces both waste and 3 AM scrambles.
An API that's easy to build against is easy to build wrong. The decisions around versioning, error models, pagination, idempotency, and authentication compound over time — here's how to make the ones that don't cause regret.
Prompt engineering for a demo is different from prompt engineering for a system that runs reliably at scale. Here's how to design prompts for consistency, build evaluation frameworks, and manage the failure modes that appear in production but not in testing.
Both RoCE and iWARP provide RDMA over Ethernet, but they have different network requirements, loss behavior, and operational characteristics. Here's how to choose between them for AI training, HPC, and storage workloads.
The network topology of an HPC or AI training cluster determines its bandwidth, latency, cost, and how well it scales. Here's how fat-tree, torus, and dragonfly topologies work, their trade-offs, and which workloads each suits best.
Industry acclaim
Delivered high-impact architecture solutions and exceptional technical leadership, consistently exceeding enterprise operational targets.
Recognised for driving successful enterprise platform migrations, resolving complex technical challenges, and maintaining rigorous stakeholder confidence.
Known for precise architectural documentation and scenario-based labs, significantly improving our engineering team's systemic understanding.
Feedback from engagement stakeholders. Names withheld under client confidentiality.
Architecture review, migration, AI in production, or training your team to run it. Start with the constraint you actually have.