AI Solutions
Agentic AI systems, built in-house.
Alongside the infrastructure practice, our engineers design and build the AI systems that run on it — from a working problem definition through to something that operates reliably in production.
What we build
Capability, not a product line
Agentic systems
Systems that take actions across your tools rather than just answering questions — reading and writing to the applications a task actually touches, with the boundaries and checks that make that safe to run unattended.
Retrieval over private documents
Search and question-answering grounded in a document set that belongs to you — contracts, manuals, tickets, internal wikis — so answers cite your material instead of a model's general training.
Workflow automation
The repetitive parts of a process — triage, extraction, routing, drafting — handed to a system built around the shape of the work, not a generic chatbot bolted onto it.
Evaluation & observability
Test sets, traces and dashboards for an LLM system already in production, so a change to a prompt or a model is judged against evidence rather than a demo that happened to work.
How we approach it
Start from the problem, not the model
A capable model is one input, not the plan. We spend the early part of an engagement narrowing what the system actually needs to do, then build the smallest thing that does it reliably — because a smaller system is also the one that's easier to inspect, evaluate and fix when it's wrong.
The team that builds the models also specifies the machines.
Accelerator choice, memory bandwidth and thermal headroom for a GPU deployment are decided against the workloads we actually run — that's the same practice behind our AI systems, applied to the hardware advice on the infrastructure side.
See GPU & AI-ready systemsTell us what the system needs to do.
Send the workflow, the data it touches, and where it needs to run — we will come back with an approach, not a demo.