GenAI that makes it past the demo.
Anyone can prototype with an LLM in an afternoon. Getting GenAI into production — with your data protected, costs controlled, and outputs you can stand behind — is an infrastructure problem. That's our home turf.
Aimed at real processes, not headlines.
Knowledge assistants (RAG)
Assistants grounded in your documentation, tickets, and internal knowledge — answering from your data, with sources cited, running on Amazon Bedrock.
Process automation
Document processing, classification, extraction, and summarisation woven into your existing workflows — measured by hours saved, not novelty.
GenAI platform foundations
The landing zone for AI in your company: model access governance, private networking, prompt and output logging, and cost guardrails — so teams can experiment safely.
The hard parts of GenAI are the parts we already do.
Security & data privacy
Your data stays in your AWS account. Private endpoints, encryption, IAM boundaries, and no training on your data — designed in from the start, aligned with GDPR and your compliance framework.
Cost control
Token costs scale invisibly until they don't. We apply the same FinOps discipline to GenAI workloads as to everything else — budgets, monitoring, model selection by cost-performance.
Governance
Who can use which model, with which data, logged how? Scaling AI without governance is how pilots become liabilities. We build the guardrails first.
Operations
GenAI in production is still production: monitoring, evaluation, versioning, incident response. It folds into the same 24/7 managed operations as the rest of your AWS.
GenAI that respects your data boundaries.
Not every workload needs data sovereignty — but when yours does, by regulation or by customer demand, we build for it from the start instead of bolting it on later.
Your region, your rules
Bedrock runs in the AWS region you choose — including European regions and, as it rolls out, the AWS European Sovereign Cloud — so inference happens where your regulation says it must.
Private by design
Model access over private endpoints, encryption with your own KMS keys, and no vendor training on your prompts or outputs. Traffic never has to touch the public internet.
Evidence, not promises
Residency, retention, and access decisions documented per workload — mapped to GDPR, ENS, or whichever framework your auditors ask about. We hold our own ENS certification, so we know what they'll ask.
Portable choices
Bedrock exposes many models behind one API, so the model can change without rebuilding the platform — sovereignty today doesn't lock out capability tomorrow.
One use case, proven end to end.
Use case selection
A working session to pick one use case with measurable value — and to say no to the ones that don't justify the effort yet.
Proof of concept
A time-boxed PoC on your data in your AWS account, with success criteria agreed up front. AWS proof-of-concept credits may apply.
Production
If the PoC earns it: hardened, governed, monitored, and handed to your team or operated by ours.
Have a GenAI idea — or pressure to have one?
Bring us the use case. We'll tell you honestly whether it's ready for production, what it would take, and what it would cost to run.
Book your free review →