Capability
Platform & Managed Services
Getting AI into production is one problem. Keeping it there is the other.
What we build and run
Cloud & Infrastructure Engineering
The environments the product runs in, provisioned as code and identical to one another, with deployment automated across more than 100 of them.
- Terraform
- Kubernetes
- AWS · GCP · Hetzner
- EU data residency
MLOps & Model Operations
Serving, versioning and monitoring for models in production, so a drifting model surfaces as an alert rather than as a customer complaint.
- Model serving
- Versioning & rollback
- Drift detection
- Evaluation in production
Deployment & Migration
Getting a system from where it is to where it should run, including the cutover plan and the way back if the cutover goes badly.
- Zero-downtime cutover
- Rollback plans
- Data migration
- Cloud exit
Application Management & Support
We run what we built: someone on the pager, alerts in a channel your engineers are already in, and accountability that does not end at go-live.
- On-call rota
- Agreed response times
- Patching & upgrades
- Restore testing
Observability & Cost Optimization
Logs, traces and spend visible in one place and reviewed as usage grows, so capacity decisions come from measurement rather than from the invoice.
- Metrics · logs · traces
- Alerting
- Capacity planning
- Unit cost per request
The delivery capability behind everything above. We build the product the intelligence lives in, across web, mobile and services, and then we run it: on our own platform, on EU infrastructure, with someone accountable when it breaks.
The problem it addresses
A model that works in a notebook is not a product. Getting intelligence into production is where most AI work quietly stalls: behind an API nobody hardened, inside a mobile app, across environments that were configured by hand and are now slightly different from each other.
What comes after launch is harder, and it is where the current wave of AI-assisted building is failing loudest. Code arrives faster than anyone can review it, demos pass and production does not, and the team that assembled the thing has already left. Someone still has to hold the pager, produce the evidence an auditor asks for, and restore the database at two in the morning.
Most firms answer that by handing the repository back at go-live. We answer it by running the thing we built, on our own platform, for as long as you want us to.
How this is delivered
Team shape
2 to 6 engineers, senior-led, one integrated team across hubs
Common dynamic
From a 4-month build to ongoing product ownership
Engagement model
Managed Delivery or Embedded Partnership
Delivery hub
London · Zagreb · Belgrade
Where a competitor would stay quiet
The trade-offs
We do not take on delivery we cannot staff to a senior standard. A team of contractors assembled for one project will move fast and leave you with a system nobody owns; we would rather scope smaller and hand over something your own engineers can run. Where a project is pure staff augmentation with no technical decisions to make, we are the wrong firm and will say so.
Questions procurement actually asks
Both, and the second rarely works without the first. QED is deep tech first, but the intelligence has to ship inside a real product across web, mobile and services, delivered by one team rather than handed between three.