Understand the environment
Architecture, data, permissions, existing systems and technical constraints come first.
Move AI from pilot to production without creating another layer of complexity.
We work with enterprise teams to design, build and scale AI systems within existing architecture, security standards and governance requirements.
45 minutes. We’ll look at one system that needs to move beyond the pilot.
Enterprise delivery
EU data options
Security from the start
Production ready
01 · Where enterprise AI gets difficult
The model is rarely the hardest part.
Pilots prove that the intelligence works. Production has to make it reliable inside your systems, policies and budget.
01
Integrating with existing systems and data
Context lives in ERP, CRM, document stores and data platforms that were never designed to feed a model.
02
Controlling what AI can access and do
Permissions, review and escalation have to hold at the level of each action, not just at login.
03
Keeping performance and cost predictable
Usage grows, models change, and the cost profile can shift significantly as a pilot moves into production.
02 · How we build
A clear path from pilot to production.
Design the intelligence layer
Choose models, context, decision logic and boundaries around the job the system needs to do.
Put it into real use
Build against real data and workflows, with evaluation and review designed into the system.
Productionise the system
Infrastructure, monitoring, security, deployment and ownership are made repeatable.
Scale what proves useful
Extend only where usage and evaluation show that the system is worth expanding.
03 · Performance and cost at scale
AI cost is an architecture decision.
Model choice, token use, routing, infrastructure and evaluation all affect what an AI system costs to run. We design these together rather than treating cost optimisation as something to fix after launch.
- Infrastructure as code
- Repeatable environments, deployments and configuration.
- Model routing
- Use different models where their capability and cost make sense.
- Token efficiency
- Reduce unnecessary context, repeated calls and oversized model usage.
- Evaluation & monitoring
- Track quality, behaviour and cost as the system changes.
- Cost visibility
- Make usage and model spend visible enough to manage.
What it adds up to
Predictable running cost, a model mix you can explain, and infrastructure your own team can rebuild.
Control surface·Last 30 daysWithin budget
UsageRequests per day
Model mix62 / 31 / 7
SmallMidLarge
RoutingBy task
Classificationsmall
Summariesmid
Complex reasoninglarge
EvaluationStable
Quality within range
Last run todayBehaviour unchanged
Spend vs budgetDay 18 of 30
On trackPer model, per team
Environments as codeAlerts on driftSpend by model
04 · Governed from pilot to scale
Governance that survives production.
The system needs clear limits, traceable decisions and operational ownership once real users and real data are involved.
Action level permissions
Define what the system may read, recommend, change or trigger, with escalation around higher impact actions.
Traceability and evaluation
Keep important inputs, outputs, system actions and evaluation records available for review over time.
Operational ownership
Make responsibility explicit across the people who operate, review and maintain the system.
GDPREU AI ActEU data optionsAccess controlAuditability
Marketplace Asset Platform2 brands · 100 SKUs · production system
Agentic Localisation System10 languages · 11 European markets · brand specific intelligence
Start with one system that needs to move beyond the pilot.
Start here
One system, beyond the pilot.
Bring the system that has proved itself in a pilot and needs to run inside your architecture, security standards and budget. We’ll map what production requires and where to start.