AI for CTOs: architecture that survives operations
Prototypes run everywhere. The real work starts with integration, cost control, auditability and the question of who gets paged at night.
For CTOs, AI projects are decided on four points: an architecture that keeps models replaceable, clean integration with existing systems and data sources, controlled inference cost and latency, and governance that covers traceability and EU AI Act requirements. Davies Meyer builds these layers into existing stacks rather than beside them.
Our approach: creative engineering
We come from creative production and build it as a system: templates, generative modules, machine-checkable brand rules and interfaces to delivery systems. For the technology side that means versioned artefacts, reproducible runs and clear handover points instead of a toolbox nobody can operate.
The concept stays human
Idea, visual language and tone are created as before. What follows is what gets automated: variants, formats, language versions, delivery.
Brand rules the machine can check
Colour, typography, logo usage, forbidden claims — stored as rules that apply automatically before approval instead of sitting in a PDF guideline.
Production as a chain, not a tool
Template, generative module, validation step, approval and delivery interlock. A single tool without that chain only moves the manual work elsewhere.
Impact measurable at creative level
We evaluate not only the campaign but the individual attribute: hook, composition, colour world, length. That feeds the next production round.
What we take on
Architecture & model independence
A blueprint with a routing layer so switching models stays a configuration change instead of triggering re-engineering.
Integration into the existing stack
Integration with CRM, DAM, CMS and internal data sources — including authentication, permission model and logging.
Cost, latency & operations
Inference cost and response times are measured, budgeted and monitored before a use case goes wide.
Governance & compliance
Roles, approvals, audit trail and EU AI Act disclosure duties — documented instead of verbally agreed.
How the engagement runs
- 01
Assessment
We review your processes, data situation and existing stack, and document where AI realistically creates impact today — and where it does not.
- 02
Prioritisation & business case
Use cases are ranked by effort, impact and risk. Every candidate gets a transparent calculation instead of a gut estimate.
- 03
Pilot in production
We build the first use case all the way into real operations — including system integration, approval flow and measurement points.
- 04
Scaling & enablement
What works gets rolled out and handed over: playbooks, training and governance, so you do not stay dependent on us.
What a CTO is measured on — and how we define it
- Inference cost per transaction
- Cost per completed use-case run rather than per token — including retries, tool calls and context length. Only that makes a budget plannable.
- Latency at percentile
- We measure p95, not the average. For customer-facing touchpoints we define an upper bound and a fallback behaviour up front.
- Quality rate against your own test set
- A fixed set of real cases with expected outcomes. It decides model switches and releases instead of subjective impressions.
- Auditability
- Share of runs with a complete trail of input, model version, prompt version, output and approval. The basis for EU AI Act evidence.
Criteria for architecture and provider
Model replaceability
Is switching providers a configuration change? If model names sit in application code, the answer is no.
Documented data paths
For every use case it must be clear up front which data leaves the system, where it is processed and what is logged.
Clear operational ownership
Who gets paged, which failure modes exist, what is the fallback if a provider fails or a model is deprecated?
Evaluation instead of demo
Before rollout, a reproducible test run against real cases — with the same data that will arrive in production.
Typical internal objections
"We will build this in-house."
Often the right call. The bottleneck is rarely capability but capacity next to daily operations. In that case we deliver the blueprint and the first production use case and hand over to your team.
"Security and data protection are non-negotiable."
Exactly why every project starts with the data flow diagram and the question which processing must stay inside the EU. Where needed we use EU hosting, redaction or locally operated models.
"Cost will run away once it scales."
Cost is modelled per transaction before building and monitored with budgets and alerts in operation. Where possible, smaller models handle the routine and large ones only the exceptions.
Frequently asked questions
30 minutes, a concrete assessment
Describe your use case briefly — we will say plainly whether and how it pays off.