AI for financial services and insurers
Every outbound statement has to be reviewed. That does not rule AI out — it only determines what the process has to look like.
In a regulated environment success is decided not by the model but by the process around it: which sources are permitted, who approves, what is documented and how a publication can be traced later. Davies Meyer sets up AI production so that every step is logged, mandatory disclosures are checked automatically and the EU AI Act transparency obligations are met.
Our approach: creative engineering
We work neither as tool consultancy nor as a classic production agency, but in between: creative engineering means building the production chain behind the creative work — templates, generative modules, checkable brand rules and delivery as one system. The creative judgement stays with the team, the multiplication becomes an engineering task.
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
Content with review and approval trail
Creation, automatic checking against mandatory disclosures and documented approval — every step logged and traceable.
Governance and disclosure
Policies, roles and disclosure of AI-generated content, aligned with the EU AI Act transparency obligations.
Internal assistants instead of open tools
Assistants on the approved knowledge base, so employees do not fall back on private accounts of public tools.
Data-minimising measurement
Tracking and attribution models that work with consent and aggregated data instead of collecting as many individual attributes as possible.
How the engagement runs
- 01
Take stock
We look at existing workflows, systems and data sources and record which steps cost time today.
- 02
Pick the use case
One case with a clear metric and manageable risk goes first. Baseline and target are fixed beforehand.
- 03
Build and integrate
Production, validation and approval are connected to existing systems instead of adding another isolated tool.
- 04
Hand over
Playbook, training and documentation are part of the delivery so the internal team can run it.
Frequently asked questions
30 minutes, a concrete assessment
Describe your use case briefly — we will say plainly whether and how it pays off.