Strategy · GenAI · Automation

AI Agency.

Davies Meyer helps CMOs and product leaders turn AI hype into shipped products, workflows and personalization — with senior engineering, clear measurement and pragmatic governance.

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What an AI agency does in 2026

AI in 2026 is past the demo phase. LLMs, RAG systems, embeddings, agentic workflows and multimodal generation are becoming infrastructure. The winners are not the companies with the most experiments — they are the ones that ship durable, measured AI systems into real customer and employee workflows.

Davies Meyer works as an AI agency across three layers: strategy (where AI creates measurable value and where not), products (customer-facing GenAI experiences, assistants, on-site personalization) and automation (marketing, service and content workflows). Every engagement includes evaluation, security and governance from day one.

Our team combines applied ML, LLM engineering, product design and marketing craft. That is what turns a prototype into a system your business actually depends on.

Our approach in 5 steps

A process that connects strategy, execution and measurement.

Use Case Discovery

Workshops, data audit and prioritization — where AI creates measurable value in your business and where it does not.

Feasibility & Prototype

Rapid technical prototypes (RAG, agents, personalization) with evaluation before you invest in production.

Production Engineering

Robust product build with evaluation, guardrails, observability, cost controls and security by design.

Rollout & Change

Integration with existing systems, training for teams and clear ownership models.

Evaluation & Iteration

Quality evaluation, drift monitoring, cost tracking and quarterly roadmap updates.

Challenges we typically solve

What lands on our table most often — and how we approach it.

Endless PoCs, no production

We help you cut the backlog to the 3 use cases with real ROI and ship them properly.

Hallucination and quality risk

We build evaluation harnesses, guardrails and retrieval systems that make LLM output reliable enough to ship.

AI cost getting out of hand

Model selection, caching, batching and fallback logic keep quality up and cost predictable.

AI Overview & LLM search visibility

We optimize your content and structured data for AI Overviews and LLM answers — with measurement.

Services & disciplines

Integrated pillars — delivered by one senior team.

AI Strategy & Roadmap

Use case discovery, prioritization, ROI modeling and roadmap across marketing, product and operations.

GenAI Products

RAG chatbots, on-site assistants, content copilots and creative tools — production-grade with evaluation.

Automation & Ops

Marketing automation, content workflows and internal copilots that reduce cost and increase throughput.

Personalization

AI-driven personalization on web, app and CRM — with proper measurement of incremental value.

Why Davies Meyer

Applied AI team: LLM engineering, RAG, embeddings, agentic workflows and evaluation.
Strategy that separates real value from theater — and a roadmap you can defend.
Marketing-native: AI shipped into content, campaign and customer workflows.
Governance, security and evaluation from day one, not as an afterthought.
Ships on your stack — OpenAI, Anthropic, Google, open-source models, your cloud.

FAQ

Which models do you work with?

Frontier models from OpenAI, Anthropic and Google as well as open-source models (Llama, Mistral, Qwen) hosted on your cloud when data residency or cost requires it.

How do you measure AI ROI?

Per use case: cost saved, throughput gained, incremental revenue from personalization, response times reduced, quality improved — with baselines.

What about data privacy and EU regulation?

We design for GDPR and EU AI Act from day one: data residency, PII handling, model choice, human oversight and documentation.

Can you also do AI SEO / AI Overviews?

Yes. We optimize structured content, schema, entity coverage and authority signals for AI Overviews and LLM answer engines — with measurement of visibility and referral traffic.

How long does an AI project take?

Strategy sprints run 4–6 weeks. Product builds typically 3–6 months for the first production release. Automation programs deliver first wins in weeks.

Do you handle change management?

Yes. Training, playbooks and hands-on enablement for teams working with the new AI tools — otherwise adoption stalls.

Turn AI into a system, not a demo

We help you prioritize use cases, ship production-grade AI and measure what it is worth.

Start with a strategy call