AI Consulting for Marketing

Because vendor AI is trained on aggregate data across all of that vendor's customers and offers everyone the same capability — it can raise your baseline, but it cannot differentiate you. Custom models are trained on your first-party data: your customers' behavior, your campaign history, your margins. They answer questions no tool ships with, like "which of our customers will churn this quarter and why," and their advantage compounds as your data grows.

The two are complements, not rivals: we routinely deploy custom model outputs into martech tools, where their built-in automation can act on them. The tools remain the hands; your models become the brain.

With measurement designed before the model ships, not reporting bolted on after. The standard instrument is a holdout: a randomly selected group of customers or campaigns that the model does not touch, compared against the group it does. The difference between the two is the model's incremental effect, isolated from seasonality, market shifts, and creative changes that hit both groups equally.

Every model we deploy comes with this measurement design and a dashboard that reports lift against the holdout — so when finance asks what the AI changed, the answer is a controlled comparison, not a platform-reported number.

Scattered is the normal starting state — CRM, web analytics, campaign platforms, email, support desk, e-commerce, each holding a fragment of the customer. Unifying those fragments is the first engineering layer of every engagement, not a prerequisite you must meet before calling us.

What matters is that the raw material exists: transaction or conversion history, behavioral signals, and campaign records covering enough time to learn from. During discovery we audit exactly what you have, tell you honestly which use cases your data supports today, and which need a few months of better collection first. Sometimes the honest recommendation is to fix tracking before training models — and we say so.

No. A customer data platform or warehouse helps, and if you have one we build on it — but the pipelines we construct to feed your models can pull directly from the systems you already run, via their APIs and exports. Many clients effectively get a lean, model-ready customer data layer as a by-product of the first project.

What we advise against is the reverse order: spending a year on a platform migration justified by AI that hasn't been scoped yet. Prove the model on a focused data pipeline first; let the evidence shape what infrastructure is actually worth buying.

By treating brand and compliance rules as system constraints rather than instructions the model is politely asked to follow. Concretely, that means generation grounded in your approved content — brand guidelines, tone-of-voice documentation, verified product claims — so the model draws from what your team has signed off rather than the open internet; automated validation that checks outputs against prohibited claims and required disclaimers before anyone sees them; and human review checkpoints built into your existing editorial workflow for anything customer-facing.

Where confidentiality matters, the whole pipeline can run on a private, hosted LLM, so your unreleased campaigns and prompts never leave your environment or train anyone else's model.

As a German company, we engineer every project to GDPR — the strictest mainstream privacy regime — for every client worldwide, which puts you ahead of most regional requirements by default. In practice: data processing agreements, data minimization, and pseudonymization or aggregation wherever the model doesn't need identities — most marketing models predict from behavior patterns, not names.

Architecturally, your data stays under your control: models can train and run inside your environment, and nothing is shared with third parties or used beyond your project. With third-party cookies fading, models built responsibly on consented first-party data are not just the compliant path — they are the only durable one.

You do. The models are trained on your data, deployed in your infrastructure or accounts, and delivered with documentation, architecture notes, and runbooks. There is no proprietary platform you must keep paying to retain access to your own predictions — vendor lock-in is precisely the problem custom AI is meant to solve, and we won't rebuild it under a different name.

It replaces work, not marketers — specifically the work your team probably resents: manual list-building, copy-variant grinding, report assembly, eyeballing thousands of reviews. What it cannot replace is judgment: positioning, creative direction, and the decisions the models' outputs inform.

We build for that division of labor deliberately, and through the AI Academy we train your team to operate, evaluate, and extend the systems — so the capability lands inside your organization instead of remaining dependent on us.

Start where three things intersect: a KPI you already report on, data you already collect, and a decision someone makes repeatedly. Common first projects by symptom:

• Retention is leaking — churn prediction on your CRM and usage history, with a holdout to prove the saves
• Content is the bottleneck — guardrailed generation grounded in your brand material, measured on throughput and approval rates
• Budget allocation runs on opinion — campaign forecasting and marketing-mix insights from your performance history
• You're rich in feedback, poor in insight — sentiment mining across reviews, tickets, and surveys

Our use case discovery scores the candidates by expected lift and data readiness, so the first project is chosen on evidence rather than enthusiasm.

Through fixed development plans: a defined outcome at a predefined price for each stage — proof of concept, MVP, production — with a go/no-go decision between stages, so budget only follows evidence. A well-scoped marketing PoC typically takes weeks, not months, and is measured against the KPI agreed at the start. Contact us with the metric you want to move, and we'll come back with a concrete scope.