Marketing & Advertising AI Engineering

Yes, and arguably better. As third-party signals fade, value shifts to first-party data used well: propensity and attribution models built on data your customers consented to share. We design for that shift, because targeting that leans on disappearing signals is a short-term play, while first-party modeling is durable and compliant.

With the right setup, yes, but not out of the box. We ground generation in your brand guidelines and examples and keep human review before anything publishes, because generic AI copy erodes trust faster than it saves time. On-brand generation is a system you tune, not a button you press, and we build it that way.

We build on consented first-party data, apply consent and subject-rights controls, and test that models do not proxy for protected characteristics. Privacy is not a constraint bolted on at the end; it shapes the data and the model from the start, which is also what makes the targeting durable as rules tighten.

It can model which channels and touches drive conversion far better than last-click, but attribution is inherently noisy, so we build defensible, explainable models rather than a black box. The honest goal is a clearer picture to guide spend, not false precision. We are upfront about the uncertainty so decisions rest on signal, not a single number.

You do. Your customer, campaign, and analytics data, the trained models, and the code remain yours, with privacy-respecting handling. We build on your stack and hand over documentation, so there is no lock-in to us in what we deliver.

There is no single price; cost tracks scope. A single segmentation and propensity modeling build is a modest, weeks-long project, while a wider rollout across your CRM, ad, and analytics systems is larger. We scope from one use case, quote a fixed range up front, and sequence so early value funds the next step rather than pricing everything at once.

The return comes from less wasted spend, better attribution, and higher conversion, set against build and running cost. It only holds when the model targets a real, measured cost, so we baseline first and report value against it. We would rather size the return honestly on your numbers than quote an industry average that may not fit you.

A focused pilot on one use case such as segmentation and propensity modeling usually reaches a working version in a few weeks, then tuning on real data. Wider rollout takes longer. We start narrow, prove the numbers, and extend, so you see value early instead of waiting months for one large launch.

Common ones are segmentation and propensity, attribution modeling, on-brand content generation, and ad-fraud detection. The best first project is the one tied to your biggest measurable cost or opportunity, not the most advanced-sounding option. We help you pick the use case where value is fast and the data already supports it.

We start with a short feasibility check on one use case: does the data exist, is it usable, and does it hold the signal the model needs. Often you already have more usable data than you expect. We assess it before recommending any build, so the first step is a decision, not a commitment.