AI Consulting for Brands

By treating your brand voice as a specification, not a vibe. We codify your guidelines, tone-of-voice documentation, approved claims, terminology, and prohibited topics into machine-enforceable constraints, ground generation in your own approved content through retrieval, and add automated output checks that flag drafts drifting off voice before a human ever sees them.

Then we test it the honest way: blind review by your own editors. If they can reliably tell the system’s drafts from your writers’ — and prefer the writers’ — the system isn’t ready. That evaluation is part of the proof of concept, so you see the evidence before anything scales.

Your editorial workflow stays in charge — we engineer around it, not past it. Most brands land on a tiered model: low-risk, high-volume assets (variants, internal drafts, localizations of approved copy) flow through a lighter review; customer-facing and claim-bearing content always passes human sign-off. The system routes each output to the right tier and keeps an audit trail of what was generated, edited, and approved.

The practical effect is that editors stop being production bottlenecks and become what they should be: the quality bar.

Yes — that’s the default architecture we recommend. We deploy private, hosted LLM solutions where your guidelines, archives, and prompts stay in an environment you control and are never used to train a third party’s model. Your voice is competitive IP; the architecture should treat it that way.

By turning unstructured public feedback into tracked metrics. Sentiment models score reviews, social mentions, and support conversations continuously; topic models group them into themes (product quality, service, price perception, values); and trend analysis shows how each theme moves over time and around events — launches, campaigns, incidents.

The output is a brand-health dashboard with a baseline, so instead of “sentiment feels worse this quarter,” you can say which driver moved, when it started, and in which channel — and see whether your response worked.

Done properly, yes. Our brand-health systems analyze publicly available content, work on aggregated and pseudonymized data wherever the question doesn’t require identity (and brand-health questions almost never do), and follow platform terms for data access. As a German company we apply GDPR discipline by default — data minimization, documented processing, retention limits — which keeps the program defensible for clients worldwide. The goal is measuring the crowd’s perception, not profiling individuals, and the architecture enforces that distinction.

Computer vision makes the visual layer of your brand monitorable at scale. We build detection models trained to recognize your products, packaging, logos, and design marks in image streams — marketplace listings, submitted photos, partner content — and flag likely counterfeits, unauthorized usage, or off-standard presentation for your team to act on.

To be clear about the division of labor: the AI does the finding at a scale no human team can match; enforcement remains a legal and commercial process. Our object detection work shows the underlying engineering: continuous visual oversight without continuous supervision.

Only if the models optimize clicks with no notion of the brand. We build personalization inside brand rules: the system decides which approved message, offer, or product story fits each customer, but every variant is drawn from — or validated against — your codified standards. Think of it as adaptive delivery of one identity, not a thousand micro-brands.

Behavioral modeling is what makes this work: models trained on what customers actually do (see our usage-based insurance project for the technique) choose relevance, while the brand layer guarantees consistency.

As the engineering layer under the relationships you already have. Your agencies keep doing what they’re good at — strategy, creative, media — while we build the brand-owned infrastructure they plug into: the voice-safe generation system, the brand-health dashboard, the personalization models. Deliberately brand-owned, because the models, guardrails, and data foundations should belong to you, not sit inside any one agency’s stack.

In practice we work with your agencies day to day — shared briefs, access to the guardrailed tooling, joint review workflows — and many brands find that a common brand-constrained toolset actually makes multi-agency output more consistent, not less.

You do. The brand-voice specifications, prompt libraries, fine-tuned or configured models, data pipelines, and dashboards we build are deliverables that transfer to you, with documentation and training so your team — and your agencies — can operate and extend them. We deliberately avoid architectures that lock your brand into our involvement or into a proprietary platform you can’t leave. Continued partnership should be a choice you make on results, not a dependency we engineered.

Start where your brand’s data is richest and the question is sharpest. Three common entry points:

• A brand-health baseline if perception is tracked by anecdote — your reviews and mentions already exist, so a listening PoC can show signal quickly
• Voice-safe content operations if production volume or agency consistency is the bottleneck — your guidelines are the training material
• A personalization pilot if you’re a DTC brand with first-party behavioral data and flat conversion

A well-scoped proof of concept takes weeks, not months, and is priced as a fixed package — so the decision to scale is made on evidence. Contact us to scope the first question.