We apply large language models and predictive modelling to specific marketing decisions — which segment, which message, which next action — and wire the output back into the systems your team already uses.
Most AI marketing initiatives stall in one of two places. Either the team buys a tool with AI in the name and nothing about the underlying process changes, or someone runs an unbounded model against customer data with no evaluation, no guardrails and no way to tell whether the output is right. Neither produces durable advantage. Useful AI marketing starts with a specific, measurable decision that a model can make better or faster than the current process.
We identify the decisions in your marketing operation where a model genuinely outperforms a rule, build those with evaluation and guardrails in place, and leave the deterministic parts deterministic. AI where it earns its place, plain engineering everywhere else.
Models embedded into live marketing workflows rather than sitting beside them in a separate tool, so their output triggers real downstream action.
Content operations where the model handles volume and variation while your experts own the argument, the facts and the final call.
Personalisation scoped to the data you actually hold, with clear fallbacks so an incomplete profile never produces an awkward experience.
Scoring that learns from your closed-won history rather than from a workshop guess, and that explains why a lead scored the way it did.
Segments discovered from behaviour, then validated against business meaning — a cluster is only useful if you can act on it differently.
Scoped agents that carry out multi-step tasks with tool access, operating inside permissions and logging every action they take.
Models applied to measurement, where the value is in surfacing what a human analyst would not have had time to look for.
AI is most useful where a marketer repeats a judgement call hundreds of times a week. We start by finding those decisions and measuring them, rather than adding a chatbot to a homepage.
Any AI component ships with an evaluation set. We measure accuracy against human-labelled examples, define a fallback path, and keep a human in the loop for anything customer-facing until quality is proven.
Typical situations where teams bring us in for this work.
An LLM reads the free-text request, extracts intent and company context, and scores it alongside firmographic fit.
Experts write the source argument once; the workflow adapts it across formats, markets and channels under editorial review.
Behavioural signals across product, support and billing feed a risk score that triggers a retention play before renewal.
Content structured and marked up so ChatGPT, Perplexity and AI Overviews can retrieve and cite it accurately.
An agent assembles account history, recent news and product fit into a one-page brief ahead of every meeting.
The same delivery method every engagement follows — see our full process for more detail.
We list the decisions in your funnel, then score each on volume, cost of error and data availability to find where a model pays off.
What data exists, what quality it is in, and what policy governs sending it to a model vendor.
Before building, we assemble labelled examples that define what a correct output looks like. Nothing ships without measuring against it.
A narrow working prototype on real data within weeks, so the decision to proceed is based on evidence rather than a vendor demo.
The model is wired into live workflows with confidence thresholds, fallbacks and human review where the stakes justify it.
Ongoing quality monitoring, drift detection and a documented retraining or prompt-update procedure your team can run.
We are vendor-neutral. These are the platforms we work with most often on this service.
Consultants spent significant time locating precedent documents and prior project material spread across several repositories.
Read the case study SaaSInbound demo requests were triaged by hand in a shared inbox. Follow-up took over a day on average and sales had no consistent definition of a qualified lead.
Read the case studyWe are model-agnostic and select per task — typically frontier models from OpenAI, Anthropic or Google for reasoning tasks, and smaller or open models where cost and latency matter.
Customer data is processed under the model provider's zero-retention or enterprise terms where available, and we minimise what is sent by redacting identifiers that the task does not require.
Generative Engine Optimisation and Answer Engine Optimisation are the practices of making a site legible and citable to AI answer systems such as Google AI Overviews, ChatGPT search and Perplexity.
See how answer engines read and cite your site.
Website audit, AI readiness, DNS, SSL, API and MCP testing tools.
Articles on AI marketing, automation, analytics and AI search visibility.
Fixed-scope automation, AI agent and analytics engagements.
Automated lifecycle, lead flow and CRM operations
ServiceProduction AI applications, internal tools and agents
ServiceTechnical SEO, semantic structure and AI search visibility
SolutionAgents that act inside your marketing systems
SolutionLLM-driven decisions inside your lifecycle workflows
Tell us the marketing decision that costs you the most time or accuracy today, and we will tell you honestly whether AI is the right answer for it.
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