AI where the math genuinely helps.
AI agents for data analysis, marketing mix models for budget decisions, predictive LTV and churn models — applied AI deployed where it produces better decisions than the alternative, not where it pads a proposal.
AI that learns and predicts.
Applied AI, not AI theatre.
AI has had two bad years in marketing. Every vendor proposal has an AI section. Every agency deck talks about AI capabilities. Most of it is either rebranded historical analytics or chat interfaces layered on top of dashboards that were already working fine. We build the AI work that actually produces better decisions — and we pass on the work where a human analyst or a simpler model would produce the same answer cheaper.
Where applied AI earns its place: marketing mix modelling for budget allocation decisions, predictive LTV and churn models that route CRM investment, AI agents that analyse account anomalies a human analyst would miss in the volume, forecasting models that survive seasonality and structural breaks, and classification models for commerce use cases (product categorisation, review sentiment, content tagging at scale).
Where we push back: AI-generated content that costs more to edit than to write, AI-powered dashboards that obscure what simple SQL would show clearly, and predictive models built on three months of data that will silently mispredict anything seasonal. Applied AI is useful when it solves a problem human effort cannot scale to — not as a default.
What makes the difference.
Marketing Mix Modelling
Open-source marketing mix modelling frameworks deployed for budget allocation decisions — not as black-box vendor services. The model runs transparently against your data and the outputs are auditable.
AI Agents for Analysis
LLM-powered agents that perform account anomaly detection, cohort analysis, and the kinds of repetitive investigations a human analyst would do manually. Internal tools we built and use daily — now deployable against client data.
Predictive LTV & Churn
LTV prediction models that account for cohort and channel dynamics. Churn models that identify at-risk customers early enough for retention interventions to work. Both feed marketing and CRM decisions directly.
Forecasting
Time-series forecasting with seasonality, holidays, and structural break handling. Revenue forecasting, demand forecasting, and capacity planning — models calibrated to the commercial reality, not fit to short histories.
Classification & NLP
Product categorisation at scale, review sentiment analysis, content tagging, and the text classification work commerce operators need to scale a catalog or understand customer voice at scale.
Honest Model Selection
When a simpler model or a structured query produces the same answer, we use that instead. The job is producing better decisions — not deploying the most impressive model that would fit on a pitch deck.
Running applied AI.
Problem Framing
Honest diagnosis of whether AI is the right solution to the problem, or whether simpler analytics would answer the question at lower cost. Many proposals end here with a simpler recommendation.
Model Design
Model architecture chosen for the specific problem. Feature engineering, validation approach, and success criteria defined before development starts. Bias and failure modes considered explicitly.
Build & Validate
Model development with holdout validation, backtesting, and sensitivity analysis. Results stress-tested before deployment — most models that pass initial validation fail real-world testing, which is why we do both.
Deploy & Monitor
Model integrated into decision workflows (bidding, CRM, inventory, reporting). Ongoing monitoring for drift, accuracy degradation, and situations where the model should be retrained or retired.
Common questions.
Ready to apply AI where it helps?
Let's talk about where applied AI produces better decisions — and where simpler analytics would answer the same question at lower cost.
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