Marketing

What We Do

We apply machine learning and LLM technology to the parts of marketing where automation creates measurable leverage: segmentation, content personalisation, lead scoring, and campaign analytics.

Core Capabilities

Customer Segmentation Behavioural clustering on CRM and product usage data. Move beyond firmographic segments to clusters that actually predict buying behaviour — then operationalise them in your existing marketing stack.

AI-Assisted Content Production Structured content pipelines that draft on-brand copy at scale — campaign emails, landing page variants, product descriptions — with human review at defined quality gates.

Lead Scoring & Routing ML models trained on your historical conversion data. Scores that update in real time as prospects interact with your product and content, delivered to CRM and sales tooling via API.

Campaign Analytics Beyond click rates: attribution modelling, segment-level performance decomposition, and cohort analysis that links campaign spend to downstream revenue.

How We Work

Every engagement starts with a data audit. We assess what signals are available, what is predictive, and what is missing. Work items are scoped to produce a measurable output — not an open-ended analysis — within a defined timeframe.

When this work is useful

Common triggers are a CRM full of inconsistent fields, campaigns that cannot be compared across segments, or a content team spending time on repeatable drafts without a reliable review path. The objective is not to automate persuasion. It is to make one decision—such as prioritisation, routing, or controlled content variation—more consistent and easier to inspect.

Delivery model and deliverables

We begin with a data audit and a baseline for the selected workflow. Available identifiers, consent and retention boundaries, missingness, label quality, and the destination system are mapped before modelling. A first slice produces a bounded output with a review state. Only after the team can inspect false positives and false negatives do we add refresh schedules, CRM delivery, or content variants.

Deliverables typically include a signal map, segment or scoring definition, evaluation set, review interface, API integration, refresh runbook, and monitoring for drift. Related patterns include customer segmentation and the finance reporting service where grounding matters just as much.

Risks and acceptance

Historical conversion data can encode outdated decisions or proxy variables. Personalisation can also exceed the intended purpose of data collection. These are design constraints, not tasks for a later legal checklist. Acceptance should define the target population, permitted inputs, review rules, freshness, and the action taken when confidence is low. Scope a focused marketing system.

Integration notes

Outputs should arrive where the team already works, with a clear status and a way to inspect the supporting signals. A score without its definition, refresh time, and fallback behaviour is difficult to operate. Content variants should carry their review state and provenance into the publishing workflow. This keeps automation reversible and makes it possible to compare a controlled change with the existing process.