Applied AI.
Human-led.
Outcome-driven.

The problem. Abuse and leakage are among the best-evidenced financial drains a smaller organisation faces — promotion abuse, coordinated multi-accounting, payment and process exploitation. Leaner brands are most exposed: they compete hard on acquisition and offers but rarely have the dedicated risk teams larger organisations maintain. Manual review catches only a fraction — usually after the money has gone — and coordinated abuse is engineered to look like ordinary activity.

Our approach. A machine-learning detection layer that learns what legitimate behaviour looks like and flags deviation, cross-referencing device, registration, payment and activity patterns to surface coordinated abuse invisible at single-account level. It runs as a scoring layer feeding your existing team — rules for known patterns, ML for the adaptive ones, with humans retaining every decision.

The problem. Most organisations identify churn only after customer behaviour has already deteriorated — the point at which winning someone back is most expensive and least likely. Most smaller organisations hold the data needed to see churn coming but lack the modelling capability to act on it, so retention stays reactive.

Our approach. A churn-propensity model built on leading indicators — engagement patterns, purchase or deposit cadence, product-mix shifts — that predicts churn weeks ahead rather than confirming it afterwards. The output is deliberately simple: a regularly refreshed, ranked at-risk list with the drivers attached, delivered into the tools your team already uses, with vulnerable customers handled responsibly by design.

The problem. Most smaller organisations run CRM manually with crude segments, held back by data quality and team capacity. The result is batch-and-blast campaigns and interventions that arrive hours or days after the moment that mattered — while larger competitors personalise at scale.

Our approach. Intelligent segmentation, behavioural trigger logic and automated lifecycle journeys layered on the CRM stack you already own — welcome series, dormancy-triggered reactivation, failed-payment recovery, win-back. AI-assisted copy scales campaign volume and testing without added headcount, with human compliance review in the loop and a phased rollout that builds foundations before advanced use cases.

The problem. Organisations competing across multiple markets need localised product descriptions, promotional copy and market pages — almost always a manual bottleneck served by stretched teams or costly agencies. At the same time, discovery is shifting: a growing share of searches now runs through generative AI tools, and businesses that structure content for AI citation can win visibility competitors miss.

Our approach. An LLM-assisted content pipeline with human-in-the-loop compliance review: templated generation, genuine localisation rather than translation, and structuring for generative-engine visibility. Every output passes a market-specific compliance checklist before publication, so speed never comes at the cost of regulatory accuracy.

The problem. Support is a 24/7 cost centre dominated by repetitive queries — order or payment status, terms questions, document chasing — often staffed across multiple markets. Generic automation is dangerous here: terms and conditions are a contract, not content, and a bot that freestyles around them contradicts legal text in public chat logs and mishandles sensitive moments.

Our approach. A support agent grounded strictly in your approved knowledge, resolving routine queries with clean escalation — and with sector-specific guardrails built in from day one: vulnerable-customer and duty-of-care triggers wired to the right workflows, sensitive cases routed correctly, and an audit-ready trail. We instrument resolution quality and escalation success, not just deflection, so the system never optimises for making people give up.

The problem. Regulatory burden scales badly for smaller organisations. Monitoring, affordability or eligibility checks and duty-of-care interactions consume analyst time and carry direct licence risk, and regulators increasingly expect proactive, documented detection rather than manual spot-checks.

Our approach. AI-assisted risk detection — methodologically a sibling of churn modelling — that flags behavioural signals to your compliance team with the evidence attached, supporting timely, proportionate, documented intervention. Every output is explainable and defensible to a regulator, thresholds are co-designed with your compliance owner, and the model augments human judgement rather than replacing it.

The problem. B2B suppliers and sales-led businesses live or die on sales effectiveness, yet most run sales as individual craft rather than engineered process. Performance concentrates in one or two rainmakers, forecasting is unreliable, and what the best performer does differently at each stage is invisible to the rest of the team. With long, high-value cycles, every conversion point is disproportionately valuable.

Our approach. The methodology our founder delivered at Persado, now AI-scaled: decompose the sales process into stages, identify the best performer at each, extract the repeatable behaviours, and standardise them across the team — with AI performing the pattern extraction continuously through call and CRM analysis. Proven to deliver a 35% sales improvement when run by hand; now a living optimisation system.