AI Engineering

AI reviews draft assets against past campaign performance and brand guidelines, flagging low-performing variants before they reach production, with fewer revision cycles and faster client approvals.

Agentic AI generates theme-consistent visuals, alt text, and localisation edits, cutting hours of manual work per asset and shortening time-to-publish.

Models swap headlines, images, and offers for each visitor in the moment, boosting engagement without extra creative rounds.

Real-time clustering adjusts media buying based on behaviour data, helping planners reallocate budget toward high-converting cohorts and reduce wasted spend.

Reinforcement-learning agents test subject lines, send times, and content blocks, then select for each subscriber the variant with the highest open-to-conversion ratio.

Models monitor daily spend and authorised audiences, pausing ads that drift off brief and preventing budget overruns.

Natural-language algorithms analyse form fills, chat logs, and intent signals, routing high-quality prospects directly to CRM stages and lifting sales acceptance rates.

Large language models translate analytics dashboards into client-friendly summaries, giving account managers more time for strategic planning and relationship building.

Forecasting aligns studio schedules with projected asset demand, balancing workload, reducing overtime costs, and improving on-time delivery metrics.