Optimisation & AI

Most Braze implementations go live at a fraction of their potential. The initial build covers the use cases that were scoped, the journeys that were prioritised, and the data that was available at the time. What comes after is where the real return on investment is built. That work requires a different kind of engagement than implementation: less about building from scratch, more about knowing where the ceiling is and how to raise it.

The most common pattern we see is this: an organisation migrates off a legacy platform under pressure, scoping to what can be shipped before the licence expires rather than what should be built. The team goes live. The crisis passes. And then the MVP configuration they inherited on day one becomes the permanent state of their Braze environment.

The symptoms are recognisable. Custom attributes being used as decision flags because the account was built before Canvas could interrogate entry properties natively. Product catalogues still running on batch syncs because Connected Content was descoped in the final sprint. Template libraries built in a crunch that no one has had the bandwidth to revisit. Journey logic that made sense for the capabilities available eighteen months ago, sitting untouched while the platform has shipped feature after feature around it.

These are not Braze problems. They are the residue of a go-live that was treated as a finish line. Our optimisation work starts by understanding exactly what that residue looks like in your environment, and building a prioritised path through it that is sequenced by commercial return rather than technical convenience.

We are resident experts across the full BrazeAI suite, and we are direct about what each capability actually delivers in practice rather than in a product overview. That means Intelligent Selection and Winning Path for automated variant optimisation without waiting for a scheduled review cycle. Intelligent Timing and Intelligent Channel to stop making send-time and channel decisions manually at campaign level. Predictive Churn and Predictive Events to build audiences defined by propensity rather than recency. AI Item Recommendations for personalisation at a catalogue scale that rules-based logic cannot reach. Generative AI for Liquid, for copy, for SQL-based segment extensions that would otherwise require a data analyst to write. And Decisioning Studio for teams ready to move from predefined journeys into agent-led, outcome-optimised engagement.

None of these are buzz features to put on a C-suite slide. Wielded correctly, each one is a material improvement to the commercial performance of your marketing program. We know which ones are ready to deploy in your environment and which ones require groundwork first.

Beyond Braze, we work with the AI capabilities embedded in the broader composable stack. Hightouch AI Decisioning uses reinforcement learning to make 1:1 decisions on message, content, channel, timing and frequency continuously, learning from every interaction rather than waiting for a human to review a test. Segment's AI Recommendations surface next-best actions and product affinities from your existing customer data without requiring a separate recommendation engine. These are not speculative capabilities. They are in production for enterprise marketers today, and we know how to implement them in ways that compound rather than complicate.

Speed of execution is a competitive advantage that most marketing teams underinvest in. Our operating model work looks at how your team is set up to execute against your platform: where approvals slow campaigns, where technical dependencies create bottlenecks, where the gap between marketing intent and platform output is wider than it needs to be. The goal is a team that can move at the speed the platform is capable of, without trading quality or governance to get there.

This is where deep Braze expertise and operational thinking come together. We know what good looks like at the delivery layer, and we know what it requires from the team and processes around it.