Artificial Intelligence (AI) has quickly evolved to become an integral part of modern business solutions, revolutionizing how companies operate and engage with customers.
Redpoint’s approach to AI starts with getting the data right. In the first part of this series, we explored how the Redpoint Data Readiness Hub ensures data is complete, accurate, timely, and fit-for-purpose—making it AI-ready. This post highlights the next step: AI Inside. Here, we focus on how Redpoint embeds AI directly into the platform to enable intelligent automation, advanced personalization, and continuous optimization – while giving businesses the flexibility to bring their own models and tools to tailor AI to their unique needs.
AI Possibilities Unleashed
Whether it’s through embedding native algorithms or integrating with third-party platforms like Databricks and Snowflake Cortex AI, Redpoint’s software leverages AI to deliver a wide range of functionality. From predictive models and machine learning to embedded reporting and visualization, these capabilities empower companies to make data-driven decisions and optimize their processes. AI-prompt based workflows and embedded data visualization tools like Sigma further enhance ease of use, making sophisticated AI tools accessible even to those with limited technical expertise.
In addition to these capabilities, Redpoint also implants AI to tackle difficult edge cases such as address standardization. This is accomplished using a methodology built upon decades of experience in learning about and solving these challenging scenarios, effectively baking that knowledge into AI-fueled processes.
AI Innovation Made Real
From simple to complex, there are an endless number of ways to use AI to improve CX through the use of software products and solutions. As a starting point, perhaps the initial objective is to simply integrate with a third-party innovation to produce a more human-like chatbot. Advancing on the AI maturity curve, it is also possible to infuse AI in a way that brings out-of-the-box predictive models and machine learning into workflows to create next-level personalized journeys and campaigns – with optimal journeys and campaigns where the AI is properly fed with AI-ready data.
Embedding AI innovations into software unleashes a full spectrum of AI-driven decisioning, from descriptive analytics and evaluation to recommendations and next-best actions.
Infusing customer engagement technology with AI is a closed-loop process, where integrating AI into processes generates a better lift in performance – from detecting anomalies in data to improving data match rates. When those results are fed back into a system that makes the data right and fit-for-purpose, the methodology behind a process like identity resolution is continually fine-tuned. It’s a form of captured intelligence, an AI sidecar that continuously improves both inputs and outputs.
AI Your Way
This embedded intelligence is made possible by the way Redpoint’s software is architected. Designed with openness and flexibility in mind, the Redpoint platform is entirely platform-agnostic, built to work with any cloud, any data warehouse, any database, and any security or deployment model. It fits seamlessly into the broader AI ecosystem without locking you into a single vendor or approach. Whether you’re using AI capabilities from Salesforce, Adobe, Databricks, or your own proprietary models, Redpoint makes it easy to integrate, orchestrate, and scale without compromising control or transparency.
A key advantage of Redpoint’s architecture is the option it provides to keep your data where it is most secure – behind your firewall – while still making it accessible for advanced analytics, machine learning, and AI. This means your approaches to managing and protecting data stay intact, even as you evolve your AI capabilities.
Redpoint’s engines are highly configurable and built with standardized APIs, making it easy to expose functionality and automation across a wide range of roles, systems, and use cases – from simple to sophisticated. Its object-oriented design allows individual components to be tuned for specific channels, databases, attributes, or roles. For AI applications, this translates into a powerful environment where AI agents can operate with fine-grained control, adjusting and optimizing across multiple dimensions.
AI Transparency & Trust
Unlike rigid or black-box solutions, Redpoint was built not from the UI down but from the engine up – giving AI agents access to the levers that matter. That transparency is critical: not only can AI optimize decisionsthat may span data quality, identity resolution, and campaign orchestration, but the outcomes are understandable and tunable. Analysts and data teams retain visibility into how decisions are made, creating trust in both the models and the data that powers them.
This balance of robustness and manageability puts Redpoint in a unique position. Where isolated systems might use AI to perform one or two tasks with little flexibility or explainability, Redpoint thrives in bringing simplicity to the complexity inherent in modern CX and AI use cases. It gives your teams – and your AI agents – the tools to optimize outcomes intelligently, securely, and at scale.
This series on data readiness for AI will continue with a post that expands on the Redpoint approach to Agentic AI. For more on the Redpoint approach to AI, click here.
