Show how Redis gives advisors a real-time context layer that surfaces the right next action before client drift becomes asset flight.
This demo is about stopping high-value client drift before it turns into an ACAT request. The data already exists in portfolio systems, CRM, risk analytics, held-away aggregation, and market feeds. The issue is getting all of it in front of the advisor fast enough to change the meeting before the meeting starts.
This is built for heads of wealth, advisor platform teams, and data/ML teams. The visible demo stays customer-safe. The script is where the presenter carries the detail, the stakes, and the ask.
The data is already there: Portfolio and performance platform, Salesforce-style advisor CRM, risk analytics engine, held-away asset aggregation, market/news feeds, Databricks features, and Kafka behavioral events. The issue is not whether the data exists. The issue is whether it can be assembled and acted on in <10 ms while the moment is still live.
That is what this demo shows. Nine stages, one primary decision moment centered on Elena Petrov, and a decisioning pipeline that turns scattered operational context into the winning action: Proactive portfolio review.
At the top are the systems of record and the live signal sources for Meridian-Style Wealth. Nothing here is being ripped out or displaced.
The ingest layer has two jobs. RDI handles change data capture and operational sync from the core repositories. Redis FeatureForm handles the feature pipeline from the analytical and streaming systems into the Redis context layer. Two tools, two roles, one unified ingest layer.
The Redis context layer is the operational working set. Hot data and live session state stay in RAM for sub-millisecond access. Larger history, embeddings, and warm operational context sit in Redis Flex. And the Redis Context Retriever sits below those stores, assembling the Client 360 — portfolio state, relationship history, and risk context — and exposing it as structured tools for the decision engine. This is the layer that makes history and live state usable together.
The decision engine is where rules, eligibility, ML ranking, vector search, and policy arbitration come together. The output channels are where the business sees the result. And the learning loop makes every accepted or rejected action improve the next one.
This is the architecture. Now let me show you what happens when the live customer moment actually starts.
Elena Petrov is the stand-in for the larger pattern. This is not one edge case. This is the repeatable decision moment Meridian-Style Wealth needs to handle every day.
If the business waits too long or acts on partial context, it loses the moment. If it decides fast enough with full context, it creates higher retention, wallet-share capture, and better advisor preparation in the highest-value households.
We have one live moment to recognize Elena Petrov correctly and act before the old process falls back to something generic.
This is the additive-not-disruptive stage. Say it explicitly: Meridian-Style Wealth keeps its existing repositories, models, and applications. Redis is not the new system of record. Redis Iris is the context engine — the operational serving layer that makes the existing systems act together.
Walk the room through the source systems in the context of this use case: Portfolio and performance platform, Salesforce-style advisor CRM, risk analytics engine, held-away asset aggregation, market/news feeds, Databricks features, and Kafka behavioral events.
For a business audience, keep this short and emphasize lower implementation risk. For a technical audience, slow down on the separation of concerns: RDI for operational sync and change capture, Redis FeatureForm for train-serve parity and online feature delivery.
Redis does not replace the existing stack. RDI and Featureform make that stack operational in the live decision window.
The left panel is who the customer has been over time. The right panel is what is happening right now. The decision quality depends on both.
Use the left side to explain durable context, then the right side to explain the live trigger. Bring the room back to why the winning action is Proactive portfolio review and why the alternatives are weaker in this moment.
Redis Context Retriever assembles the Client 360 — portfolio state, relationship history, and risk context — so the decision engine has exactly the live context it needs.
The important point is that this is not just personalization. It is contextual intelligence. History without the live state is stale. Live state without the history is shallow. Redis is the layer that serves both together at request time.
A profile tells you who the customer is. Context tells you what the business should do next.
This stage is for the data and ML stakeholders in the room. The point is not the specific feature names by themselves. The point is that the same features used to train the model are available online at the moment of decision with the same definitions.
Explain train-serve parity clearly. Most teams can train a model. The hard part is serving the right features fast enough in production. Redis FeatureForm on Redis closes that gap and removes the drift between the notebook and the application.
Tie it back to the visible demo. These features are what allow the system to choose Proactive portfolio review instead of defaulting to Held-away consolidation pitch or surfacing New alternative investment at the wrong time.
Your model is only as good as the features you can serve in milliseconds, not the features you can describe in a slide deck.
Now the decision is visible. Walk the room through the winner first: Proactive portfolio review. Explain why it wins on relevance, economics, and policy fit for this exact moment.
Then compare it to the alternatives. Held-away consolidation pitch is usually the path the legacy process would take because it is simple or generic. New alternative investment is the kind of action a model might surface if it saw only part of the context or ignored policy and operational constraints.
The point of this stage is to show that Redis is not just ranking what is most likely to be clicked. It is arbitrating across policy, economics, relevance, and timing in one place.
We are not surfacing random recommendations. We are ranking the actions the business already cares about and choosing the one that fits this moment best.
The winner translates directly into business language: higher retention, wallet-share capture, and better advisor preparation in the highest-value households.
The value is not one click, one claim, one quote, or one call. It is the compounding effect of getting these moments right at scale.
The visible economics are the reason the next step is a pilot, not just a technical evaluation of whether Redis is fast.
The math is not the single transaction in front of us. It is what happens when this decision gets repeated across the full book of business.
The left side shows the legacy experience: generic, delayed, or incomplete. The right side shows the same customer moment with the right action already staged. It is the same surface and the same business flow. What changed is the decision layer underneath it.
The winner is Proactive portfolio review. The real product is not the UI redesign. The real product is the ability to put the right action into the existing UI before the moment passes.
Same surface. Same moment. Different decision layer. That is the product.
Come back to the architecture now that the room has seen the story end-to-end. The same five tiers are still there, but now the audience understands what each one contributed to the outcome.
Summarize the three takeaways. First, this is not a science project — it is a practical architecture for Meridian-Style Wealth. Second, it is additive, not disruptive — the existing systems stay in place. Third, it is a business story first and a platform story second — the reason to do it is higher retention, wallet-share capture, and better advisor preparation in the highest-value households.
Close on the ask. The next step is not to admire the demo. The next step is a focused working session to map this reference architecture to the customer's actual environment and scope one advisor team, one HNW cohort, and a pilot measured on retention-risk capture and wallet-share lift.