Show how Redis helps carriers route claims to the fastest safe path by assembling policy, history, fraud, and operational context before the first triage decision.
Claims organizations already have policy data, prior claims, telematics, repair network data, and fraud models. The problem is that the adjuster or digital FNOL flow sees them too late. The result is over-review on clean claims and slow handling on the claims that should move immediately.
This is built for claims, operations, fraud, and platform 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: Policy admin system, claims history repository, telematics/photo ingestion, repair network and rental APIs, fraud graph, feature lakehouse, and Kafka claim 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 Carlos Ramirez, and a decisioning pipeline that turns scattered operational context into the winning action: Straight-through repair scheduling.
At the top are the systems of record and the live signal sources for Harbor Mutual. 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. This is the layer that makes history and live state usable together. And the Redis Context Retriever sits below those stores, assembling the Claimant 360 — policy state, claim history, and risk context — and exposing it as structured tools for the decision engine.
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.
This is Carlos Ramirez — the stand-in for the larger pattern. This is not one edge case. This is the repeatable decision moment Harbor Mutual needs to handle every day.
The business stakes are visible on the right-hand panel. 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 faster cycle time, lower handling cost, and tighter fraud triage without slowing clean claims.
We have one live moment to recognize Carlos Ramirez correctly and act before the old process falls back to something generic.
Harbor Mutual 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.
The source systems in this use case are: Policy admin system, claims history repository, telematics/photo ingestion, repair network and rental APIs, fraud graph, feature lakehouse, and Kafka claim events.
RDI handles operational sync and change capture. Redis FeatureForm handles 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.
Redis Context Retriever assembles the Claimant 360 — policy state, claim history, and risk context — so the decision engine has exactly the live context it needs. The left side is durable context, the right side is the live trigger. The winning action is Straight-through repair scheduling because both sides converge on the same answer.
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.
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.
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.
These features are what allow the system to choose Straight-through repair scheduling instead of defaulting to Virtual adjuster review or surfacing SIU escalation 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.
The winner is Straight-through repair scheduling. It wins on relevance, economics, and policy fit for this exact moment.
Virtual adjuster review is usually the path the legacy process would take because it is simple or generic. SIU escalation is the kind of action a model might surface if it saw only part of the context or ignored policy and operational constraints.
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.
Translate the winner into business language: faster cycle time, lower handling cost, and tighter fraud triage without slowing clean claims.
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 Straight-through repair scheduling. The real product is not the UI redesign. The real product is the ability to put the right content, action, or recommendation into the existing UI before the moment passes.
Same surface. Same moment. Different decision layer. That is the product.
The same five tiers are still there, but now the audience understands what each one contributed to the outcome.
Three takeaways. First, this is not a science project — it is a practical architecture for Harbor Mutual. 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 faster cycle time, lower handling cost, and tighter fraud triage without slowing clean claims.
The next step is a focused working session to map this reference architecture to the customer's actual environment and scope one loss type, one geography, and a pilot measured on straight-through-processing rate and cycle time.