REDIS REAL-TIME DECISIONING

Make instant decisions with real-time data

Redis Enterprise is the real-time decisioning layer that gives every system, model, and agent the live context it needs to decide and act in milliseconds.

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The problem
Solution
Redis Iris
Results
Architecture
Use cases

<10msEnd-to-end decision latency

15xFaster AI responses with LangCache

99.999%Uptime with Active-Active resilience

90%Fewer data hops per decision

THE PROBLEM

Your enterprise brain is running in batch

You have the data platforms, AI pipelines, and analytics engines. But when a decision matters most, your systems don’t have the real-time awareness to act correctly.

Fragmented context

Decisions require multi-hop lookups across 6 to 8 disconnected systems. Each hop adds unpredictable latency and incomplete context.

Offline-only features

ML features live in Databricks or Snowflake with no paths to production inference. Models that worked in training fail when deployed against stale inputs.

No attribution loop

Teams can’t answer simple questions like “What did we recommend?”, “What did the customer see?”, or “What was the outcome?”. Without this loop, models never improve.

Latency that can’t be tuned away

Legacy decisioning stacks run at 300 to 900ms. That is a 30 to 90X gap from ad-exchange-grade performance. No amount of tuning closes a structural limitation.

THE REDIS SOLUTION

The real-time decisioning layer

Redis closes the gap between knowing and doing. One unified platform or surface that ingests live data, serves real-time context, and powers intelligent decisions in milliseconds.

Operational 360

A continuously updated model of customers, accounts, assets, devices, and sessions. Redis Data Integration (RDI) keeps context current through sub-second CDC.

JSON for profiles
TimeSeries for behaviors
Streams for event ordering
Vector embeddings for semantic context

Decisioning engine

Rules, policies, workflows, and ML models run directly on live state. No batch scoring, no stale features, no delayed reactions.

Online feature store via Feature Form
Real-time vector search for AI
Sub-millisecond model serving
LLM grounding with LangCache

Execution fabric

Decisions are immediately applied to apps, services, channels, and workflows. Every outcome feeds back for continuous learning.

Active-Active multi-datacenter
Omnichannel consistency
Decision attribution and RL
Agentic AI foundation

AGENTIC AI-READY:

Redis Iris

Humans prefer realtime, agents require it. The same real-time context that powers sub-10ms decisioning today becomes the foundation for autonomous AI agents.

Context Retriever

Auto-generated MCP tools from your Redis data schemas. Agents discover and query operational data in real time with built-in row-level security.

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Agent Memory Server

Dual-tiered memory system for AI agents. Working memory for active sessions, long-term memory for persistent user context and preferences.

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Redis Data Integration

Keep your context in Redis synced with your source systems and the data from your source systems available to our agents at the speed of Redis.

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LangCache

Semantic caching for LLM responses. Up to 90% reduction in LLM API costs with 15x faster response times on repeated queries.

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AGENTIC AI-READY:

Redis Iris

Humans prefer realtime, agents require it. The same real-time context that powers sub-10ms decisioning today becomes the foundation for autonomous AI agents.

OUTCOMES

Business and technical results

Redis delivers measurable impact for both technology leaders focused on architecture and business leaders focused on outcomes.

Higher conversion & engagement

Real-time personalization delivers the right action at the right moment, driving measurable lift in conversion rates.

Reduced fraud losses

Prevention replaces detection. Real-time behavioral signals catch threats before transactions clear.

Improved retention

Continuous learning and adaptive decisioning improve relevance with every interaction, reducing churn.

Lower operational costs

Fewer manual reviews, fewer failed interactions, and engineering-owned infrastructure replace vendor-heavy licensing models.

Sub-10ms decisioning at scale

Ad-exchange-grade performance with deterministic latency. P90 tracks P50, even during 5x traffic surges.

Unified real-time data tier

One substrate replaces 6 to 8 fragmented systems. RDI ensures the fabric always reflects current reality.

Feature store for production AI

Redis Feature Form bridges training and production. Models serve on live features with zero drift.

Active-Active global resilience

Multi-datacenter consistency with conflict-free CRDTs. Automatic failover, zero data loss, 99.999% uptime.

HOW IT WORKS

Component architecture

Redis replaces 6 to 8 fragmented systems with one in-memory substrate. Five tiers form a continuous loop: ingest, context, decide, act, and learn.

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USE CASES

Decisions that can’t wait

Fraud prevention and risk

Shift from detection to prevention. Evaluate real-time behavioral signals, device context, and transaction patterns.

<5ms transaction risk scoring

Next best action and personalization

Deliver the right offer, message, or action for each customer in each moment and channel. Combine ML scoring, eligibility rules, and live context for next-best-action decisioning at scale.

10ms offer ranking and delivery

Payments and authorization

Authorize, route, or flag transactions with live merchant context and customer state.

99.999% availability SLA

ECOSYSTEM

Complements your existing architecture

Redis is not a rip-and-replace. It is the real-time tier that empowers your systems of record and decisioning platforms with live context and millisecond execution.

Data platforms: Snowflake, Databricks, BigQuery

Redis makes analytical intelligence operational by serving features in real time.

Decisioning systems: Pega, Adobe, SAS, homegrown

Redis accelerates existing decision logic with live context and pre-staged data.

Event streaming: Kafka, Confluent, Event Hub

Redis Streams and RDI connect streaming data to usable operational state.

AI/ML platforms: SageMaker, Vertex AI, Azure ML

Redis serves as the production feature store and LLM memory layer.

Start making decisions on live context

Work with us to see how your meaningful decisions currently perform and the difference live context can make.

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