Redis Real-Time Decisioning Demo | B2B Pricing and Quote Decisioning

Stage 1: The Architecture

Forge Industrial's Real-Time Decisioning Stack

Five tiers form a continuous loop: ingest, context, decide, act, learn. Redis turns scattered account, inventory, margin, and policy data into a quote decisioning cockpit. Systems of record stay where they are. Redis becomes the operational context layer that makes the moment actionable.

Data Sources

CRM / CPQ Platform

Operational source

ERP / Inventory

Operational source

Contract Repository

Operational source

Pricing Analytics Lakehouse

Operational source

Partner Availability APIs

Operational source

Kafka / Seller Events

Operational source

Ingest Layer

Redis Data Integration (RDI)

CDC from repositories and operational databases with sub-second activation

Redis Feature Form + Streams

Feature serving and live event hydration from Kafka, Redis Streams, and industry APIs

Unified Context Layer

Redis RAM

Hot quote state, active deal signals, live inventory availability, and approval routing flags

Redis Flex

Warm account and deal history, relationship embeddings, competitive context, and prior win patterns

Feature Store

Win probability, discount authority, inventory fit, and margin risk features

Redis Context Retriever

Assembles the Account 360 — deal state, contract history, and pricing policy — and exposes it as structured MCP tools for the decision engine

Decision Engine

Eligibility Rules

Policy, compliance, and inventory constraints

NBA Ranker

Contextual ranking weighted by value, risk, and fit

Policy Arbitration

One action selected for the current moment

Output Channels (Act)

Seller Console

Customer-facing activation surface

Distributor Portal

Customer-facing activation surface

Field Tablet

Customer-facing activation surface

Email Workflow

Customer-facing activation surface

↺

Learn: Decision logs and outcomes stream back into model training, policy updates, and the Redis context layer.

Decision latency

10.9 ms

Gross margin lift

+4.6 pts

Approval cycle reduction

-54%

Stage 2: Trigger Event

Nora Patel creates a decision moment

A seller starts a quote for a strategic account with expiring inventory and margin pressure. Price the deal, route approvals, and recommend substitutions in the live selling moment.

Live Trigger

Nora Patel

Strategic manufacturer account | $2.1M annual spend | quarter-end quote

B2B

Eventquote_session_started

Customer surfaceSeller console

Decision objectiveWin rate + margin

Decision windowBefore the surface finishes rendering

Context requiredHistory + live signals + policy

Business stakes+4.6 pts

Why This Moment Matters

Redis turns scattered account, inventory, margin, and policy data into a quote decisioning cockpit.

Without Redis: the application waits on siloed repositories and defaults to a generic next step.

With Redis: the decision surface opens with ranked, contextual action already staged for the moment.

Stage 3: Ingest

Industry repositories and streams flow into Redis

These are common repositories and streaming APIs for b2b commerce decisioning. They remain the systems of record. Redis activates the working set needed to decide now.

Redis Data Integration (RDI)Redis Feature Form

Source Systems → Redis

SALE

Salesforce / CPQ

Common repository or streaming source for salesforce context

→

SAPO

SAP / Oracle ERP

Common repository or streaming source for sap context

→

CONT

Contract store

Common repository or streaming source for contract context

→

DATA

Databricks pricing models

Common repository or streaming source for databricks context

→

KAFK

Kafka / Redis Streams

Event streaming via Kafka or Redis Streams — no separate broker required with Redis Streams built-in

→

PART

Partner APIs

Common repository or streaming source for partner context

→

Ingest Pipeline Status

CDC modeReal-time

Streaming lag<100 ms

Feature parityTrain = serve

Custom sync code0 lines

Serving roleOperational context layer

Stage 4: Context Assembly

History and live state converge into one working view

Redis assembles account history, deal and contract state, live intent, and pricing policy constraints in one low-latency lookup path. What looked like a simple event in Stage 2 becomes a much richer decision moment here.

Redis RAMRedis FlexRedis Context Retriever

Historical Context

Customer value bandavailable

Tenure / relationship depthEstablished

Prior interaction patternKnown baseline available

Eligibility stateResolved in memory

Policy constraintsCurrent and active

Action historyFrequency caps enforced

Live Context

Current intentWin rate + margin

Streaming signal stateFresh for this session

Capacity / inventoryAvailable

Risk / complianceWithin active rules

Surface readinessSeller console

Decision scopeBundled quote with premium SLA

Key insight: Redis Context Retriever surfaces the Account 360 — deal state, contract history, and pricing policy — via MCP tools the decision engine calls directly. The winning action becomes obvious only when historical context and live signals appear in the same response path.

Stage 5: Feature Serving

Online features hydrate the ranker in milliseconds

Redis Feature Form serves model-ready signals from Redis with train-serve parity. No fan-out calls to downstream systems during decisioning. No stale fallbacks.

Redis Feature FormRedis Feature Store

account_growth_score

Model or ranking signal pulled online at decision time.

0.860.3 ms

deal_margin_floor

Business value weighting for this moment.

maintained0.4 ms

inventory_fit

Real-time feature served from Redis with train-serve parity.

good0.5 ms

sla_capacity

Real-time feature served from Redis with train-serve parity.

available0.6 ms

discount_elasticity

Real-time feature served from Redis with train-serve parity.

moderate0.7 ms

approval_risk

Risk and policy factor used to gate the action.

low0.8 ms

Feature Serving Performance

Features Hydrated

186

P99 Lookup Latency

<15ms

Train / Serve Parity

100%

Stage 6: Ranking

Candidate actions are ranked for this moment

Rules, policies, vector similarity, and business weighting combine to rank the best action now. The winner is selected because it fits the moment, not because it is the easiest generic fallback.

Redis SearchNBA RankerRules Engine

#1 Winner

PRIMARY ACTION

Bundled quote with premium SLA

Best probability-adjusted margin with available stock

NBA score0.94

#2 Candidate

ALTERNATIVE

Deep-discount line-item quote

Higher immediate win probability but weak gross margin

NBA score0.79

Suppressed

POLICY

Backordered configuration

Suppressed because supply and delivery SLAs cannot be met

Suppressed0.24

Stage 7: Business Impact

The value of choosing the right action now

Fast decisions matter, but the real value is choosing the right action while the moment is still open. Redis improves both latency and outcome quality.

Decision Economics

Decision latency10.9 ms

Gross margin lift+4.6 pts

Approval cycle reduction-54%

The key insight: value compounds when the decision surface uses live operational context instead of generic fallback logic.

Moment Outcome

Generic

Siloed systems
lower relevance

→

Redis

Context-aware
higher impact

Stage 8: Outcome

Same surface. Different decision layer.

On the left is a generic or delayed path. On the right is the Redis-powered experience with ranked action already staged. Same customer moment. Different outcome.

Generic Experience

Nora Patel

B2B Commerce workflow

What the system knows

Partial profile, delayed retrieval, and limited live context.

Action shown

Deep-discount line-item quote

Outcome risk

The action fits broadly, but not the full moment.

Lower

relevance

Higher

fallback use

Siloed

context

Redis-Powered Experience

Nora Patel

Decision-ready profile

#1 Next Best Action

Bundled quote with premium SLA

Best probability-adjusted margin with available stock

Why it wins

Combines history, live signals, business value, and active policy constraints in the same low-latency response.

Visible impact

Gross margin lift: +4.6 pts.

High

relevance

Low

fallback use

Ready

before render

Stage 9: The Architecture, Proven

Architecture first. Architecture last. Outcome in the middle.

The same five tiers you saw at the start now tie directly to a measurable decision outcome. Common repositories and streaming APIs stay in place. Redis remains the operational decisioning layer that makes the customer moment work.

Data Sources

CRM / CPQ Platform

Operational source

ERP / Inventory

Operational source

Contract Repository

Operational source

Pricing Analytics Lakehouse

Operational source

Partner Availability APIs

Operational source

Kafka / Seller Events

Operational source

Ingest Layer

RDI

CDC and activation from systems of record

Streams + Features

Live event hydration and model parity

Unified Context Layer

Redis RAM

Hot quote state, active deal signals, live inventory availability, and approval routing flags

Redis Flex

Warm account and deal history, relationship embeddings, competitive context, and prior win patterns

Feature Store

Win probability, discount authority, inventory fit, and margin risk features

Redis Context Retriever

Assembles the Account 360 — deal state, contract history, and pricing policy — and exposes it as structured MCP tools for the decision engine

Decision Engine

Rules

Eligibility, compliance, and policy

Ranker

Propensity, value, and fit

Arbitration

One action selected now

Output Channels

Seller Console

Customer-facing activation surface

Distributor Portal

Customer-facing activation surface

Field Tablet

Customer-facing activation surface

Email Workflow

Customer-facing activation surface

↺

Learn: Outcomes feed retraining, policy updates, and future decisions without changing the systems of record.

Decision latency

10.9 ms

Gross margin lift

+4.6 pts

Approval cycle reduction

-54%