Show how Redis helps commercial teams assemble the live deal context needed to price, route, and approve quotes while the buying moment is still active.
B2B sellers already have CRM data, CPQ, contract history, inventory, and margin policy. The problem is that they do not come together fast enough in the live quote moment. Sellers fall back to generic discounts, slow approval chains, and avoidable margin leakage.
This is built for sales, pricing, CPQ, and revenue operations 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: CRM / CPQ platform, ERP and inventory, contract repository, pricing policy engine, feature lakehouse, and Kafka quote 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 Nora Patel, and a decisioning pipeline that turns scattered operational context into the winning action: Bundled quote with premium SLA.
This is the full architecture for what you are about to see. At the top are the systems of record and live signal sources — the CRM and CPQ platform, ERP and inventory, contract repository, pricing analytics lakehouse, partner availability APIs, and Kafka seller events. None of these go away. They stay exactly where they are.
The ingest layer has two jobs. RDI handles change data capture and operational sync from the core repositories — always-fresh context with no custom pipeline code. Redis FeatureForm handles the feature pipeline from the analytical and streaming systems into the context layer, with full train-serve parity. Two tools, two roles, one unified ingest layer.
The context layer is the operational working set. Hot deal state and live session signals stay in Redis RAM for sub-millisecond access. Larger account history, relationship embeddings, and warm competitive context sit in Redis Flex. Redis Context Retriever connects those stores to the decision engine as the semantic access layer — it assembles the Account 360 — deal state, contract history, and pricing policy — and exposes it as structured MCP tools the decision engine can call directly.
The decision engine is where eligibility rules, ML ranking, and policy arbitration come together. The output channels are where the seller 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 Nora Patel. Strategic manufacturer account, two-point-one million in annual spend, and a quarter-end quote session just opened on the seller console. This moment has to resolve before the console finishes rendering.
This is not an edge case. This is the repeatable decision moment Forge Industrial handles every day across hundreds of active deals. The system has to be fast enough that the seller walks into the conversation with the right quote already staged — not a generic discount or a configuration that cannot ship.
If the system waits too long, the seller improvises. If it acts on partial context, it surfaces a deep-discount line-item quote when the account qualifies for a bundled premium configuration. Either way, Forge leaves margin on the table. If the system decides in time with full context — account history, live inventory, margin policy, and contract terms all assembled together — it captures higher win rates, shorter approval cycles, and healthier gross margin on strategic deals.
We have one live moment to recognize Nora Patel correctly and act before the old process falls back to something generic.
Forge Industrial keeps everything you see at the top of this architecture. The CRM and CPQ platform, SAP and Oracle ERP, the contract store, Databricks pricing models, Kafka seller events, and partner APIs all stay exactly where they are. Redis is not the new system of record. Redis Iris is the context engine — the operational serving layer that makes those existing systems act together in the live quote window.
The ingest layer has two jobs. RDI handles change data capture from the CRM, ERP, and contract repositories — always-fresh context sync with no custom pipeline code required. Redis FeatureForm handles the feature pipeline from the pricing analytics lakehouse and streaming systems into the online feature store, with full train-serve parity. Two tools, clear separation of concerns, one unified ingest layer.
The result is a working set that is always current. Not a nightly batch. Not a stale snapshot. Milliseconds behind the source.
Redis does not replace the existing stack. RDI and Featureform make that stack operational in the live decision window.
This is the heart of the decisioning stack. The left panel is Nora Patel's account — customer value band, relationship tenure, prior interaction pattern, eligibility state, contract constraints, and frequency cap history. The right panel is what is happening right now in this session — current intent, live inventory state, capacity availability, risk and compliance check, and surface readiness.
Most systems have one or the other. They can look up an account record. Or they can capture a live quote event. The gap is serving both together at request time, inside the latency budget.
Redis Context Retriever is what makes that possible. It assembles the Account 360 — deal state, contract history, and active pricing policy — and surfaces it as structured tools the decision engine can call directly. No fan-out queries, no manual joins across repositories. History without live state is stale. Live state without history is shallow. Redis is the layer that serves both in the same response path.
A profile tells you who the customer is. Context tells you what the business should do next.
You are looking at six features served live from Redis in under a millisecond each — account growth score, deal margin floor, inventory fit, SLA capacity, discount elasticity, and approval risk. One hundred eighty-six features total across this decision path. P99 lookup latency under fifteen milliseconds.
The point is not the feature names themselves. The point is that these are the same features used to train the model, served online at decision time with the same definitions and the same logic. That is train-serve parity. Most teams can train a model. The hard part is serving the right features fast enough in production without drift between the notebook and the live application.
Redis FeatureForm on Redis closes that gap. These features are specifically why the system chooses Bundled quote with premium SLA instead of defaulting to the deep-discount path or surfacing a backordered configuration that cannot be fulfilled.
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 Bundled quote with premium SLA, with an NBA score of 0.94. It wins because it fits this exact moment — Nora's account has a strong growth score, inventory is available for this configuration, the SLA capacity is confirmed, and the margin floor holds. High relevance, strong economics, fully within policy.
Deep-discount line-item quote scores 0.79. That is the path the legacy CPQ process typically takes because it is the simplest fallback when context is incomplete. It is not wrong — it just misses the moment. A bundled premium configuration captures the same win at materially better gross margin.
Backordered configuration is suppressed entirely. Supply and delivery SLAs cannot be met for this account's timeline. A model operating on partial context might have surfaced it. The full picture removes it before it reaches the decision engine.
Redis Search is what powers the similarity matching in this ranking step. Vector search is not a separate product you bolt on — it is a query type that Redis Search handles natively, the same way it handles full-text and numeric filtering. It is just another data type Redis can search at sub-millisecond speed.
This is not content ranking. This is quote decisioning — arbitrating across policy, margin, availability, and account fit in one low-latency response.
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.
These numbers are direct results of the architecture. Decision latency of 10.9 milliseconds means the quote brief is ready before the seller console finishes rendering. A 4.6 point gross margin lift means deals are structured around value and fit rather than defaulting to the deepest discount available. A 54 percent reduction in approval cycles means strategic accounts move faster through the pipeline without unnecessary escalation.
The value is not this single quote. It is what happens when this decision gets repeated across the full book of business — every strategic account, every product family, every quarter-end window where the system is choosing between a margin-preserving bundle and a generic fallback. That is where the math compounds.
That is also why the next step is a pilot, not a deeper technical evaluation. The question is not whether Redis is fast. The question is what one product family looks like when the quote engine runs on live context instead of stale CRM data and manual pricing lookup.
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.
Same seller. Same console. Same moment. The left side shows what happens without the context layer — partial account profile, delayed retrieval, limited live signals. The system falls back to a deep-discount line-item quote because that is the safest generic option available.
On the right, the same console opens with the right action already staged. Bundled quote with premium SLA — best probability-adjusted margin with available stock. Gross margin lift of 4.6 points is the visible result.
The product is not the UI. The UI is identical on both sides. The product is the decision layer underneath it — the one that assembled account history, live inventory, margin policy, and contract terms before the screen finished loading.
Same surface. Same moment. Different decision layer. That is the product.
This is the same architecture you saw at the start. Every tier looks the same. What is different now is that you have seen what each one contributed to the outcome.
Three takeaways. First, this is not a science project. This is a practical reference architecture that Forge Industrial can operate today. Second, it is additive — the CRM, ERP, contract repository, and pricing lakehouse stay exactly where they are. Redis sits in the operational path so those systems can act together. Third, this is a business story first. Higher quote win rates, 4.6 points of gross margin, and 54 percent fewer approval escalations are the reasons to do it — not the platform architecture.
The next step is a focused working session to map this against your actual environment. We scope one product family, one strategic segment, and one pilot that runs Redis-powered quote decisioning alongside your current CPQ routing logic. That is a clean comparison with a real KPI before you commit to broader rollout.