Predictika · Data platform subscription and consumption quotes

An AI sales agent's data platform subscription quote, checked before it goes out

An AI sales agent prepares a subscription and consumption quote for an enterprise data platform customer: public cloud services priced by compute-unit hours, or a private cloud (on-premises) annual subscription, with a term, a discount, a support tier and committed credits. Before it goes out, the agent sends it to Predictika, which checks it against a declarative model of the deployment, commit and approval rules and returns a verdict, every reason and the estimate it computed.

Scope: enterprise data platform subscription and consumption quotes: cloud versus private cloud deployment, compute-unit hour consumption, committed credits, support tier and multi-year terms. See everything this validator covers

This validator models subscription and consumption quotes for an enterprise data platform sold either as hourly cloud services or as an annual private cloud subscription — not just the scenarios shown below.

  • Deployment — public cloud services priced per compute-unit hour (warehouse, data engineering, operational database, machine learning and data hub), and private cloud annual subscription items (visualization users, GPU units, observability). The two kinds cannot be mixed on one quote.
  • Committed credits — the minimum commitment, and how well the committed credits cover the estimated annual consumption (too little leaves overage exposure, too much leaves unused credits).
  • Term and discount — committed terms of 1 to 3 years; what a sales rep can discount for each term, what needs management approval and what no one can approve.
  • Support tier — which support tiers a given annual size qualifies for.
  • Stated figures — the annual consumption estimate the quote states is compared with the computed one.

Any subscription or consumption quote for these deployment types that touches services, hours, term, discount, support or committed credits is in scope — not only the scenarios pictured here. The validator computes an annual estimate from published list rates, but it produces no line-item quote and no price for the private cloud base platform, which is not public. Not covered: negotiated rates, other services and add-ons, taxes and any vendor CPQ platform. Assumptions: the minimum commitment, discount limits, support tier thresholds and commit-coverage bands are invented illustrative values; list rates are estimates, not quotes.

The AI sales agentBuilds the subscription quote from the conversation and says it is ready.
Predictika validatorChecks every rule at once and returns the verdict, each finding, and the computed annual estimate.

7 scenarios in 3 groups · each group runs from the agent’s first attempt to the outcome · click one to switch

What the agent proposes

The AI sales agent says

Illustrative agent statement. The validator receives the structured request below.

Proposing agent: AI sales agent (scripted)

Before sending, the agent must call Recorded run

Predictika validator


      

        
        
        
      

      
Waiting for the call.

      
    

  

  
  

    
Where rules-based CPQ gets it wrong
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Our assessment of what a typical rules-based CPQ (product rules, price rules and validation rules, as in Salesforce CPQ) does with the same cases. It is a judgment about effort and reliability, not a claim that nothing can be built.

    
What the validator caughtTypical rules-based CPQPredictika
Per-hour rates for several services rolled into an annual estimateOnly with care
A price rule per service works, but every service or rate change adds rules, and the annual estimate is easy to get out of step with the rate table.		Rates are inputs to one calculation; the estimate is computed on every call.
A stated consumption estimate that differs from the computed oneOnly with care
A validation rule can compare them, but only if the stated figure is a field on the quote, which it rarely is.		The stated estimate travels with the quote and is compared with the computed estimate on every call.
Cloud and private cloud items that must not be mixed on one quoteOnly with workarounds
Mixing is often caught only by separate product rules per deployment, and a rep can still assemble an invalid bundle.		The deployment decides which items are allowed, and every offending item is reported together.
Committed credits sized against estimated consumption, with a band that needs approvalOnly with workarounds
The coverage ratio needs a computed field and two thresholds, usually built in custom code or outside the quote.		Coverage is computed from the commit and the estimate; below, inside and above the bands give block, approval or allow.
Every problem returned in one answerOnly with workarounds
Rules tend to fire as the quote is saved or a step is completed, so a rep often fixes one error and meets the next.		All findings come back together, whatever order the choices arrived in, ordered by severity.

  

  

    
What this validator catches: findings from its test cases
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Every rule is checked on every call, in any order the choices arrive; one problem never hides another. Blocking findings stop the quote; approval findings need a person to sign off; everything else is computed.

    
1Committed credits of $500,000 cover only 44.2 percent of the estimated annual consumption of $1,131,600; below 60 percent the commit is not sized to the workload.
2The quote states an estimated annual consumption of $1,230,000, but the computed estimate is $1,131,600.
3Hourly CCU cloud services cannot be quoted on CDP Private Cloud (annual on-premises subscription).
4On-premises annual subscription items (Data Visualization, GPU Acceleration, Observability) cannot be quoted on CDP Public Cloud (hourly CCU services).
5The quote has no priced services; add cloud CCU hours or on-premises subscription items.
6Committed credits of $40,000 are below the $50,000 minimum annual commitment for CDP Public Cloud (hourly CCU services).
7A 5 year term is outside the 1 to 3 year range for committed terms.
8A 22 percent discount is above the 15 percent limit that anyone can approve.
9Support tier 3 is not available at an estimated annual consumption of $200,000 (tier 2 needs $100,000 or more, tier 3 needs $250,000 or more; 1 Business, 2 Enterprise, 3 Premier).
10Committed credits of $151,000 are 151 percent of the estimated annual consumption of $100,000; above 150 percent the commit is not sized to the workload.
11A 12 percent discount is above the 10 percent a sales rep can give on a 2 year term; it needs sales management approval.
12Committed credits cover 60 percent of the estimated annual consumption; below 80 percent leaves overage exposure, so confirm sizing with the customer.
13Committed credits are 121 percent of the estimated annual consumption; above 120 percent risks unused credits, so confirm sizing with the customer.

  

  

    
Scenarios and results
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The recorded result of the validator for each scenario on this page: the request the AI agent sent, the verdict, and every finding, in the order they appear above.

    
1. Large public cloud commit: Large public cloud quote with committed credits well below the estimate and a wrong stated estimate

    
The agent's request. Please quote a two-year public cloud subscription: 8,000,000 CCU hours of Data Warehouse, 5,000,000 CCU hours of Data Engineering Core, 1,000,000 CCU hours of Machine Learning and 3,000,000 CCU hours of Data Hub, with Premier support and an 8 percent discount, and committed credits of $500,000 a year. The quote states an estimated annual consumption of $1,230,000. It is ready to send.

    
Verdict: Blocked.

    
    
      
  • Committed credits too low. Committed credits of $500,000 cover only 44.2 percent of the estimated annual consumption of $1,131,600; below 60 percent the commit is not sized to the workload.
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  • Stated estimate does not match. The quote states an estimated annual consumption of $1,230,000, but the computed estimate is $1,131,600.
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2. Large public cloud commit: The agent resubmits after fixing the errors

    
The agent's request. Please quote a two-year public cloud subscription: 8,000,000 CCU hours of Data Warehouse, 5,000,000 CCU hours of Data Engineering Core, 1,000,000 CCU hours of Machine Learning and 3,000,000 CCU hours of Data Hub, with Premier support and an 8 percent discount, and committed credits of $1,131,600 a year. The quote states an estimated annual consumption of $1,131,600. It is ready to send.

    
Verdict: Allowed.

    
    
      
  • Estimate summary. CDP Public Cloud (hourly CCU services) estimated annual consumption at public list rates: cloud services $1,230,000, on-premises subscriptions $0, gross $1,230,000, after 8 percent discount $1,131,600; committed credits $1,131,600 (100 percent of the estimate); term 2 years.
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3. Private cloud subscription: Private cloud subscription items together with hourly cloud hours

    
The agent's request. Please quote a two-year private cloud annual subscription: 500,000 CCU hours of Data Warehouse, 40 Data Visualization users, 6 GPU Acceleration units and 1,600 Observability CCUs, with Premier support. The quote states an estimated annual consumption of $253,000. It is ready to send.

    
Verdict: Blocked.

    
    
      
  • Cloud hours on a private cloud quote. Hourly CCU cloud services cannot be quoted on CDP Private Cloud (annual on-premises subscription).
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  • Stated estimate does not match. The quote states an estimated annual consumption of $253,000, but the computed estimate is $288,000.
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4. Private cloud subscription: The agent resubmits after fixing the errors

    
The agent's request. Please quote a two-year private cloud annual subscription: 40 Data Visualization users, 6 GPU Acceleration units and 1,600 Observability CCUs, with Premier support. The quote states an estimated annual consumption of $253,000. It is ready to send.

    
Verdict: Allowed.

    
    
      
  • Estimate summary. CDP Private Cloud (annual on-premises subscription) estimated annual consumption at public list rates: cloud services $0, on-premises subscriptions $253,000, gross $253,000, no discount, net $253,000; committed credits $0 (0 percent of the estimate); term 2 years.
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5. Term and discount: Mid-size public cloud quote with a 5-year term and a 22 percent discount

    
The agent's request. Please quote a five-year public cloud subscription: 3,000,000 CCU hours of Data Warehouse, 2,000,000 CCU hours of Operational Database, 400,000 CCU hours of Machine Learning and 1,500,000 CCU hours of Data Hub, with Enterprise support and a 22 percent discount, and committed credits of $400,000 a year. The quote states an estimated annual consumption of $397,800. It is ready to send.

    
Verdict: Blocked.

    
    
      
  • Term outside 1 to 3 years. A 5 year term is outside the 1 to 3 year range for committed terms.
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  • Discount above the maximum. A 22 percent discount is above the 15 percent limit that anyone can approve.
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6. Term and discount: The agent resubmits after fixing the errors

    
The agent's request. Please quote a two-year public cloud subscription: 3,000,000 CCU hours of Data Warehouse, 2,000,000 CCU hours of Operational Database, 400,000 CCU hours of Machine Learning and 1,500,000 CCU hours of Data Hub, with Enterprise support and a 12 percent discount, and committed credits of $400,000 a year. The quote states an estimated annual consumption of $448,800. It is ready to send.

    
Verdict: Needs approval.

    
    
      
  • Discount needs approval. A 12 percent discount is above the 10 percent a sales rep can give on a 2 year term; it needs sales management approval.
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  • Estimate summary. CDP Public Cloud (hourly CCU services) estimated annual consumption at public list rates: cloud services $510,000, on-premises subscriptions $0, gross $510,000, after 12 percent discount $448,800; committed credits $400,000 (89.1 percent of the estimate); term 2 years.
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7. Term and discount: The agent resubmits after fixing the errors

    
The agent's request. Please quote a two-year public cloud subscription: 3,000,000 CCU hours of Data Warehouse, 2,000,000 CCU hours of Operational Database, 400,000 CCU hours of Machine Learning and 1,500,000 CCU hours of Data Hub, with Enterprise support and a 10 percent discount, and committed credits of $460,000 a year. The quote states an estimated annual consumption of $459,000. It is ready to send.

    
Verdict: Allowed.

    
    
      
  • Estimate summary. CDP Public Cloud (hourly CCU services) estimated annual consumption at public list rates: cloud services $510,000, on-premises subscriptions $0, gross $510,000, after 10 percent discount $459,000; committed credits $460,000 (100.2 percent of the estimate); term 2 years.
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Narration transcript
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The text of the narrated walkthrough, step by step.

    
    
      
  1. Welcome to a demo of Predictika's Validator. An AI sales agent prepares a subscription and consumption quote for an enterprise data platform customer: public cloud services priced by compute-unit hours, a private cloud (on-premises) annual subscription. Before it goes out, the agent sends it to Predictika, which checks it against a declarative model of the deployment, commit and approval rules and returns a verdict, every reason and the estimate it computed.
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  2. I will start with the first request by the agent.
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  3. The agent says. Please quote a two-year public cloud subscription: 8,000,000 CCU hours of Data Warehouse, 5,000,000 CCU hours of Data Engineering Core. The quote states an estimated annual consumption of $1,230,000. The rest of the request is on screen.
    4. 
      
  4. Before it goes out, the agent calls the validator.
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  5. Blocked, with two findings, all in one answer. Committed credits of $500,000 cover only 44.2 percent of the estimated annual consumption of $1,131,600; below 60 percent the commit is not sized to the workload. The quote states an estimated annual consumption of $1,230,000, but the computed estimate is $1,131,600.
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  6. The validator returned two blocking findings in one answer. A check that reads one field at a time would pass most of this; it shows only when the choices in the quote are checked against each other.
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  7. What this shows: for a stated consumption estimate that differs from the computed one, a rules-based CPQ is error-prone for this.
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  8. Now the agent resubmits.
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  9. The corrected request. It changes the committed credits to $1,131,600 and the stated annual estimate to $1,131,600.
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  10. It calls the validator again.
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  11. This time the validator clears it.
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  12. Both earlier findings are cleared, and the validator's total, $1,131,600, matches the quote.
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  13. Two problems caught in one call, fixed in one pass, and a clean quote. That is the value of Predictika's validator: catching mistakes before they reach the customer. Now you can try the other scenarios yourself.
    14.