Predictika · Tessellate Data license deals

An AI deal desk agent's license deal, checked before it goes out

An AI deal desk agent prepares a storage-software license deal for Tessellate Data, in dollars: new terms, expansions, pay-per-gig, flash reclaim and tenant commitments. Before it goes out, the agent sends it to Predictika, which checks it against a declarative model of the deal rules, compares the annual value the deal states with the computed one, and returns every finding. Tessellate Data is a fictional company and every figure is invented for illustration.

Scope: Tessellate Data storage-software license deals: new terms, expansions, pay-per-gig, flash reclaim and tenant capacity commitments. See everything this validator covers

This validator models one slice of Tessellate Data's deal desk: capacity-based software license deals for its customers. It handles any combination of customer, deal type, term, capacity, price, workload and drive model in that slice, not only the scenarios shown on this page. It does not cover support contracts, hardware, professional services or real Tessellate prices.

  • Covered: deal types — new-term licenses, expansions (with the co-term and existing-agreement checks), pay-per-gig terms, flash reclaim and tenant capacity commitments.
  • Covered: pricing — the term-and-volume discount floor, the pricing-exception band and the locked reclaim price.
  • Covered: data reduction — promised ratios against proven ratios by workload, and the free true-up capacity they risk.
  • Covered: concentration and eligibility — customer share of company CARR, the neocloud reseller program, effective license capacity and reclaim drive models.
  • Covered: the deal itself — the annual value the agent states, compared with the computed value.
  • Labeled as assumptions — Tessellate Data is fictional and every price, discount and threshold is invented for illustration.
The AI sales agentBuilds the license deal from the conversation and says it is ready.
Predictika validatorChecks every rule at once and returns the verdict, each finding, and the computed figures.

8 scenarios in 4 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
Discount floor that depends on term and volumeOnly with workarounds
The floor moves with two discount tables and the exception band, so a rules-based tool needs a price table for each term and volume tier.		The floor is computed from the term and volume of each deal, and the finding states the discounts it applied.
Expansion that must co-termOnly with care
Whether an expansion is valid depends on the months left on the customer's existing agreement, which is data outside the deal.		The customer's agreement is part of the model, so the co-term check uses the real months remaining.
Data-reduction guarantees against proven ratiosOnly with workarounds
The risk of a guarantee depends on the workload, the capacity and the gap to the proven ratio, and the limit between approval and block is a threshold on a computed figure.		The free capacity at risk is computed per deal and compared with the tolerance, so a small gap needs approval and a large one is blocked.
Customer concentrationOnly with care
Share of company revenue after the deal needs the customer's current revenue and the company total, both outside the deal.		Both figures are in the model, and the share after the deal is computed every time.
Tenant promises against effective capacityOnly with workarounds
Effective capacity depends on the workload's proven ratio and what the customer has already promised.		Licensed capacity, ratio and existing promises are combined in one check, and the finding shows all three.
Annual value the agent statesOnly with workarounds
A rule can compare a stated figure with a computed one only if the stated figure is a field on the deal, which it rarely is.		The stated annual value travels with the deal and is compared with the computed value on every call; a mismatch is its own block.

  

  

    
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.

    
1A license price must be at least the floor for its term and volume: list price less a term discount (12 percent at 36 months, 20 percent at 60) and a volume discount (8 percent from 50 PB, 15 percent from 200 PB).
2A price below the floor but within 5 percent of it can go ahead only with a recorded pricing exception, which needs approval.
3No term may run longer than 60 months.
4An expansion needs an existing agreement, and it must end with that agreement (co-term).
5Pay-per-gig terms run at least 3 months and are priced at a 25 percent premium to list; no data-reduction guarantee is offered on them.
6A data-reduction guarantee is checked against the ratio proven for the workload; a small gap needs approval and a large one is blocked, based on the free true-up capacity it risks.
7Flash reclaim needs supported drives shipped in 2021 or later and is priced at the locked $14 per TB-month.
8Tenant capacity commitments are open only to customers in the neocloud reseller program.
9A customer's tenant promises may not exceed the effective capacity of its license for the workload.
10A deal that takes a customer above 25 percent of company CARR needs CFO approval.
11The annual value the agent states must match the computed value to within about a cent per million; otherwise the deal is blocked.
12Every finding is returned together, so one problem never hides another.

  

  

    
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. Pricing: New license priced below the floor

    
The agent's request. Please prepare a 36-month new license for Solstice AI: 60 PB at $17 per TB-month. The deal states an annual value of $12.24 million. It is ready to send.

    
Verdict: Blocked.

    
    
      
  • Price below the floor. $17 per TB-month is below the $17.81 floor for this term and volume (12 percent term and 8 percent volume discount off $22 list).
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2. Pricing: New license priced at the floor

    
The agent's request. Please prepare a 36-month new license for Solstice AI: 60 PB at $18 per TB-month. The deal states an annual value of $12.96 million. It is ready to send.

    
Verdict: Allowed.

    
    
      
  • Deal summary. Annual value $12.96 million; the customer would hold 1.4 percent of company CARR after the deal.
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3. Data-reduction guarantee: Guarantee of 2:1 on model checkpoints

    
The agent's request. Please prepare a 60-month new license for Solstice AI: 100 PB at $16.5 per TB-month. It carries a 2:1 data-reduction guarantee on model checkpoints. The deal states an annual value of $19.80 million. It is ready to send.

    
Verdict: Blocked.

    
    
      
  • Data-reduction guarantee. Promising 2:1 on Model checkpoints (proven 1.3:1) means up to 53.85 PB of free true-up capacity, above the tolerance.
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4. Data-reduction guarantee: Guarantee of 1.33:1 on model checkpoints

    
The agent's request. Please prepare a 60-month new license for Solstice AI: 100 PB at $16.5 per TB-month. It carries a 1.33:1 data-reduction guarantee on model checkpoints. The deal states an annual value of $19.80 million. It is ready to send.

    
Verdict: Needs approval.

    
    
      
  • Data-reduction guarantee. Promising 1.33:1 on Model checkpoints (proven 1.3:1) risks 2.31 PB of free true-up capacity. Within tolerance; needs approval.
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  • Deal summary. Annual value $19.8 million; the customer would hold 2.1 percent of company CARR after the deal.
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5. Tenant capacity: Tenant commitment of 40 PB

    
The agent's request. Please record a tenant capacity commitment: Pinecrest Compute promises 40 PB of model checkpoints to a tenant. It is ready to send.

    
Verdict: Blocked.

    
    
      
  • Tenant promises exceed the license. Pinecrest Compute has 120 PB licensed, which is 156 PB effective for Model checkpoints. Tenant promises would reach 190 PB. A license expansion is needed first.
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6. Tenant capacity: Tenant commitment of 5 PB

    
The agent's request. Please record a tenant capacity commitment: Pinecrest Compute promises 5 PB of model checkpoints to a tenant. It is ready to send.

    
Verdict: Allowed.

    
7. Stated annual value: Stated annual value is low

    
The agent's request. Please prepare an expansion for Harbor Retail: 5 PB for 30 months at $22 per TB-month. The deal states an annual value of $1.06 million. It is ready to send.

    
Verdict: Blocked.

    
    
      
  • Stated annual value does not match. The stated annual value of $1.06M does not match the validator value of $1.32M.
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8. Stated annual value: Correct annual value stated

    
The agent's request. Please prepare an expansion for Harbor Retail: 5 PB for 30 months at $22 per TB-month. The deal states an annual value of $1.32 million. It is ready to send.

    
Verdict: Allowed.

    
    
      
  • Deal summary. Annual value $1.32 million; the customer would hold 0.8 percent of company CARR after the deal.
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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 deal desk agent prepares a storage-software license deal for Tessellate Data, in dollars: new terms, expansions, pay-per-gig, and more. Before it goes out, the agent sends it to Predictika, which checks it against a declarative model of the deal rules, compares the annual value the deal states with the computed one, and returns every finding. Tessellate Data is a fictional company and every figure is invented for illustration.
    2. 
      
  2. Each tab is a different request. We will start with the first one.
    3. 
      
  3. The agent says. Please prepare a 36-month new license for Solstice AI: 60 PB at $17 per TB-month. The deal states an annual value of $12.24 million. The rest of the request is on screen.
    4. 
      
  4. Before it goes out, the agent calls the validator.
    5. 
      
  5. Blocked, with one finding, all in one answer. $17 per TB-month is below the $17.81 floor for this term and volume (12 percent term and 8 percent volume discount off $22 list).
    6. 
      
  6. The validator returned one blocking finding 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 deal are checked against each other.
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  7. What this shows: for discount floor that depends on term and volume, a rules-based CPQ can do this only with workarounds.
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  8. Now the agent resubmits.
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  9. The corrected request. It changes the price to 18 and the stated annual value to 12.96.
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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. The earlier finding is cleared, and the validator's total, $12.96 million, matches the deal.
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  13. One problem caught in one call, fixed in one pass, and a clean deal. That is the value of Predictika's validator: catching mistakes before the deal is signed.
    14.