Predictika · Storage-software 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 (a fictional storage-software company) 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 the Tessellate Data deal desk, not just the scenarios shown below.

  • Deal types — new-term licenses, expansions (with the existing-agreement and co-term checks), pay-per-gig terms, flash reclaim and tenant capacity commitments.
  • Pricing — the floor that depends on term and volume (including the capacity already licensed on an expansion), the 5 percent pricing-exception band, the pay-per-gig premium and the locked reclaim price.
  • Data reduction and capacity — promised ratios against the ratio proven for the workload, the free true-up capacity they risk, effective license capacity against tenant promises, and the supported drive models for reclaim.
  • Concentration and the stated figure — the customer share of company CARR after the deal (above 25 percent needs CFO approval), and the annual value the agent states against the computed value.

Any deal that touches these rules is in scope, not only the scenarios pictured here. Tessellate Data is fictional and every price, discount, threshold and customer is invented for illustration; the model does not cover support contracts, hardware, services, other currencies or real vendor prices.

The AI deal desk 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.

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 deal desk agent says

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

Proposing agent: AI deal desk 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.
Fixing one issue changes the inputs of the nextOnly with workarounds
Moving an expansion to the agreement end changes its term discount and so its price floor, and a new price changes the annual value the agent stated; rules fire in a fixed order, so a rep fixes one error and meets the next.
Every rule is checked on every call, in whatever order the choices arrive, so the full list of holds returns at once and the next call shows what the fix changed.
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 deal; approval findings need a person to sign off; everything else is computed.

1$17.0 per TB-month is below the $17.81 floor for this term and volume (12.0 percent term and 8.0 percent volume discount off $22.0 list).
2$17.2 per TB-month is below the $17.81 floor but within the exception band. Needs a pricing exception.
3After this deal Aurora Grid would be 0.2966 of company CARR, above the concentration limit. Needs CFO approval.
4An expansion must end with the existing agreement, which has 44 months left; this one runs 60 months.
5Solstice AI has no agreement to expand. Write a new-term license instead.
6Pay-per-gig terms run at least 3 months; this one is 2.
7Data-reduction guarantees are not offered on pay-per-gig terms.
8Promising 1.33:1 on Model checkpoints (proven 1.3:1) risks 2.31 PB of free true-up capacity. Within tolerance; needs approval.
9Promising 2.0:1 on Model checkpoints (proven 1.3:1) means up to 53.85 PB of free true-up capacity, above the tolerance.
10Flash reclaim needs supported drives shipped in 2,021 or later; the drives named do not qualify.
11Reclaimed capacity is priced at the locked $14.0 per TB-month; $13.5 is below it.
12Pinecrest Compute has 120.0 PB licensed, which is 156.0 PB effective for Model checkpoints. Tenant promises would reach 190.0 PB. A license expansion is needed first.
13Harbor Retail is not in the neocloud program, so it cannot resell capacity to tenants.
14The stated annual value of $1.06M does not match the validator value of $1.32M.
15A 72-month term is longer than the 60-month maximum.
16Name the workload, so the promise can be checked against proven data reduction.

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. Pay-per-gig for Pinecrest Compute: A 2-month pay-per-gig term of 10 PB for Pinecrest Compute at $24.00 per TB-month, promising 2:1 on model checkpoints and stating an annual value of its own

The agent's request. Please write a 2-month pay-per-gig term for Pinecrest Compute of 10 PB at $24.00 per TB-month, promising a 2:1 data reduction on model checkpoints, stating an annual value of $3.00 million. It is ready to send.

Verdict: Blocked.

  • Pay-per-gig term too short. Pay-per-gig terms run at least 3 months; this one is 2.
  • Pay-per-gig price below the premium. Pay-per-gig is priced at no less than $27.5 per TB-month (list plus the consumption premium); this is $24.0.
  • Guarantee on pay-per-gig. Data-reduction guarantees are not offered on pay-per-gig terms.
  • Stated annual value does not match. The stated annual value of $3.0M does not match the validator value of $2.88M.

2. Pay-per-gig for Pinecrest Compute: The agent resubmits after fixing the errors

The agent's request. Please write a 3-month pay-per-gig term for Pinecrest Compute of 10 PB at $27.50 per TB-month, stating an annual value of $3.00 million. It is ready to send.

Verdict: Blocked.

  • Stated annual value does not match. The stated annual value of $3.0M does not match the validator value of $3.3M.

3. Pay-per-gig for Pinecrest Compute: The agent resubmits after fixing the errors

The agent's request. Please write a 3-month pay-per-gig term for Pinecrest Compute of 10 PB at $27.50 per TB-month, stating an annual value of $3.30 million. It is ready to send.

Verdict: Allowed.

  • Computed figures. Annual value $3.3M; customer share of company CARR after the deal 0.0701.

4. Data-reduction guarantee: A 60-month license of 100 PB for Solstice AI promising 2:1 on model checkpoints

The agent's request. Please write a new 60-month license for Solstice AI of 100 PB at $16.50 per TB-month, promising a 2:1 data reduction on model checkpoints, stating an annual value of $19.80 million. It is ready to send.

Verdict: Blocked.

  • Reduction guarantee above tolerance. Promising 2.0:1 on Model checkpoints (proven 1.3:1) means up to 53.85 PB of free true-up capacity, above the tolerance.

5. Data-reduction guarantee: The agent resubmits after fixing the errors

The agent's request. Please write a new 60-month license for Solstice AI of 100 PB at $16.50 per TB-month, promising a 1.33:1 data reduction on model checkpoints, stating an annual value of $19.80 million. It is ready to send.

Verdict: Needs approval.

  • Reduction guarantee within tolerance. Promising 1.33:1 on Model checkpoints (proven 1.3:1) risks 2.31 PB of free true-up capacity. Within tolerance; needs approval.
  • Computed figures. Annual value $19.8M; customer share of company CARR after the deal 0.0215.

6. Tenant commitment: A tenant commitment of 40 PB of model checkpoints for Pinecrest Compute

The agent's request. Please record a tenant commitment of 40 PB of model checkpoints for Pinecrest Compute.

Verdict: Blocked.

  • Tenant promises exceed effective capacity. Pinecrest Compute has 120.0 PB licensed, which is 156.0 PB effective for Model checkpoints. Tenant promises would reach 190.0 PB. A license expansion is needed first.

7. Tenant commitment: The agent resubmits after fixing the errors

The agent's request. Please record a tenant commitment of 5 PB of model checkpoints for Pinecrest Compute.

Verdict: Allowed.

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. I will start with the first request by the agent.
  3. The agent says. Please write a 2-month pay-per-gig term for Pinecrest Compute of 10 PB at $24.00 per TB-month, promising a 2:1 data reduction on model checkpoints. It is ready to send. The rest of the request is on screen.
  4. Before it goes out, the agent calls the validator.
  5. Blocked, with four findings, all in one answer. Pay-per-gig terms run at least 3 months; this one is 2. Pay-per-gig is priced at no less than $27.5 per TB-month (list plus the consumption premium); this is $24.0. Data-reduction guarantees are not offered on pay-per-gig terms. The other one is on screen.
  6. The validator returned four 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 deal are checked against each other.
  7. What this shows: for annual value the agent states, a rules-based CPQ can do this only with workarounds.
  8. Now the agent resubmits.
  9. The corrected request. It changes the term to 3 months and the price per TB-month to $27.50. It removes the workload and promised data reduction.
  10. It calls the validator again.
  11. Still blocked, with one finding, all in one answer. The stated annual value of $3.0M does not match the validator value of $3.3M.
  12. Three findings cleared; still blocked on stated annual value does not match.
  13. The agent resubmits once more.
  14. The revised request. It changes the stated annual value to 3.3 million dollars.
  15. It calls the validator once more.
  16. This time the validator clears it.
  17. The earlier finding is cleared.
  18. Four problems caught in one call, fixed in two passes, and a clean deal. That is the value of Predictika's validator: catching mistakes before the deal is signed.