Predictika · Automated case picking project quotes

An AI sales agent's automated case picking quote, checked before it goes out

An AI sales agent prepares a project quote for an automated case picking system built on Case Order Machines, with the number of machines and the hours they run, the layer depalletizers, the daily case volume, the SKU count, the temperature zones, the load carrier and its height, the omni-channel and AI options, the site and the picking capacity the quote states. Before it goes out, the agent sends it to Predictika, which checks it against a declarative model of the system rules and returns a verdict, every reason and the capacity it computed.

Scope: Automated case picking project quotes: Case Order Machine capacity, layer depalletizing, daily volume and SKU range, dry, chilled and frozen zones, pallets and roll containers, the omni-channel and AI options, the site and the picking capacity the quote states. See everything this validator covers

This validator models automated case picking project quotes, not just the scenarios shown below.

  • Capacity — the picking capacity of the Case Order Machines and the layer depalletizers against the daily case volume, the per-site volume limit and the picking capacity the quote states.
  • Configuration — the dry, chilled and frozen zones and the cold-rated package, pallets and roll containers with their load heights, and the omni-channel and AI options.
  • Referrals — a person reviews three-zone designs, an existing building, and volumes or SKU counts outside the usual range.

Any case picking quote that touches these rules is in scope — not only the scenarios pictured here. Not covered: the other WITRON systems, services and prices, because WITRON quotes each project individually and this validator models no prices. The product names and the machine rate follow public WITRON pages; the depalletizer capacity, the height limits, the option rules and the approval thresholds are modeled from comparable systems and are not published by WITRON.

The AI sales agentBuilds the project quote 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 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
Capacity that depends on machines and hoursOnly with care
The capacity is the machines times the rate times the hours, and a separate depalletizer limit applies to the same volume, so two formulas must be compared with the volume.		The machines, hours and depalletizers are fields of one request and both capacities are computed and compared on every call.
Options that depend on the carrier and the machine countOnly with care
The omni-channel option needs roll containers and the AI option needs two machines, so each option rule must read another field.		Every option is checked against the carrier and the machine count on every call.
A frozen zone that needs a cold packageOnly with workarounds
A zone choice and a package choice are made separately, so a rule must read both and the order of the choices must not matter.		Both choices are compared on every call, whatever order they were made in.
A stated capacity that must match the machinesOnly with workarounds
The capacity is computed from the machines and the hours, so a separate rule must recompute it and compare it with the stated figure.		The stated capacity is a field of the request and is compared with the computed capacity on every call.
Referrals for zones, sites and unusual volumesOnly with workarounds
A review depends on the zones, the site, the volume and the SKU count together, on top of the blocking rules.		The referrals are separate findings returned together with the blocking ones.

  

  

    
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.

    
1The customer needs 40,001 cases per day; the quoted Case Order Machines give 40,000 cases per day.
2The customer needs 65,000 cases per day; the quoted layer depalletizers handle 60,000 cases per day.
3600,000 cases per day is above the 570,000 cases per day modeled for one OPM site; split the volume across sites.
4The quote states 45,000 cases per day of picking capacity; the quoted Case Order Machines give 40,000.
5The quote selects no temperature zone (dry, chilled or frozen).
6A frozen zone needs the cold-rated equipment package; the quote does not include it.
7The quote spans dry, chilled and frozen zones; a person should review the multi-zone design.
8The requested order load height is 201 cm; this carrier allows at most 200 cm.
9The requested order load height is 181 cm; this carrier allows at most 180 cm.
10The omni-channel option is modeled for roll-container loading only; the quote uses pallets.
11The AI option needs at least 2 Case Order Machines; the quote has 1.
1210,000 cases per day is below the 15,000 cases per day at which OPM is usually applied; a person should confirm OPM is the right system.
13The OPM is quoted into an existing building; a person should confirm the site survey and building limits.
1414,999 cases per day is below the 15,000 cases per day at which OPM is usually applied; a person should confirm OPM is the right system.
15299 SKUs is below the 300 SKUs at which OPM is usually applied; a person should confirm OPM is the right system.
1610,001 SKUs is above the usual OPM range of 10,000 SKUs; a person should review the tray-warehouse design.
17The customer needs 90,000 cases per day; the quoted Case Order Machines give 60,000 cases per day.
18The customer needs 90,000 cases per day; the quoted layer depalletizers handle 60,000 cases per day.
19The quote states 70,000 cases per day of picking capacity; the quoted Case Order Machines give 60,000.
20The customer needs 50,000 cases per day; the quoted Case Order Machines give 30,000 cases per day.
21The requested order load height is 210 cm; this carrier allows at most 200 cm.

  

  

    
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. Three-zone system for 50,000 cases per day: A three-zone system for 50,000 cases per day with one Case Order Machine, the AI and omni-channel options and a frozen zone

    
The agent's request. Please quote an automated case picking system with 1 Case Order Machine running 20 hours a day and 1 layer depalletizer for 50,000 cases per day across 2,500 SKUs, with dry, chilled and frozen zones, loading pallets to a height of 190 cm, on a greenfield site. It includes the omni-channel option and the AI option. The picking capacity stated on the quote is 50,000 cases per day. It is ready to send.

    
Verdict: Blocked.

    
    
      
  • COM capacity below daily volume. The customer needs 50,000 cases per day; the quoted Case Order Machines give 10,000 cases per day.
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  • Stated capacity does not match. The quote states 50,000 cases per day of picking capacity; the quoted Case Order Machines give 10,000.
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  • Frozen zone needs cold-rated equipment. A frozen zone needs the cold-rated equipment package; the quote does not include it.
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  • Omni-channel needs roll containers. The omni-channel option is modeled for roll-container loading only; the quote uses pallets.
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  • AI option needs two COMs. The AI option needs at least 2 Case Order Machines; the quote has 1.
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  • Three temperature zones. The quote spans dry, chilled and frozen zones; a person should review the multi-zone design.
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2. Three-zone system for 50,000 cases per day: The agent resubmits after fixing the errors

    
The agent's request. Please quote an automated case picking system with 3 Case Order Machines running 20 hours a day and 1 layer depalletizer for 50,000 cases per day across 2,500 SKUs, with dry, chilled and frozen zones, loading roll containers to a height of 175 cm, on a greenfield site. It includes the cold-rated package, the omni-channel option and the AI option. The picking capacity stated on the quote is 50,000 cases per day. It is ready to send.

    
Verdict: Blocked.

    
    
      
  • COM capacity below daily volume. The customer needs 50,000 cases per day; the quoted Case Order Machines give 30,000 cases per day.
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  • Stated capacity does not match. The quote states 50,000 cases per day of picking capacity; the quoted Case Order Machines give 30,000.
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  • Three temperature zones. The quote spans dry, chilled and frozen zones; a person should review the multi-zone design.
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3. Three-zone system for 50,000 cases per day: The agent resubmits after fixing the errors

    
The agent's request. Please quote an automated case picking system with 5 Case Order Machines running 20 hours a day and 1 layer depalletizer for 50,000 cases per day across 2,500 SKUs, with dry, chilled and frozen zones, loading roll containers to a height of 175 cm, on a greenfield site. It includes the cold-rated package, the omni-channel option and the AI option. The picking capacity stated on the quote is 50,000 cases per day. It is ready to send.

    
Verdict: Needs approval.

    
    
      
  • Three temperature zones. The quote spans dry, chilled and frozen zones; a person should review the multi-zone design.
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  • Capacity summary. 5 Case Order Machine(s) give 50,000 cases per day over 20 hours; 3 temperature zone(s).
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4. Large volume of 90,000 cases per day: A system for 90,000 cases per day with six Case Order Machines and one depalletizer

    
The agent's request. Please quote an automated case picking system with 6 Case Order Machines running 20 hours a day and 1 layer depalletizer for 90,000 cases per day across 4,000 SKUs, with the dry zone, loading pallets to a height of 190 cm, on a greenfield site. The picking capacity stated on the quote is 70,000 cases per day. It is ready to send.

    
Verdict: Blocked.

    
    
      
  • COM capacity below daily volume. The customer needs 90,000 cases per day; the quoted Case Order Machines give 60,000 cases per day.
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  • Depalletizing capacity below daily volume. The customer needs 90,000 cases per day; the quoted layer depalletizers handle 60,000 cases per day.
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  • Stated capacity does not match. The quote states 70,000 cases per day of picking capacity; the quoted Case Order Machines give 60,000.
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5. Large volume of 90,000 cases per day: The agent resubmits after fixing the errors

    
The agent's request. Please quote an automated case picking system with 10 Case Order Machines running 20 hours a day and 2 layer depalletizers for 90,000 cases per day across 4,000 SKUs, with the dry zone, loading pallets to a height of 190 cm, on a greenfield site. The picking capacity stated on the quote is 100,000 cases per day. It is ready to send.

    
Verdict: Allowed.

    
    
      
  • Capacity summary. 10 Case Order Machine(s) give 100,000 cases per day over 20 hours; 1 temperature zone(s).
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6. Existing building and load height: A system for an existing building with a 210 cm load

    
The agent's request. Please quote an automated case picking system with 3 Case Order Machines running 20 hours a day and 1 layer depalletizer for 28,000 cases per day across 800 SKUs, with the dry zone, loading pallets to a height of 210 cm, in an existing building. The picking capacity stated on the quote is 30,000 cases per day. It is ready to send.

    
Verdict: Blocked.

    
    
      
  • Order load above carrier height. The requested order load height is 210 cm; this carrier allows at most 200 cm.
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  • Existing building needs site survey. The OPM is quoted into an existing building; a person should confirm the site survey and building limits.
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7. Existing building and load height: The agent resubmits after fixing the errors

    
The agent's request. Please quote an automated case picking system with 3 Case Order Machines running 20 hours a day and 1 layer depalletizer for 28,000 cases per day across 800 SKUs, with the dry zone, loading pallets to a height of 195 cm, in an existing building. The picking capacity stated on the quote is 30,000 cases per day. It is ready to send.

    
Verdict: Needs approval.

    
    
      
  • Existing building needs site survey. The OPM is quoted into an existing building; a person should confirm the site survey and building limits.
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  • Capacity summary. 3 Case Order Machine(s) give 30,000 cases per day over 20 hours; 1 temperature zone(s).
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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 project quote for an automated case picking system built on Case Order Machines, with the number of machines and the hours they run. Before it goes out, the agent sends it to Predictika, which checks it against a declarative model of the system rules and returns a verdict, every reason and the capacity 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 an automated case picking system. It includes the omni-channel option and the AI option. The rest of the request is on screen. The picking capacity stated on the quote is 50,000 cases per day.
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  4. Before it goes out, the agent calls the validator.
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  5. Blocked, with five findings and one approval, all in one answer. The customer needs 50,000 cases per day; the quoted Case Order Machines give 10,000 cases per day. The quote states 50,000 cases per day of picking capacity; the quoted Case Order Machines give 10,000. A frozen zone needs the cold-rated equipment package; the quote does not include it. The other two are on screen.
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  6. The validator returned five blocking findings and one approval 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 frozen zone that needs a cold package, 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 Case Order Machines to 3, the load carrier to Roll Container, the load height to 175 cm and the cold-rated package to yes.
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  10. It calls the validator again.
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  11. Still blocked, with two findings and one approval, all in one answer. The customer needs 50,000 cases per day; the quoted Case Order Machines give 30,000 cases per day. The quote states 50,000 cases per day of picking capacity; the quoted Case Order Machines give 30,000.
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  12. Three findings cleared. Still blocking: COM capacity below daily volume and stated capacity does not match. One approval also remains for a person: three temperature zones.
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  13. The agent resubmits once more.
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  14. The revised request. It changes the Case Order Machines to 5.
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  15. It calls the validator once more.
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  16. Not blocked, but held for a person.
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  17. The two blocking findings are cleared; one approval remains for a person: three temperature zones.
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  18. Six problems caught in one call, fixed in two passes, and the corrected quote now goes to a person for approval. That is the value of Predictika's validator: catching mistakes before they reach the customer. Now you can try the other scenarios yourself.
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