Screenshot of the Shared-service demand credit experiment interactive demo
Screenshot of the interactive demo, on sample data

Shared-service demand credit experiment

Expose opportunity cost and reduce queue congestion.

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For
Managers allocating scarce internal design or analysis capacity
Solves
Internal teams submit unlimited low-priority requests because capacity has no visible tradeoff.
Delivers
Management-reviewed internal demand allocation experiment
Built in
about 4 weeks of creation time, MVP in 5 days
Investment
$25,000 for the MVP, $50,000 for the full product
Run it
Inside your business, or as part of your offer to clients
01

What it does

Expose opportunity cost and reduce queue congestion.

  1. Simulate noncash request-credit allocations.
  2. Compare service levels across teams.
  3. Track effects of a bounded voluntary pilot.
  4. Compare the reviewed result with the recorded baseline and value assumptions.
  5. Capture corrections and named-owner approval before consequential use.
  6. Export a versioned management-reviewed internal demand allocation experiment with source references and unresolved questions.

What goes in, what comes out

What the customer puts in
  • Service capacity
  • Historical requests
  • Requester budgets
  • Agreed priority constraints

AI drafts, people review. Interactive practice or facilitated workshop platform.

What the customer gets
  • Management-reviewed internal demand allocation experiment
02

How it works

The workflow

  1. In
    Start with

    Service capacity, historical requests, requester budgets and agreed priority constraints

  2. 1

    Confirm the buyer's problem and scope

  3. 2

    Collect service capacity

  4. 3

    Historical requests

  5. 4

    Requester budgets and agreed priority constraints

  6. 5

    Then follow this sequence: 1

  7. Out
    Finish with

    Management-reviewed internal demand allocation experiment

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Noncash planning credits only; managers retain exceptions and avoid employee performance scoring. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Scenario designer, Interactive replay, Evidence and debrief. Use a scenario catalog with clear goals and difficulty settings. The main session area supports text, optional voice and visible context. Follow it with a replay or decision map, annotated feedback and a next-practice plan. Facilitators can author scenarios and review participant-selected sessions. Make the task-specific outcome management-reviewed internal demand allocation experiment visible beside its evidence, review state and value baseline.

Accounts and administration

Participant-controlled session sharing, scenario versions, facilitator tools, replay history, rubric calibration, practice goals and exportable feedback. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Team updates, calendars, project records and agreed management routines. Learning portals, calendar scheduling and authorized session exports. Make recording, sharing and retention controls explicit in the product. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

03

How we build it

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

  1. 1

    Scoping call

    Day 1

    Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

  2. 2

    MVP

    5 days

    One buyer segment, one recurring use case; first modules: simulate noncash request-credit allocations; compare service levels across teams. Manual review in the loop. Built by our AI software factory.

  3. 3

    Paid pilot

    6 days

    Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

  4. 4

    Full product

    10 days

    Self-serve onboarding, billing, monitoring and the wider integration set.

  5. 5

    Run and improve

    Monthly

    We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

  1. Pick the riskiest assumption. Here: will managers allocating scarce internal design or analysis capacity use it to solve "internal teams submit unlimited low-priority requests because capacity has no visible tradeoff"?
  2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
  3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Lead time for accepted priority work and abandoned low-value demand, including administration time.
  4. Measure, then decide. Track lead time for accepted priority work and abandoned low-value demand and including administration time; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: Noncash planning credits only; managers retain exceptions and avoid employee performance scoring. Implement one approved input format, a bounded representative case set and the first two task modules: simulate noncash request-credit allocations; compare service levels across teams. Support the third module with operator review: track effects of a bounded voluntary pilot. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around management-reviewed internal demand allocation experiment. Retain the explicit scope boundary: Noncash planning credits only; managers retain exceptions and avoid employee performance scoring.

What the build depends on. Scenario state management, coherent dialogue, explicit rubrics, session replay and reviewer feedback. Voice interaction adds latency and audio QA requirements. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: Noncash planning credits only; managers retain exceptions and avoid employee performance scoring.

04

Investment

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

  1. Phase 1

    MVP

    One buyer segment, one recurring use case; first modules: simulate noncash request-credit allocations; compare service levels across teams. Manual review in the loop.

    $25,000 · about 5 days of creation time

  2. Phase 2

    Paid pilot

    Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

    $10,500 · about 6 days of creation time

  3. Phase 3

    Full product

    Self-serve onboarding, billing, monitoring and the wider integration set.

    $14,500 · about 10 days of creation time

Indicative total, MVP to full product$50,000about 4 weeks of creation time · start with the MVP from $25,000

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

StageHosting and infrastructureAI usageTotal per month
MVP and paid pilotabout 3 customers$30–$60$50–$110$80–$170
Full productabout 50 customers$110–$210$420–$840$530–$1,050
05

Run it or resell it

Internally

For your own team

Managers allocating scarce internal design or analysis capacity run it inside the business: service capacity, historical requests, requester budgets and agreed priority constraints in, management-reviewed internal demand allocation experiment out, reviewed by your people.

For your clients

As part of your offer

Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.

Your brand, or this one

Run it under your own brand, or start from this concept style.

  • primary#275891
  • accent#c98f54
  • surface#e4eaf1
  • ink#22201e
Headings
Playfair Display
Text
Source Sans 3
Voice
Practical, organised, candid
Selling it to your own clients: the go-to-market playbook

Pricing to test

Test USD 300-1,500 for a facilitated team pilot, or USD 20-80 per participant monthly for self-serve practice with limited usage. Bespoke workshops and expert coaching are separately scoped. Pricing is hypothetical. Package the initial sale as one bounded management-reviewed internal demand allocation experiment. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Expose opportunity cost and reduce queue congestion. Demonstrate a concrete management-reviewed internal demand allocation experiment using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Managers allocating scarce internal design or analysis capacity professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample management-reviewed internal demand allocation experiment from a small authorized input set, with a transparent calculation of lead time for accepted priority work and abandoned low-value demand, including administration time and no promised savings.

The first 30 days

  1. Week 1: interview five managers allocating scarce internal design or analysis capacity and inspect a recent example of internal teams submit unlimited low-priority requests because capacity has no visible tradeoff.
  2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
  3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
  4. Week 4: measure lead time for accepted priority work and abandoned low-value demand, including administration time, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Lead time for accepted priority work and abandoned low-value demand, including administration time. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Lead time for accepted priority work and abandoned low-value demand, including administration time; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs management-reviewed internal demand allocation experiment. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

Realistic domain scenarios, qualified facilitator relationships and reviewed examples of useful feedback and successful practice. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for managers allocating scarce internal design or analysis capacity. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Human coaching, workshops, static courses, roleplay with colleagues and general chat tools. Compare this product with the buyer's present method on lead time for accepted priority work and abandoned low-value demand, including administration time. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Scenario design, voice processing if used, model interaction length, facilitator review, rubric calibration and learner support. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of management-reviewed internal demand allocation experiment. Track cost per accepted output, including correction work, unsuccessful cases and support.

06

Safeguards

Confirm owners, decisions and commitments. Keep employee discussion notes access-controlled and avoid covert individual performance inference. Noncash planning credits only; managers retain exceptions and avoid employee performance scoring. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

Get this solution built

Built for you by our AI software factory, MVP in about 5 days. Tell us about your business and how you want to run it: inside your company, or as part of what you offer your clients. We reply within one working day.

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