
Supplier minimum-order scenario planner
Visible tradeoffs around stated order limits.
- For
- Small retail purchasing teams
- Solves
- Minimum orders distort purchase decisions.
- Delivers
- Buyer-reviewed purchase scenario
- Built in
- about 4 weeks of creation time, MVP in 4 days
- Investment
- $13,000 for the MVP, $44,000 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
For small retail purchasing teams, turn approved price lists and buyer forecasts into buyer-reviewed purchase scenario.
- Extract order constraints.
- Compare supplied scenarios.
- Flag surplus assumptions.
- Link proposed outputs to original source records.
- Capture reviewer corrections and approval.
- Export a versioned buyer-reviewed purchase scenario.
What goes in, what comes out
- Approved price lists
- Buyer forecasts
AI drafts, people review. Assumption-driven planning and decision workspace.
- Buyer-reviewed purchase scenario
How it works
The workflow
- InStart with
Approved price lists and buyer forecasts
- 1
The buyer creates a project
- 2
Supplies approved price lists and buyer forecasts
- 3
Confirms scope and access
- OutFinish with
Buyer-reviewed purchase scenario
AI does the heavy lifting, people stay in charge
AI assists these bounded tasks: extract order constraints; compare supplied scenarios; flag surplus assumptions. Use only approved price lists and buyer forecasts and preserve uncertainty in buyer-reviewed purchase scenario. Keep model suggestions separate from verified facts. Link factual outputs to authorized input evidence and show missing information explicitly. Use deterministic checks for counts, dates, identifiers and arithmetic where applicable. A designated reviewer validates consequential outputs and signs off the delivered result.
What your team sees
Key screens: Brief and sources, Supplier minimum-order scenario planner, Review and delivery. Place editable drivers and constraints beside a clearly labeled scenario output. Include a baseline view, comparison chart or schedule, and an assumptions history. Let users trace a proposed quantity or date back to its inputs. Keep forecasts distinct from actual results. Open with brief and sources; move into supplier minimum-order scenario planner for the detailed task; finish in review and delivery for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.
Accounts and administration
Scenario versions, baseline reconciliation, constraint checks, assumption ownership, reviewer approvals, plan exports and actual-versus-plan tracking. Include organization-scoped access, named project owners, review queues, usage limits, export history and retention settings. Never reuse private customer material for other accounts without permission.
Integrations and data access
Orders, inventory, supplier files, process documents and workflow records. Read-only operational exports, calendars and finance or inventory records as relevant. Start with plan exports and retain human approval for execution. Begin with uploads and exports of approved price lists and buyer forecasts. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.
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
Scoping call
Day 1Thirty 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
MVP
4 daysOne buyer segment, one recurring use case; first modules: extract order constraints; compare supplied scenarios. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
5 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
9 daysSelf-serve onboarding, billing, monitoring and the wider integration set.
- 5
Run and improve
MonthlyWe 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.
- Pick the riskiest assumption. Here: will small retail purchasing teams use it to solve "minimum orders distort purchase decisions"?
- Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
- Run a paid pilot. Agree the acceptance criteria, input limits and reviewer responsibilities before starting.
- Measure, then decide. Track unplanned excess units; reviewer correction minutes; buyer acceptance and repeat purchase. Then expand, change course or stop, with evidence instead of opinions.
MVP scope for this solution. Costed pilot: One organization, one defined input format and one representative pilot batch using approved price lists and buyer forecasts. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: extract order constraints; compare supplied scenarios. Support the third task through an assisted review queue: flag surplus assumptions. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of buyer-reviewed purchase scenario. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.
After the MVP. After paying customers repeatedly accept buyer-reviewed purchase scenario, automate link proposed outputs to original source records; capture reviewer corrections and approval; export a versioned buyer-reviewed purchase scenario. Add one tested read integration, reusable customer configuration and scheduled repeat delivery. Increase supported formats or teams only when evaluation cases and reviewer capacity cover the new scope. One organization, one defined input format and one representative pilot batch using approved price lists and buyer forecasts. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype.
What the build depends on. A defensible calculation model, explicit units, constraint validation and representative boundary tests. Advanced forecasting or optimization needs adequate historical data. Obtain representative authorized inputs, an agreed review rubric and a buyer-side owner. Specific scope: One organization, one defined input format and one representative pilot batch using approved price lists and buyer forecasts. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype.
Investment
A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.
- Phase 1
MVP
One buyer segment, one recurring use case; first modules: extract order constraints; compare supplied scenarios. Manual review in the loop.
- Phase 2
Paid pilot
Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- Phase 3
Full product
Self-serve onboarding, billing, monitoring and the wider integration set.
Indicative total, MVP to full product$44,000about 4 weeks of creation time · start with the MVP from $13,000
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.
| Stage | Hosting and infrastructure | AI usage | Total per month |
|---|---|---|---|
| MVP and paid pilotabout 3 customers | $30–$60 | $50–$100 | $80–$160 |
| Full productabout 50 customers | $110–$210 | $350–$700 | $460–$910 |
Run it or resell it
For your own team
Small retail purchasing teams run it inside the business: approved price lists and buyer forecasts in, buyer-reviewed purchase scenario out, reviewed by your people.
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
#5c2791 - accent
#6ac954 - surface
#ebe4f1 - ink
#22201e
- Headings
- Libre Baskerville
- Text
- IBM Plex Sans
- Voice
- Calm, reliable, step-by-step
Selling it to your own clients: the go-to-market playbook
Pricing to test
Test USD 750-3,000 for a scoped planning setup and review, then USD 200-900 monthly for refreshes within agreed complexity. Data integration and optimization are separately scoped. All ranges are hypotheses. For this buyer, package the first sale around prepare a sample buyer-reviewed purchase scenario from a small authorized set of approved price lists and buyer forecasts and the defined buyer-reviewed purchase scenario. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.
Message to test
Visible tradeoffs around stated order limits. Demonstrate the result with prepare a sample buyer-reviewed purchase scenario from a small authorized set of approved price lists and buyer forecasts for small retail purchasing teams. Use a concrete before-and-after example without promising unmeasured savings.
Where to find buyers
Industry operations associations and process improvement consultants
Lead magnet
Prepare a sample buyer-reviewed purchase scenario from a small authorized set of approved price lists and buyer forecasts
The first 30 days
- Week 1: interview five prospective buyers from small retail purchasing teams and inspect how they handle minimum orders distort purchase decisions.
- Week 2: prepare prepare a sample buyer-reviewed purchase scenario from a small authorized set of approved price lists and buyer forecasts using authorized or synthetic material.
- Week 3: share the demonstration through industry operations associations and process improvement consultants and seek one bounded paid pilot.
- Week 4: measure unplanned excess units; reviewer correction minutes; buyer acceptance and repeat purchase, review delivery effort and ask for a repeat purchase. This is a validation schedule, not a promise that the full product can be built in thirty days.
Paid pilot
Agree the acceptance criteria, input limits and reviewer responsibilities before starting. Run prepare a sample buyer-reviewed purchase scenario from a small authorized set of approved price lists and buyer forecasts and deliver buyer-reviewed purchase scenario. Compare unplanned excess units; reviewer correction minutes; buyer acceptance and repeat purchase with the buyer's current process on comparable cases; include corrections, missed issues and reviewer time. Seek payment and repeat use. Stop or revise the scope if data access, accuracy or unit economics fail.
Success metrics
Unplanned excess units; reviewer correction minutes; buyer acceptance and repeat purchase
Retention and expansion
Build repeat use around buyer-reviewed purchase scenario. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on unplanned excess units; reviewer correction minutes; buyer acceptance and repeat purchase. Offer a recurring volume allowance after repeat demand; expand to adjacent tasks only when the buyer asks and delivery quality remains acceptable.
Why clients would pick it
A validated domain model, customer-approved constraints and forecast or decision history that improves practical planning. For this concept, accumulate permissioned examples and reviewer corrections around visible tradeoffs around stated order limits. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.
Alternatives and positioning
Spreadsheets, planners, specialist forecasting tools and existing scheduling or configuration software. Position this concept around visible tradeoffs around stated order limits. Compare it against the customer's current process on the same representative task. This is proposed differentiation; no exhaustive competitor study or uniqueness claim has been established.
Main delivery costs
Data preparation, domain modeling, validation, scenario computation, reviewer support and ongoing assumption maintenance. Initial validation additionally budgets for representative sample preparation, interviews with small retail purchasing teams, and buyer-side review of buyer-reviewed purchase scenario. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.
Safeguards
Make operational states and ownership explicit. Validate data and require appropriate approval before purchases, scheduling commitments or external system writes. One organization, one defined input format and one representative pilot batch using approved price lists and buyer forecasts. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.