Demand planning workspace
Versioned assumptions and honest error measurement for planners.
- For
- Commercial planners at specialist distributors
- Solves
- Forecasts hide assumptions and cannot explain revisions.
- Delivers
- Demand scenarios and assumption history
- Built in
- about 3 weeks of creation time, MVP in 3 days
- Investment
- $6,000 for the MVP, $20,500 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
For commercial planners at specialist distributors, turn historical demand, promotions and customer commitments into demand scenarios and assumption history.
- Validate historical periods.
- Model seasonality.
- Add promotion assumptions.
- Compare demand cases.
- Track forecast revisions.
- Calculate backtest errors.
What goes in, what comes out
- Historical demand
- Promotions
- Customer commitments
AI drafts, people review. Assumption-driven planning and decision workspace.
- Demand scenarios
- Assumption history
How it works
The workflow
- InStart with
Historical demand, promotions and customer commitments
- 1
Validate baseline inputs
- 2
Confirm definitions and constraints
- 3
Select editable assumptions
- 4
Calculate feasible alternatives
- 5
Inspect sensitivities
- 6
Let the responsible person approve a plan
- 7
Compare later actuals with the recorded assumptions
- OutFinish with
Demand scenarios and assumption history
AI does the heavy lifting, people stay in charge
Extract input context and explain scenario differences. Use deterministic calculations or explicit optimization for quantities, compatibility, dates and prices. Show uncertain assumptions. Never let generated prose silently change the calculation rules.
What your team sees
Key screens: Demand drivers, scenario view, backtest results. 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. In this product, the first view is demand drivers, followed by scenario view and backtest results.
Accounts and administration
Scenario versions, baseline reconciliation, constraint checks, assumption ownership, reviewer approvals, plan exports and actual-versus-plan tracking.
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. These are candidate integration categories, not verified supported connectors.
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
3 daysOne buyer segment, one recurring use case; first modules: validate historical periods; model seasonality. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
4 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
7 daysRemaining modules: compare demand cases; track forecast revisions; calculate backtest errors. Self-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 commercial planners at specialist distributors use it to solve "forecasts hide assumptions and cannot explain revisions"?
- 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. Reproduce a known historical plan, test missing inputs and boundary constraints, then run a new scenario.
- Measure, then decide. Track forecast error and planning adoption. Then expand, change course or stop, with evidence instead of opinions.
MVP scope for this solution. Begin with commercial planners at specialist distributors and one recurring use case. Build the first two modules: validate historical periods; model seasonality. Provide operator assistance for the third module: add promotion assumptions. Deliver demand scenarios and assumption history through a manual review queue. Perform other necessary full-scope functions manually during the pilot. Include all applicable access, accuracy and professional-review controls from the start.
After the MVP. After paid pilots establish value, automate the remaining modules: compare demand cases; track forecast revisions; calculate backtest errors. Add one validated source integration, reusable customer configuration and recurring delivery. Expand to additional teams, document formats or languages only after testing the new scope.
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.
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: validate historical periods; model seasonality. 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
Remaining modules: compare demand cases; track forecast revisions; calculate backtest errors. Self-serve onboarding, billing, monitoring and the wider integration set.
Indicative total, MVP to full product$20,500about 3 weeks of creation time · start with the MVP from $6,000
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
Commercial planners at specialist distributors run it inside the business: historical demand, promotions and customer commitments in, demand scenarios and assumption history 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
#512791 - accent
#81c954 - surface
#e9e4f1 - ink
#22201e
- Headings
- Fraunces
- Text
- Inter
- 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.
Message to test
Demand planning workspace for commercial planners at specialist distributors. Versioned assumptions and honest error measurement for planners. Demonstrate the claim through a demand scenario and backtest report.
Where to find buyers
Distribution planning consultants
Lead magnet
A demand scenario and backtest report
The first 30 days
- Week 1: interview five prospective buyers in this segment: commercial planners at specialist distributors. Ask to see a recent example of the problem and their current process.
- Week 2: prepare this demonstration using authorized or synthetic material: a demand scenario and backtest report.
- Week 3: present it through distribution planning consultants and seek one narrowly scoped paid pilot.
- Week 4: review forecast error, planning adoption, total delivery effort and a concrete renewal decision before increasing scope.
Paid pilot
Reproduce a known historical plan, test missing inputs and boundary constraints, then run a new scenario. Compare feasibility, reconciliation and observed error rather than judging the quality of the explanation alone. For this solution, use historical demand, promotions and customer commitments and evaluate demand scenarios and assumption history. Agree success thresholds with the buyer before starting; collect a baseline for forecast error, planning adoption. A positive signal is payment and repeat use with acceptable quality and delivery cost, not a favorable demo reaction alone.
Success metrics
Forecast error, planning adoption
Retention and expansion
Refresh inputs, compare recorded assumptions with actual outcomes and refine validated constraints. Expand scenario complexity only when the buyer uses it for a decision.
Why clients would pick it
A validated domain model, customer-approved constraints and forecast or decision history that improves practical planning. For this solution, build around versioned assumptions and honest error measurement for planners. This advantage requires execution and accumulated customer trust; the base model alone is not a defensible asset.
Alternatives and positioning
Spreadsheets, planners, specialist forecasting tools and existing scheduling or configuration software. Differentiate on this specific proposed advantage: versioned assumptions and honest error measurement for planners. Test it against the buyer's current method on the same task. Competitor coverage and uniqueness have not been established.
Main delivery costs
Data preparation, domain modeling, validation, scenario computation, reviewer support and ongoing assumption maintenance.
Safeguards
Make operational states and ownership explicit. Validate data and require appropriate approval before purchases, scheduling commitments or external system writes. Validate source access and reviewer availability during the pilot. Maintain customer-level access, data deletion controls and a record of final approvals.