Solution Database / Product Development
Experiment design assistant
Experiments begin without clear success and stopping criteria. Pre-registered decision logic with explicit assumptions and measurement limits.
01The offer
For growth and product teams testing new features, turn business hypothesis, baseline metrics and testing constraints into reviewed experiment design brief. Address the recurring problem: experiments begin without clear success and stopping criteria. The value hypothesis is a more complete, reviewable deliverable with less repeated preparation; the pilot must establish whether that benefit is real.
- For
- Growth and product teams testing new features
- Takes in
- Business hypothesis, baseline metrics and testing constraints
- Delivers
- Reviewed experiment design brief
- Message
- Experiment design assistant for growth and product teams testing new features. Pre-registered decision logic with explicit assumptions and measurement limits. Demonstrate the claim through a complete experiment specification for one hypothesis.
- Lead magnet
- A complete experiment specification for one hypothesis
02How it works
- Clarify proposed mechanism
- Define primary measures
- Specify guardrails
- Document assignment approach
- Flag measurement gaps
- Draft analysis plans
Workflow
Validate baseline inputs, confirm definitions and constraints, select editable assumptions, calculate feasible alternatives, inspect sensitivities, let the responsible person approve a plan, and compare later actuals with the recorded assumptions. Start with business hypothesis, baseline metrics and testing constraints and finish with reviewed experiment design brief.
AI and people
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.
Screens
Key screens: Hypothesis canvas, measure definitions, decision rules. 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 hypothesis canvas, followed by measure definitions and decision rules.
Admin
Scenario versions, baseline reconciliation, constraint checks, assumption ownership, reviewer approvals, plan exports and actual-versus-plan tracking.
03Market gap
Alternatives buyers use today
Spreadsheets, planners, specialist forecasting tools and existing scheduling or configuration software. Differentiate on this specific proposed advantage: pre-registered decision logic with explicit assumptions and measurement limits. Test it against the buyer's current method on the same task. Competitor coverage and uniqueness have not been established.
Where this wins
A validated domain model, customer-approved constraints and forecast or decision history that improves practical planning. For this solution, build around pre-registered decision logic with explicit assumptions and measurement limits. This advantage requires execution and accumulated customer trust; the base model alone is not a defensible asset.
04Why now
Product Development teams are adopting AI for exactly this kind of repeatable work, and the cost of language and vision models has dropped far enough that a narrow, reviewed workflow pays back quickly. The buyer already feels the problem: experiments begin without clear success and stopping criteria.
05Proof & signals
Channels where buyers gather: Product experimentation consultants. Metrics that prove it works: Protocol completeness, interpretable results.
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 business hypothesis, baseline metrics and testing constraints and evaluate reviewed experiment design brief. Agree success thresholds with the buyer before starting; collect a baseline for protocol completeness, interpretable results. A positive signal is payment and repeat use with acceptable quality and delivery cost, not a favorable demo reaction alone.
06Execution plan
MVP
Begin with growth and product teams testing new features and one recurring use case. Build the first two modules: clarify proposed mechanism; define primary measures. Provide operator assistance for the third module: specify guardrails. Deliver reviewed experiment design brief 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.
First 30 days
Week 1: interview five prospective buyers in this segment: growth and product teams testing new features. Ask to see a recent example of the problem and their current process. Week 2: prepare this demonstration using authorized or synthetic material: a complete experiment specification for one hypothesis. Week 3: present it through product experimentation consultants and seek one narrowly scoped paid pilot. Week 4: review protocol completeness, interpretable results, total delivery effort and a concrete renewal decision before increasing scope.
After the pilot
After paid pilots establish value, automate the remaining modules: document assignment approach; flag measurement gaps; draft analysis plans. 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.
Retention
Refresh inputs, compare recorded assumptions with actual outcomes and refine validated constraints. Expand scenario complexity only when the buyer uses it for a decision.
Integrations
Product feedback, authorized interviews, usage exports and requirement 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.
07Investment and running costs
| Phase | Scope | Time | Budget |
|---|---|---|---|
| MVP | One buyer segment, one recurring use case; first modules: clarify proposed mechanism; define primary measures. Manual review in the loop. | 3 days | $6,000 |
| Paid pilot | Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers. | 4 days | $6,000 |
| Full product | Remaining modules: document assignment approach; flag measurement gaps; draft analysis plans. Self-serve onboarding, billing, monitoring and the wider integration set. | 7 days | $8,500 |
| Total | $20,500 | ||
| Running | Hosting | AI usage | Total a month |
|---|---|---|---|
| MVP and paid pilot (about 3 customers) | $30–$60 | $50–$100 | $80–$160 |
| Full product (about 50 customers) | $110–$210 | $350–$700 | $460–$910 |
Revenue model 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.
Cost drivers
Data preparation, domain modeling, validation, scenario computation, reviewer support and ongoing assumption maintenance.
Safeguards
Use consented research and preserve contradictory evidence. Separate observed user behavior, proposed explanations and untested product assumptions. Validate source access and reviewer availability during the pilot. Maintain customer-level access, data deletion controls and a record of final approvals.
Take it further
Concept proposal expanded from the 315-solution conversation. Demand, pricing, differentiation, build scope and integration feasibility are hypotheses, not verified market findings. Category link is inspiration rather than evidence of business viability.