Experiment design assistant

Buyer

Growth and product teams testing new features

Problem

Experiments begin without clear success and stopping criteria.

Format

Assumption-driven planning and decision workspace

Also fits

Customer Support; Science and Research; Finance

USP

Pre-registered decision logic with explicit assumptions and measurement limits.

The product

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.

Core functionality

1. Clarify proposed mechanism.
2. Define primary measures.
3. Specify guardrails.
4. Document assignment approach.
5. Flag measurement gaps.
6. Draft analysis plans.

Customer 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 human review

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 the customer puts in

Business hypothesis, baseline metrics and testing constraints

What the customer gets

Reviewed experiment design brief

Accounts and administration

Scenario versions, baseline reconciliation, constraint checks, assumption ownership, reviewer approvals, plan exports and actual-versus-plan tracking.

MVP scope

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.

After the MVP is validated

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.

Build dependencies

A defensible calculation model, explicit units, constraint validation and representative boundary tests. Advanced forecasting or optimization needs adequate historical data.

Integrations and data access

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.

Defensibility

A validated domain model, customer-approved constraints and forecast or decision history that improves practical planning. For this idea, 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.

Alternatives and positioning

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.

Revenue model and test pricing

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.

Main delivery costs

Data preparation, domain modeling, validation, scenario computation, reviewer support and ongoing assumption maintenance.

Marketing message to test

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.

Acquisition channels

Product experimentation consultants

Lead magnet

A complete experiment specification for one hypothesis

The first 30 days of marketing

1. 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.
2. Week 2: prepare this demonstration using authorized or synthetic material: a complete experiment specification for one hypothesis.
3. Week 3: present it through product experimentation consultants and seek one narrowly scoped paid pilot.
4. Week 4: review protocol completeness, interpretable results, total delivery effort and a concrete renewal decision before increasing scope.

Paid pilot and validation

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 idea, 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.

Success metrics

Protocol completeness, interpretable results

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.

Operating controls and limitations

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.

Investment indication

What it would take to build, from a first MVP to the full product. A planning range to start the conversation, not a quote. Running costs (model usage, hosting, reviewer hours) come on top.

Brand style (concept)