Run focused context-market studies, on demand
For teams making a pricing, packaging, positioning, or feature decision who want a fast, structured read before committing to a larger study or a market-facing change.
A headless research engine
You bring the decision. It picks the right study from the stack, runs it, and hands back a decision-ready read — including how AI buyer proxies are likely to interpret the offer.
Use it when you have a specific decision to make and want a directional, defensible read quickly — without becoming a research-methods expert first.
One engine, many methods: it routes between study types so you don’t have to know which one to reach for.
Bring a decision — get a defensible read
Describe the offer, the buyer, and the alternative they weigh against.
The engine picks the right method for the question you’re answering.
Collects responses and analyzes them — no methods expertise required.
A plain-English recommendation, plus how an AI agent reads the offer.
The questions it puts to rest
What should we charge?
Which features matter most?
What comparison frame are buyers using?
Does an AI agent understand the offer correctly?
Reach for it when a decision is on the line
You are about to set or change a price and want to understand acceptable range, expensive thresholds, and the demand curve.
You are launching a new feature, add-on, or package and want to know which outcomes buyers actually prioritize.
You are rewriting a pricing page or homepage and want to know whether AI agents classify the offer correctly.
You are choosing between packaging options and need a directional preference signal.
You want to test how the offer is interpreted before committing to a larger study.
A structured, decision-ready report
Not a stack of raw data — a recommendation you can act on.
A clear decision recommendation in plain English
Best-fit buyer context: the role, struggle, or alternative that anchors the strongest choice logic
Mental model and comparison-frame risk: what buyers (or agents) are actually comparing you against
Willingness-to-pay range, demand curve, priority weights, or package preference — depending on the module
The proof, claims, or implementation signals needed to defend a higher price or stronger claim
An agentic interpretation note: whether an AI buyer proxy classifies the offer in the comparison set that supports the price
One engine, many methods
A decision engine, not a menu. Pick a module directly, or start with the question you're trying to answer and let it route you.
- Contextual Van WestendorpWhat price range feels acceptable, expensive, cheap, or disqualifying?Ships first
- Gabor-GrangerHow does demand change at specific price points?Roadmap
- MaxDiffWhich outcomes, features, or claims matter most?Roadmap
- ConjointWhich package, feature, and price combinations drive choice?Roadmap
- Concept TestDoes the market understand, want, and correctly classify the offer?Roadmap
- Switch / JTBDWhat struggling moment, alternative, anxiety, and desired progress shape buying?Roadmap
- Agentic Interpretation TestHow do AI agents classify, compare, and recommend the offer?Roadmap
Is the MCP the right call?
You have a specific decision and can describe the offer, the buyer, and the alternative
You want a fast, structured read and are comfortable with a directional signal
You’re willing to act on what the study shows
The decision is internal-use — early-stage pricing, feature, packaging, or positioning calls
You need segment-level pricing power and value attribution (Contextual Pricing Power Study)
You need a full commercial strategy — positioning, packaging, proof, page, narrative (Context-Market Fit Sprint)
The offer is too early to describe clearly
You’re looking for client-facing research with branded reports (agency licensing is on the roadmap)
Add it to your AI client
Add this server to Claude, ChatGPT, Cursor, or any MCP-capable assistant, then ask it to run a study.
Explore the MCP
Bring the decision. The MCP picks the module, runs the study, and returns a decision-ready read — including how an AI agent is likely to interpret the offer. Free preview (15 responses), then $250 a study or $750–900 for a four-study bundle.