How It Works
FAQs
The Concept Validation Engine (CVE) is an AI-powered research pipeline that stress-tests a new product, service, or messaging concept before you invest real money building it. Each run walks through a structured 10-step process — trend analysis, competitor research, consumer complaint mining, persona simulation, and more — grounded in live web research, ending in a single, evidence-backed go/no-go verdict.
Each validation run consumes one credit. Credits are purchased individually or in bulk packs — see the Buy Credits page for current pricing. Credits never expire once purchased.
Most validations take between 20 and 30 minutes in total, though the exact time depends on the complexity of your concept. You can track progress step-by-step on the run's page.
No. Output is an automated, AI-generated indication — a preliminary signal drawn from publicly available data — not a guarantee, prediction, or independently verified finding. It's intended as a first-pass check and a starting point for your own further research, not a substitute for professional advice. See our Terms of Use for full details.
Your runs and their Output are visible only to you. We do not share your runs with other users, and no other user can view, access, or export them. See our Privacy Notice for details.
The app interface and AI-generated Output are both available in English, French, Spanish, Italian, German, and Portuguese. Choose your run's output language when creating a new run, and switch the app's interface language anytime using the language selector in the header.
Refunds are not allowed once a validation run has been started, as it consumes real compute and third-party API cost. A refund requested before a run has started is issued net of the credit card transaction fees incurred on the original payment. If you believe you were charged in error, contact us and we'll look into it.
Reach out via the Contact page or the "Contact Us" option on the Buy Credits page — we offer custom pricing for larger volume needs.
Yes. Completed runs can be exported as a Word document (DOCX) directly from the run's page.
All payments are processed directly by Stripe, our payment processor. We never store your full card number ourselves. See our Privacy Notice for more on how we handle your information.
Every purchase automatically generates a PDF invoice. Go to your Profile page and look for the "Purchase History" section — each purchase has a "Download Invoice" link with the full purchase date, amount paid, and credits granted.
No. There's no subscription or seat licensing — you pay only for the run credits you actually use, with no expiration and no recurring charges.
Use the Contact page — we're happy to help.
A few financial and business abbreviations that appear on the Launch Economics dashboard.
The average amount spent on marketing and sales to win one new paying customer.
The total revenue a business expects to earn from one customer over the full relationship, not just the first purchase.
Recurring revenue restated as a yearly figure, so subscription or repeat revenue can be compared on a consistent basis.
Recurring revenue restated as a monthly figure, so subscription or repeat revenue can be compared on a consistent basis.
The direct cost of producing what a company sells — materials, manufacturing, and other costs tied directly to each unit.
The highest price a customer is genuinely prepared to pay for a product or service, based on research or stated preference.
The point at which a product satisfies strong, real demand in a specific market — evidenced by organic pull rather than pushed adoption.
The number of unique users who engage with a product on a given day — a core measure of ongoing engagement.
The number of unique users who engage with a product at least once within a calendar month.
The practice of improving an app's visibility and conversion rate within app store search and browse listings.
A customer-loyalty metric, from -100 to 100, based on how likely customers are to recommend a product to others.
A small-scale exercise to confirm that an idea, technical approach, or partnership is feasible before committing to full development.
How a person perceives and interacts with a product overall — ease of use, flow, and satisfaction, not just visual design.
The visual and interactive surface of a product — screens, buttons, and controls a user directly sees and touches.
The practice of improving a website or app's visibility in unpaid search engine results.
Technology that reads text from an image or photo (e.g. a handwritten or printed list) and converts it into machine-readable data.
A defined way for one piece of software to request data or functionality from another — commonly how an app connects to an external service.
A category of AI in which software improves at a task by learning patterns from data, rather than following only fixed, hand-written rules.
Software capable of performing tasks — such as analysis, generation, or recognition — that typically require human-like judgment.
This pipeline's own financial model (Step 9) — pricing, cost structure, demand forecast, and margin — used to judge whether a concept can launch profitably.
A synthetic, evidence-grounded archetype representing one segment of a concept's target audience — used in Steps 5 through 8 to simulate how that kind of customer would realistically react to, price-frame, and buy (or not buy) the concept.
A unique code identifying one distinct sellable version of a product — e.g. a specific size, color, or flavor — used for inventory tracking and ordering.
A sales model in which a brand sells directly to end customers — its own website, app, or storefront — rather than through third-party retailers or distributors.
Testing two versions of something — a price, headline, design, or offer — against each other with real customers to see which one actually performs better, rather than guessing.
Two paired figures reported for the same metric in Steps 6 and 8: AS-DESCRIBED scores the concept exactly as it stands today, while WITH-RECOMMENDATIONS scores it assuming the fixes those steps recommend for the audience's stated objections are actually implemented. The gap between the two shows how much making those specific changes is worth.