BUILD SYSTEM / LC-BLD-001
Build from a spec, not a blank chat.
LaunchChair turns MVP context into scoped prompts, acceptance criteria, QA notes, and build cards for the AI tools you already use.
Keep Cursor, Codex, Claude, Lovable, Bolt, Replit Agent, v0, or ChatGPT. Give them a better brief.
Move from spec to feature card to agent run without rewriting the product story every time.
launchchair.build
active dossier / onboarding-decision-screen
ICP
Solo founder shipping first paid wedge
Feature
Decision screen after signup
Constraint
Use existing Chakra patterns
Accept
User can choose path in under 10s
AGENT PROMPT
LC-RUN-042
use_context("spec", "pricing", "auth")
task: build onboarding decision screen
return: changes, tests, blockers
guardrail: no new schema, no route rename
Plan
Patch
Verify
Launch note
Build flow
A context loop for every AI build run.
This is the Cursor + Linear hybrid: a precise work queue, but every card carries the product judgment a coding agent usually has to guess.
01 SPEC
Source: MVP spec
Lock the product spine
Customer, wedge, feature scope, constraints, and launch assumptions are pulled into one build-ready brief.
02 CARD
Output: Build card
Choose the next shippable slice
Each build card carries user intent, acceptance criteria, risk notes, and the exact context a coding agent should inherit.
03 PROMPT
Mode: Agent-ready
Generate the agent brief
LaunchChair formats the run for Cursor, Codex, Claude, Lovable, Bolt, Replit Agent, v0, or ChatGPT.
04 QA
Evidence: QA notes
Review what changed
The loop comes back with test notes, blockers, and launch context so your product story does not drift from the code.
Product evidence
The screen your agent should have had before it touched the codebase.
SPEC.md
01
Build from a living spec
Feature prompts inherit the latest ICP, wedge, scope, and acceptance criteria.
BOARD
02
Move feature cards through execution
Track what is ready, in progress, blocked, in review, and shipped.
LAUNCH
03
Carry build context into go-to-market
Landing, SEO, and launch work stay connected to what the MVP actually became.
Why this converts
Keep the AI builder. Give it a better front door.
LaunchChair does not compete with the tools builders already love. It makes each run less vague by carrying the same product spine into every prompt.
Less repeated setup
Fewer vague reruns
Cleaner handoffs
Sharper acceptance
Common objection
I already use Cursor or Claude Code.
My app builder can generate screens fast.
I do not want another AI chat wrapper.
Great. LaunchChair is the product context and prompt system you bring into those tools so they build the right slice next.
Plans
Start with the free phases. Upgrade when the build needs a full system.
Founder
$19
/mo
For founders launching a single product
1 project
1 team seat
Infinite project resets
Agent API & MCP
Builder
$39
/mo
For builders launching multiple products
5 projects
3 seats
Infinite project resets
Agent API & MCP
Agency
$99
/mo
For agencies creating multiple product MVPs for clients
Unlimited projects
10 seats
Unlimited agent API & MCP
White-labeled and shareable summary pages
FAQ
The short version.
Is LaunchChair an AI app builder?
No. LaunchChair does not replace Cursor, Lovable, Bolt, v0, Replit Agent, ChatGPT, Codex, Claude, or Claude Code. It creates the research, PRD, living spec, MVP blueprint, and feature prompts those tools need so they can build from product context instead of a vague idea.
What does LaunchChair do before I build?
LaunchChair guides market research, competitive analysis, ICP substitute behavior mapping, customer complaint and delight sentiment analysis, and wedge discovery. The goal is to understand actual user pain before AI starts generating product scope.
What does LaunchChair generate from the research?
LaunchChair turns the research into an MVP blueprint, PRD-style product spec, feature scope, acceptance criteria, build cards, landing page direction, SEO structure, launch context, and feature-by-feature prompts.
How are LaunchChair prompts different from normal prompting?
Normal prompting usually depends on memory and repeated setup. LaunchChair prompts are generated from the current product spec, so they include the relevant ICP, wedge, feature scope, acceptance criteria, constraints, and expected output for each run.
Can LaunchChair actually help save tokens?
LaunchChair can reduce repeated context and retries by giving each AI run a tighter prompt, narrower feature scope, and clearer output contract. Token savings vary by project and model, but the mechanism is straightforward: less blank-chat setup, less drift, and fewer vague reruns.
Final prompt before you build
Give your AI builder a real product brief.
Start free, validate the direction, generate the spec, then build from prompts that already know what matters.
Explore LaunchChair pages
© 2026 LaunchChair. All rights reserved.