01Capture
Turn conversation into backlog work
Use chat or voice to capture product intent, acceptance criteria, and the decision trail behind the work.
PreceptPrivate beta for AI-native product development
Ship what you meant
Turn product conversations into durable specs and carry that intent through agent implementation, review, and release.
Your model, your choice
Yourproductisbiggerthanoneprompt,onerepository,oronemodel.Preceptkeepsthedecisions,specs,codecontext,agentruns,reviews,andreleasesconnected,soyourteamcanmovefromconversationtoshippedsoftware.
How Precept works
One connected path from idea to release.
Precept is not just a chat surface. It is a product workspace that captures intent, retrieves codebase context, coordinates agents, and keeps the release path visible.
02Specify
Give agents durable instructions
Precept turns approved intent into specs, so implementation follows product behavior instead of a disposable prompt.
03Retrieve
Pull the right context only
Codebase Memory finds the relevant files, symbols, specs, and prior decisions before a model is called.
04Build
Run isolated implementation work
Send board tickets through model routing, implementation, validation, and review with clear autonomy controls.
05Review
Know what changed and what it cost
Track tokens, latency, tool calls, failures, PR feedback, and agent output before work moves forward.
06Ship
Move from ticket to release
Keep the path from product request to GitHub review, TestFlight, or App Store distribution in one workspace.
From intent to release
The work stays connected.
Precept carries the same product intent through planning, implementation, review, and delivery—without turning every step into another chat.
Turn conversation into product intent.
Capture the decision, write the spec, and keep only the context that will still matter next week.
Chat to backlogSpecs before codeProject memory
Product trace / ShapePrecept 01—04
Conversation / 09:41
“Let people turn review comments into buildable work.”
Decision captured→142 tokens
Feature spec
Review feedback
Resolve a comment into a focused fix
Preserve the original review context
Verify the resulting change
Select a stage to follow the work.
Initial benchmark
Less context. Same signal.
Huge prompts become focused context windows without throwing away the files that matter.
98.5%
less prompt context
Task-specific retrieval instead of attaching the full project.
90%
relevant-file coverage
Human-labelled files retained while unrelated context falls away.
≈3 ms
local retrieval
Median time to find and rank context before the model is called.
387k → 6k
estimated tokens
A full-project prompt reduced to a focused, bounded context window.
Internal benchmark · 5 representative tasks · token counts estimated
Get early access.
Join the private Mac beta for the product workspace that turns conversations, specs, code context, and agent runs into shippable work.
PreceptFrom product intent to shipped software
© Haros Labs Ltd 2026