ShogunAI

A day of shipping

The “why” never lands in the code — so it disappears every time

Every interruption takes the thread with it, and three weeks later you are digging for your own reasoning. The spec sits in Slack, in a hallway conversation and in an issue, and something falls out while you collect it.

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  • Local-first memory
  • Bring your own AI
  • Approval before sending

Before / After

Not 10% better. 10×, 100×.

Five moments from a day spent across several codebases. Open a card to swap today for the same day with ShogunAI in it.

Coming back from an interruption

Today

You are deep in a feature when a production incident takes forty minutes. Back at the editor, what you were thinking and what you meant to touch next are gone; fifteen minutes go into rebuilding it.

What it costs: Fifteen minutes of rebuilding, and the thread you were holding

Why the code is like that

Today

A project you have not touched in three weeks. A function is shaped oddly and you spend twenty minutes back through Slack and PR comments reconstructing why you did that.

What it costs: Twenty minutes of excavation, and the reasoning the code never carried

Switching repositories

Today

Mid-feature for A when B needs an emergency fix. Opening B, ten minutes go into recalling the layout, the branch you were on and what was half-done. Back in A, the thread has snapped.

What it costs: Ten minutes of reloading, and the thread in A

Solving the same thing twice

Today

A build error you have definitely seen before. You cannot recall which project it was or what fixed it, so it costs another hour to work out.

What it costs: An hour spent again, and an answer you had already found

Requirements in fragments

Today

Before you can start, forty minutes go into gathering days of requirements from Slack, hallway conversations, issues and notes. One spec change from three days ago is missed, and the work is redone after the fact.

What it costs: Forty minutes of gathering, and the change you missed — the rework it caused

AI across the product lifecycle

From scattered decisions to delivery-ready context

ShogunAI preserves the reasoning behind the work, prepares the next handoff, and helps turn project context into a reviewed artifact.

Research
Discussion
Design
Product memory

Follow the work

Capture relevant context as work moves between research, discussion, design, and implementation.

Why did we choose this approach?
Customer evidence
Open constraints
Handoff ready

Retrieve the why

Ask about a feature, bug, customer, or decision using the language your team already uses.

Launch update

ShogunAI

Review requiredApproved

Create the artifact

Turn that context into a brief, issue, update, or handoff for review.

Questions, answered

Clear answers before product context enters the workflow

Does this replace Linear, Jira, or Notion?

No. ShogunAI is a context and execution layer across your existing tools, not a replacement project-management system.

Can it connect code and product context?

It is designed to relate work across the tools you authorize, helping you recall the discussions and artifacts surrounding implementation.

Is it useful for individual contributors?

Yes. The product is designed around an individual’s private work memory, including engineers, designers, and product managers.

Ship the next version without losing the decisions behind it

Connect evidence, trade-offs, and implementation history, then turn that context into the next artifact.

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