Business logic

The part of your product
your AI keeps rebuilding.

Features, rules, customer paths and how they connect. It decides what your product does, it is not written down anywhere in one piece, and every task — human or agent — starts by putting it back together.

What it is

A rule, a status, a customer path
— and what they reach.

Take one rule from a product anyone can open: Supabase Studio, published in full on this site.

Rule · Billing & Subscription

An unpaid invoice restricts the organization

Warning → grace period → restricted

Reaches Organization · Project

Nothing in that sentence is a file name. It is the product: what holds, what changes, and who meets it. A change to the invoice code reaches the organization and its projects — through the rule, not through an import.

Open the model and follow it yourself →

Where it lives

Everywhere in pieces.
Nowhere in one place.

Not in the code

The code holds calls, imports and types. That a rule restricts an organization, and which customer path meets it, is nowhere in a call graph — it is spread across the files that implement it.

Not in the documents

A specification describes what was intended on the day it was written. The product keeps changing after that, and nothing ties the document back to the code that now decides the behaviour.

Not in a search index

Search and retrieval return what looks like the question. A part that shares no word with the task — an organization restricted because an invoice stayed open — is not similar to it, and is not returned.

Not in people’s heads any more

When AI writes a large share of the code, the connections stop passing through the person who would have known them. Each task starts by rebuilding what somebody already understood.

This is why no single tool your team already owns can hand it to you: each of them holds one of the pieces.

How the tools in this market compare →

What it costs

Rebuilding it is most
of the work.

Measured across the projects GitMir has run — the same workflows timed with the business logic established and without it.

Judging how risky a change is

16 h → 0.3 h

Checking what the agent did

45 min → 8 min

Time spent in conversation

6.5 h → 2.4 h a week

Onboarding somebody new

21 days → 4 days

Understanding what to build

90 min → 20 min

Rework after the first version

69% → 5%

Same team, same hours: +38% changes accepted per quarter.

What GitMir does

Builds it once from your code.
Keeps it current with every commit.

For people

Ask what a change reaches, what a rule holds and which customer path passes through a screen — and see the answer with the part of the product it came from.

GitMir Intelligence →

For your coding agent

One MCP server: the agent gets the rules, customer paths and features a task touches before it changes code, and every commit is checked against them before it lands.

GitMir Vibe →

For the work itself

Accepted work carried out from the same business logic and committed to the repository, at a fixed price per project rather than per token.

GitMir Cloud Compute →

Benchmark 001

92% of answers correct against 78% for the strongest alternative, on 8.4K tokens of context a question against 27.4K. Same repository, same fifty questions, same AI model.

Every system, every number, and how to repeat it →

Questions

Asked about business logic.

What is business logic in software?

The rules a product enforces, the states its records move through, the customer paths people walk, and the features those belong to — together with the connections between them. It is what the product does, as opposed to how the code is written.

Where does business logic live?

Implemented across the code, described partly in documents that drift, and understood in pieces by the people who wrote it. Nowhere in one place, which is why every task, every agent and every new hire starts by rebuilding it.

Can AI read business logic from the code?

An agent can read files and infer parts of it per prompt, and pays for that on every run. In Benchmark 001 — one repository, fifty questions, the same AI model for every system — answering from an established model of the business logic was correct 92% of the time against 78% for the strongest alternative, on 8.4K tokens of context a question against 27.4K.

How is this different from a code graph?

A code graph connects symbols and answers where something is used. Business logic connects rules, statuses, customer paths and features, and answers what a change alters for the customer and what else it reaches.

How is it kept current?

It is built from the repositories themselves and updated as commits land, so the answer a person or an agent gets is the product as it is now, not as it was when somebody last wrote it down.

Stop rebuilding it
on every task.