Platform

How it
works.

Shared visibility, context, and confidence for every human and AI contributor. Build performant systems at scale while staying true to your architecture vision.

~ topogy / architecture / data-flow.mcp
Sources
GitHub · Slack · CI/CD · Ticketing · MCP
Engine
Topogy Engine
Model
Knowledge Graph
Solutions
Acceleration · Confidence · Evolution · Cost
Overview

A living model of your engineering system.

Topogy connects to where engineering happens — your repos, your communication, your pipelines, your AI tools. It ingests code, decisions, conversations, and agent activity, then builds a structured, queryable model of how your systems actually work.

Four phases

From connection to continuous evolution.

Topogy starts producing value the moment your tools are connected — and keeps compounding it as the model evolves.

01
PhaseConnect

Connect your tools in minutes

Topogy integrates with GitHub, Slack, CI/CD, ticketing, cloud providers, and AI coding tools. No agents to install on developer machines. No code changes. Connect your existing tools and Topogy starts ingesting context immediately.

02
PhaseModel

Build the living model

Topogy maps your engineering system — services, teams, dependencies, ownership, conventions, and decision history — into a structured, queryable knowledge graph. It understands not just what exists, but how things relate.

03
PhaseEvolve

Stay current, automatically

Every PR, every agent session, every architecture decision updates the model. Topogy doesn't take snapshots — it evolves continuously. Overnight workflows verify conventions, generate specs, detect drift, and surface insights.

04
PhaseActivate

Put the model to work

The living model powers every Topogy solution — dynamically shared context for agents and engineers, drift detection and blast radius analysis, automated workflows, and metrics that connect AI adoption to business outcomes.

Core concepts

The fundamentals.

Knowledge Graph

The queryable, interactive model at the heart of Topogy. Maps services, teams, dependencies, conventions, ownership, and decision history into a connected structure agents and humans can query at task time.

Dynamic Context Scoping

Context isn't a document dump. Topogy scopes context dynamically to the task at hand — an agent working on a payment service gets payment-relevant architecture, conventions, and dependencies, not the entire system.

Continuous Evolution

The model isn't rebuilt — it evolves. Every code change, conversation, and agent session updates the graph incrementally. Overnight workflows verify, prune, and enrich. The system maintains itself.

Agent Communication Layer

Topogy doesn't wrap your AI tools — it communicates with them via MCP. Agents query the model for context, report what they build, and receive feedback. Topogy is a peer in the workflow, not a gatekeeper.

The outcome

One foundation, multiple applications.

The same model powers context delivery, drift detection, automated workflows, and cost attribution. Connect once. Compound forever.

FAQ

Frequently asked questions.

What tools does Topogy connect to?

Topogy integrates with GitHub, Slack, CI/CD, ticketing, cloud providers, and AI coding tools. It connects to where engineering already happens — your repos, communication, pipelines, and AI tools.

Do I need to install anything on developer machines?

No. There are no agents to install on developer machines and no code changes required — you connect your existing tools and Topogy starts ingesting context immediately.

How long does setup take?

You can connect your tools in minutes and get a working model in days. Topogy starts producing value the moment your tools are connected.

How does Topogy stay up to date?

Topogy doesn't take snapshots — the model evolves continuously. Every pull request, agent session, and architecture decision updates the knowledge graph, and overnight workflows verify conventions, detect drift, and surface insights.

Does Topogy work with my AI coding tools?

Yes. Topogy communicates with AI tools via MCP. Agents query the model for context and report what they build.

See the living model in action.

Connect your repos. Get a working model in days, not months.