Use Case

Your AI Is Only as Good as Its Context

Cursor, Copilot, and Claude Code read your code - but they infer intent from what's documented, not what's implied. Dockr generates the ground truth documentation that keeps your AI agents accurate.

Ground Your AI

The Problem

AI without documentation invents reality

AI hallucinates architecture

Without documented system design, AI agents invent interfaces that don't exist and suggest deprecated patterns.

Code changes break AI context

AI tools trained on stale code confidently suggest wrong implementations because they lack current architecture context.

Onboarding AI to a new codebase takes weeks

Feeding an AI agent a 200K LOC repository without documentation is like asking it to navigate a city with no map.

The Difference

AI output with and without Dockr

Without Dockr

AI trying to implement a new feature...

// AI suggestion:

useAuthToken(token) // method doesn't exist

import { validate } from '../legacy/auth' // path was refactored

callAPI('/api/v2/users') // endpoint deprecated

AI hallucinates based on patterns from training data, not your actual codebase.

With Dockr

AI with current architecture context...

// AI suggestion (grounded):

authenticate({ token, scope: 'user' }) // matches current API

import { verify } from '@/auth/modern' // correct post-refactor path

callAPI('/api/v3/users') // current endpoint

AI uses Dockr's API docs, architecture maps, and file explanations to generate accurate code.

What Dockr Provides

Ground truth your AI can rely on

Architecture maps

System overview, module relationships, data flow diagrams

File explanations

Natural-language summaries of every module, class, and function

API documentation

Endpoint descriptions, request/response models, auth details

Sequence diagrams

Request lifecycle, service interactions, state transitions

Decision records

Why the system was built this way - tradeoffs and constraints

We started using Cursor for code generation but kept getting suggestions that referenced APIs we'd deprecated months ago. Once we added Dockr-generated docs to the context, the hallucinations dropped dramatically.

- Tech Lead, AI-adopting engineering team

Questions about AI readiness

Which AI tools benefit from Dockr documentation?

All of them. Cursor, GitHub Copilot, Claude Code, and custom agents all perform better with structured context. Dockr's prose docs, API references, and architecture maps give any AI tool accurate ground truth to work from.

How does Dockr documentation improve AI-generated code?

AI coding tools infer intent from what's documented. When Dockr keeps docs current, AI agents know which interfaces exist, which patterns are current, and which modules are deprecated. This reduces hallucinations and improves suggestion relevance.

Can AI agents read Dockr docs directly?

Yes. Dockr generates markdown and structured documentation that AI agents can ingest via MCP, API, or direct repository access. The documentation is designed to be both human-readable and machine-parseable.

Does this work with my existing AI stack?

Dockr integrates with any AI tool that can access documentation. Whether you're using Cursor, GitHub Copilot, Claude Code, or a custom agent framework, Dockr provides the structured context they need.

Give your AI agents accurate ground truth

Book a walkthrough. We'll show you how Dockr-generated documentation improves AI tool output quality from your actual codebase.

Ground Your AI

7-day trial with 2MB upload after the demo