Source-linked codebase map and review console

For

Engineering leads and platform teams maintaining large or legacy codebases

Solves

Developers and reviewers cannot quickly see how modules, dependencies and features connect, so onboarding, change review and impact analysis take repeated manual tracing.

Delivers

Reviewer-approved source-linked code maps

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce manual code tracing while keeping every diagram linked to its source.

1. Connect a repository and select scope.
2. Generate interactive clickable diagrams of code structure.
3. Map dependencies between modules, functions and components.
4. Analyze code with AI to propose architecture and feature views.
5. Open diagrams inside the IDE.
6. Build on-demand custom diagrams for a chosen area or data flow.
7. Group code into feature-oriented hierarchies.
8. Link each feature or component back to its source.
9. Handle large and legacy repositories.
10. Share a common visual view for teams and stakeholders.
11. Update diagrams automatically on push or pull request.
12. Attach visual diff comments to pull requests.
13. Accept natural language or file-path prompts.
14. Produce flowcharts, sequence diagrams and architecture diagrams.
15. Break the codebase into functional components.
16. Surface undocumented or scattered relationships.
17. Support multiple programming languages and project types.
18. Provide an infinite canvas workspace.
19. Save and reload workspace layouts.
20. Integrate with Git version control workflows.
21. Compare the reviewed result with the recorded baseline and value assumptions.
22. Capture corrections and named-owner approval before consequential use.
23. Export a versioned reviewer-approved source-linked code map with source references and unresolved questions.

Everything these tools do, in one app

• Interactive visual maps Provides clickable, zoomable diagrams of code structure for easy navigation and exploration.Found in CodeViz (YC S24), ProductMap AI, Codalogy and 1 more
• Automatic diagram generation Automatically creates visual representations of code architecture and components from source code.Found in CodeViz (YC S24), Eraser AI, ProductMap AI and 2 more
• Dependency mapping Shows connections and dependencies between modules, functions, and components.Found in CodeViz (YC S24), Codalogy, Haystack
• AI-powered analysis Uses AI to analyze code and generate insights such as architecture diagrams or feature discovery.Found in CodeViz (YC S24), Eraser AI, ProductMap AI and 1 more
• IDE integration Works directly within a code editor or development environment without switching tools.Found in CodeViz (YC S24), Haystack
• On-demand custom diagrams Allows users to request specific diagrams for particular parts of the codebase or data flows.Found in CodeViz (YC S24), Eraser AI
• Feature-oriented visualization Focuses on software features rather than just code lines, showing feature hierarchies and relationships.Found in ProductMap AI
• Traceability Links features or components directly to their corresponding code for easier navigation.Found in ProductMap AI
• Large codebase support Handles very large repositories, suitable for legacy or enterprise projects.Found in CodeViz (YC S24), ProductMap AI, Codalogy
• Collaboration enhancement Reduces miscommunication by providing a shared visual understanding of code for teams and stakeholders.Found in ProductMap AI
• Continuous integration automation Automatically updates diagrams when code changes are pushed or pull requests are opened.Found in Eraser AI
• Visual diff on pull requests Adds visual comments on pull requests to show context of code changes.Found in Eraser AI
• Natural language prompting Allows users to specify what to visualize using natural language or file paths.Found in Eraser AI, Haystack
• Multiple diagram types Supports various diagram formats such as flowcharts, sequence diagrams, and architecture diagrams.Found in Eraser AI
• Functional component breakdown Breaks down codebase into distinct functional components for clearer understanding.Found in Codalogy
• Hidden relationship discovery Helps identify undocumented or scattered relationships between code parts.Found in Codalogy
• Multi-language support Works with a wide range of programming languages and project types.Found in Codalogy
• Infinite canvas workspace Provides a limitless 2D space to organize and view code elements freely.Found in Haystack
• Workspace saving Allows users to save and reload their current layout of open editors and diagrams.Found in Haystack
• Git integration Supports version control workflows including Git.Found in Haystack

What goes in, what comes out

The workflow

1. In
   Start with

   Repository source, git history, pull requests and team notes

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository source

4. 3

   Git history

5. 4

   Pull requests and team notes

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved source-linked code maps

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for graph construction, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One repository scope and supported language set; final architecture and correctness checks remain engineering. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Repository connection and scope, Editable map workspace, Review and delivery. Use a thumbnail gallery for repositories and saved workspaces, a large central infinite canvas for diagrams, and a right-hand panel for source links, dependencies and comments. Let users compare diagram versions side by side. Display draft, changes requested and approved states. Provide a review link with comments anchored to the relevant code element. Make the task-specific outcome reviewer-approved source-linked code maps visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, repository versions, review comments, approval states, access allowances, diagram limits, export history and a rights record for supplied code. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Customer-owned repositories, authorized git providers and permitted documentation sources. Cloud code storage, IDE plugins and CI destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   5 days

   One buyer segment, one recurring use case; first modules: connect a repository and select scope; generate interactive clickable diagrams of code structure. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will engineering leads and platform teams maintaining large or legacy codebases use it to solve "developers and reviewers cannot quickly see how modules, dependencies and features connect, so onboarding, change review and impact analysis take repeated manual tracing"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted maps per engineering hour and corrections after review.
4. Measure, then decide. Track accepted maps per engineering hour and corrections after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One repository scope and supported language set; final architecture and correctness checks remain engineering. Implement one approved input format, a bounded representative case set and the first two task modules: connect a repository and select scope; generate interactive clickable diagrams of code structure. Support the third module with operator review: map dependencies between modules, functions and components. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewer-approved source-linked code maps. Retain the explicit scope boundary: One repository scope and supported language set; final architecture and correctness checks remain engineering.

What the build depends on. Repository upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One repository scope and supported language set; final architecture and correctness checks remain engineering.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined repository package. Offer a monthly production allowance after repeat demand. Quote complex multi-repository or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved source-linked code map. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce manual code tracing while keeping every diagram linked to its source. Demonstrate a concrete reviewer-approved source-linked code map using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering leads and platform teams maintaining large or legacy codebases professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved source-linked code map from a small authorized input set, with a transparent calculation of accepted maps per engineering hour and corrections after review and no promised savings.

The first 30 days

1. Week 1: interview five engineering leads and platform teams maintaining large or legacy codebases and inspect a recent example of developers and reviewers cannot quickly see how modules, dependencies and features connect, so onboarding, change review and impact analysis take repeated manual tracing.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted maps per engineering hour and corrections after review, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted maps per engineering hour and corrections after review. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted maps per engineering hour and corrections after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved source-linked code maps. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved diagram styles, repository constraints and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering leads and platform teams maintaining large or legacy codebases. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

CodeViz (YC S24), Eraser AI, ProductMap AI, Codalogy, Haystack, manual tracing and generic diagram tools. Compare this product with the buyer's present method on accepted maps per engineering hour and corrections after review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model inference attempts, repository parsing and storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved source-linked code maps. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, code accuracy and usage permissions. Engineering leads approve substantive changes and publication scope. One repository scope and supported language set; final architecture and correctness checks remain engineering. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.