Source-linked codebase understanding console

For

Engineering teams and maintainers onboarding to unfamiliar repositories

Solves

Developers spend hours tracing unfamiliar code, dependencies and architecture across repositories, docs and wikis.

Delivers

Source-linked explanations, summaries and architecture views

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Reduce time to understand an unfamiliar repository while keeping every answer traceable to source.

1. Answer plain-language questions about a repository.
2. Explain code snippets, functions and concepts.
3. Support multiple programming languages across projects.
4. Summarize the purpose and flow of modules.
5. Allow follow-up questions during coding.
6. Work inside GitHub repositories.
7. Answer architecture and entry-point questions.
8. Re-index on every commit.
9. Generate a code graph of abstractions and relationships.
10. Index without manual tagging.
11. Open the assistant by modifying the repository URL.
12. Query code, documentation and wiki content together.
13. Index open-source code at the version the project uses.
14. Inspect dependencies, vulnerabilities, changelogs and upgrade changes.
15. Surface code examples from repositories, issues, discussions and pull requests with source links.
16. Filter repositories with copyleft, unknown or missing licenses.
17. Integrate with coding agents through one init command.
18. Provide mobile access inside the GitHub mobile app.

Everything these tools do, in one app

• Natural language querying Ask questions in plain language and get explanations or insights about code.Found in CodebaseChat, ExplainGithub, Depth AI and 2 more
• Code explanations Provides clear explanations of code snippets, functions, or concepts.Found in CodebaseChat, ExplainGithub, GitHub Chat and 1 more
• Multi-language support Works with multiple programming languages across different projects.Found in CodebaseChat, ExplainGithub, Depth AI
• Code summarization Generates summaries that outline the purpose and flow of code blocks or modules.Found in CodebaseChat, ExplainGithub
• Real-time interaction Allows developers to ask follow-up questions and clarify uncertainties as they code.Found in CodebaseChat
• GitHub integration Works directly within GitHub repositories for seamless access.Found in ExplainGithub, GitHub Chat, Copilot Chat in GitHub Mobile
• Code architecture insights Answers complex questions about code architecture and entry points.Found in Depth AI
• Incremental indexing Updates the knowledge base with every commit to reflect the latest code changes.Found in Depth AI
• Code graph generation Creates an AI-generated graph that captures abstractions and relationships in the codebase.Found in Depth AI
• No manual tagging Eliminates the need for manual tagging or summarization of files.Found in Depth AI
• URL modification access Access the chat feature by simply modifying the GitHub URL.Found in GitHub Chat
• Query multiple content types Supports querying across code, documentation, and wiki content within a repository.Found in GitHub Chat
• Version-aware indexing Builds an index of open-source code specific to the version your project uses.Found in GitHits beta 0.9
• Dependency inspection Allows agents to inspect package dependencies, vulnerabilities, changelogs, and upgrade changes.Found in GitHits beta 0.9
• Code examples from real implementations Surfaces code examples from repositories, issues, discussions, and pull requests, linked back to the source.Found in GitHits beta 0.9
• License filtering Filters out repositories with copyleft, unknown, or missing license information.Found in GitHits beta 0.9
• Agent integration Integrates with AI coding agents like Claude Code, Codex, and Cursor via a single init command.Found in GitHits beta 0.9
• Mobile access Provides AI assistance on mobile devices within the GitHub mobile app.Found in Copilot Chat in GitHub Mobile

What goes in, what comes out

The workflow

1. In
   Start with

   Repository code, documentation, wiki content, commit history and package metadata

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository code

4. 3

   Documentation

5. 4

   Wiki content

6. 5

   Commit history and package metadata

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Source-linked explanations, summaries and architecture views

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for indexing, schema validation, license filtering, dependency parsing and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Indexing scope and license filtering are configurable; final architectural and security judgments remain with the engineering team. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Repository connection and index status, Source-linked assistant, Administrator console. Use a repository list with index freshness, a large central conversation and explanation canvas, and a right-hand panel for cited files, graph views and dependency findings. Let users compare explanation versions side by side. Display indexed, stale and failed states. Provide a shareable answer link with citations anchored to the relevant file and commit. Make the task-specific outcome source-linked explanations, summaries and architecture views visible beside its evidence, review state and value baseline.

Accounts and administration

Organization ownership, repository connections, index versions, license filter settings, agent tokens, query history, approval states, usage allowances and a rights record for indexed material. 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, documentation and wiki sources. Repository hosts, issue trackers, package registries and coding agents. Start with file exchange and validate destination specifications before promising direct write access. Start with authorized read-only access. 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: answer plain-language questions about a repository; explain code snippets, functions and concepts; summarize the purpose and flow of modules. 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 teams and maintainers onboarding to unfamiliar repositories use it to solve "developers spend hours tracing unfamiliar code, dependencies and architecture across repositories, docs and wikis"?
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: Time to first correct answer and accepted explanations per review hour.
4. Measure, then decide. Track time to first correct answer and accepted explanations per review hour; 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 host, one language family and read-only indexing; final architectural and security judgments remain with the engineering team. Implement one approved input format, a bounded representative case set and the first three task modules: answer plain-language questions about a repository; explain code snippets, functions and concepts; summarize the purpose and flow of modules. Support the remaining modules with operator review: architecture answers, code graph, dependency inspection, license filtering and agent integration. 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 languages, repository hosts and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around source-linked explanations, summaries and architecture views. Retain the explicit scope boundary: One repository host, one language family and read-only indexing; final architectural and security judgments remain with the engineering team.

What the build depends on. Repository connection and indexing, asynchronous indexing 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 host, one language family and read-only indexing; final architectural and security judgments remain with the engineering team.

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 agent-integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked explanations, summaries and architecture views deliverable. 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 time to understand an unfamiliar repository while keeping every answer traceable to source. Demonstrate a concrete source-linked explanation using the buyer's approved repository and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams and maintainers onboarding to unfamiliar repositories professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample source-linked explanation from a small authorized repository, with a transparent calculation of time to first correct answer and accepted explanations per review hour and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams and maintainers onboarding to unfamiliar repositories and inspect a recent example of developers spending hours tracing unfamiliar code, dependencies and architecture across repositories, docs and wikis.
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 time to first correct answer and accepted explanations per review hour, 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: Time to first correct answer and accepted explanations per review hour. 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

Time to first correct answer and accepted explanations per review hour; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs source-linked explanations, summaries and architecture views. 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 repository configurations, review examples and verified indexing constraints, 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 teams and maintainers onboarding to unfamiliar repositories. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

CodebaseChat, ExplainGithub, Depth AI, GitHub Chat, GitHits beta 0.9 and Copilot Chat in GitHub Mobile. Compare this product with the buyer's present method on time to first correct answer and accepted explanations per review hour. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Indexing compute, embedding storage, model calls, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of source-linked explanations, summaries and architecture views. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, license compliance, secret redaction and access permissions. Engineering owners approve substantive architectural and security conclusions and repository scope. One repository host, one language family and read-only indexing; final architectural and security judgments remain with the engineering team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.