Source-linked codebase review and planning console

For

Software teams maintaining a shared codebase with review and planning duties

Solves

Review, documentation, planning and issue work sit in separate tools, so codebase context is lost between them.

Delivers

Reviewer-approved code findings, specs and documentation linked to source

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 review and planning effort while keeping every claim traceable to source.

1. Review code changes for bugs and anti-patterns.
2. Analyze the full codebase, not only the diff.
3. Map architecture and dependencies visually.
4. Post context-aware inline suggestions in pull requests.
5. Summarize pull requests in plain language.
6. Answer codebase questions in interactive chat.
7. Generate and update documentation per commit.
8. Track review quality, engineering health and bottlenecks.
9. Generate context-rich specs from ideas.
10. Run multi-agent planning with distinct roles.
11. Preview specs and diagrams in a webview.
12. Support shared editing of docs and specs.
13. Connect development, documentation and project tools.
14. Offer live suggestions on written content.
15. Apply customizable templates per team style.
16. Keep setup and learning curve minimal.
17. Generate blog, social and marketing drafts.
18. Aid debugging and issue resolution with source insights.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export versioned reviewer-approved code findings, specs and documentation linked to source with references and unresolved questions.

Everything these tools do, in one app

• AI code review Automatically reviews code changes to identify bugs, anti-patterns, and potential issues.Found in Greptile, Entelligence.ai
• Full codebase context Analyzes the entire codebase, not just the diff, to provide more accurate insights and reviews.Found in Greptile, Entelligence.ai, Shotgun CLI
• Codebase visualization Generates visual maps or graphs of code architecture and dependencies to help understand relationships.Found in Greptile
• Inline suggestions Provides context-aware comments and quick-fix suggestions directly in pull requests.Found in Greptile
• PR summaries Summarizes pull requests in natural language to quickly grasp changes.Found in Greptile
• Interactive chat Allows users to ask questions and get answers about the codebase interactively.Found in Greptile
• Automated documentation Automatically generates and updates project documentation with each commit.Found in Greptile, Entelligence.ai
• Team analytics Tracks engineering health, review quality, and bottlenecks to improve team workflows.Found in Entelligence.ai
• Spec generation Creates clear, context-rich specifications from ideas to guide AI coding assistants.Found in Shotgun CLI
• Multi-agent planning Uses multiple AI agents with distinct roles to cover different aspects of planning and design.Found in Shotgun CLI
• Spec preview Provides a webview interface to preview specs and diagrams before development.Found in Shotgun CLI
• Team collaboration Enables sharing and collaborative editing of documentation and specs.Found in Entelligence.ai, Shotgun CLI
• Integrations Connects with popular development, documentation, and project management tools.Found in Mimrr, Greptile, Entelligence.ai
• Real-time suggestions Offers live suggestions to improve clarity and engagement in written content.Found in Mimrr
• Customizable templates Provides templates that can be tailored to different writing styles and needs.Found in Mimrr
• Simple interface Offers a user-friendly design with minimal setup or learning curve.Found in Mimrr
• Content generation Generates written content for blogs, social media, and marketing materials using AI.Found in Mimrr
• Issue management Aids in debugging and issue resolution by providing insights and streamlining processes.Found in Greptile

What goes in, what comes out

The workflow

1. In
   Start with

   Repository history, pull requests, issues and team conventions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository history

4. 3

   Pull requests

5. 4

   Issues and team conventions

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved code findings, specs and documentation linked to source

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 arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One repository language set and one approved review policy; final merge, security and architecture decisions 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 and scope setup, Editable review and spec workspace, Team proof and delivery. Use a thumbnail gallery for repositories and change sets, a large central editing canvas, and a right-hand panel for source links, constraints and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a team preview link with comments anchored to the relevant file or line. Make the task-specific outcome reviewer-approved code findings, specs and documentation linked to source visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, repository versions, team comments, approval states, usage allowances, review 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

Team-owned repositories, issue trackers and documentation sources. Cloud code storage, version-control import/export and project 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: review code changes for bugs and anti-patterns; analyze the full codebase, not only the diff. 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 software teams maintaining a shared codebase with review and planning duties use it to solve "review, documentation, planning and issue work sit in separate tools, so codebase context is lost between them"?
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 review findings per reviewer hour and rework after merge.
4. Measure, then decide. Track accepted review findings per reviewer hour and rework after merge; 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 language set and one approved review policy; final merge, security and architecture decisions remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: review code changes for bugs and anti-patterns; analyze the full codebase, not only the diff. Support the third module with operator review: map architecture and dependencies visually. 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 code findings, specs and documentation linked to source. Retain the explicit scope boundary: One repository language set and one approved review policy; final merge, security and architecture decisions remain with the engineering team.

What the build depends on. Repository upload and preview, asynchronous analysis 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 language set and one approved review policy; final merge, security and architecture decisions 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 specialist security work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved code findings, specs and documentation linked to source. 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 review and planning effort while keeping every claim traceable to source. Demonstrate a concrete reviewer-approved code findings, specs and documentation linked to source using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Software teams maintaining a shared codebase with review and planning duties 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 code findings, specs and documentation linked to source from a small authorized input set, with a transparent calculation of accepted review findings per reviewer hour and rework after merge and no promised savings.

The first 30 days

1. Week 1: interview five software teams maintaining a shared codebase with review and planning duties and inspect a recent example of review, documentation, planning and issue work sitting in separate tools, so codebase context is lost between them.
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 review findings per reviewer hour and rework after merge, 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 review findings per reviewer hour and rework after merge. 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 review findings per reviewer hour and rework after merge; 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 code findings, specs and documentation linked to source. 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 review policies, codebase conventions and reviewer corrections, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for software teams maintaining a shared codebase with review and planning duties. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Mimrr, Greptile, Entelligence.ai and Shotgun CLI, plus manual review and generic assistants. Compare this product with the buyer's present method on accepted review findings per reviewer hour and rework after merge. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model and compute usage, repository indexing, storage, reviewer hours, team revision rounds and licensed source tooling. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved code findings, specs and documentation linked to source. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve code ownership, source attribution, license accuracy and usage permissions. Engineering owners approve substantive changes and merge scope. One repository language set and one approved review policy; final merge, security and architecture decisions 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.