Source-linked software development assistant console

For

Software teams and administrators building and maintaining codebases

Solves

Development teams rent several coding assistants, lose context between them, and cannot trace AI suggestions back to the code, ticket or policy they came from.

Delivers

Reviewer-approved code changes linked to their sources

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 tool sprawl and review effort while keeping code decisions traceable.

1. Generate code from natural language instructions.
2. Complete code from current context.
3. Detect bugs and suggest fixes.
4. Review code and pull requests automatically.
5. Refactor code for readability and maintainability.
6. Support real-time team collaboration on code.
7. Integrate with Git version control.
8. Run inside common IDEs.
9. Accept plain English commands.
10. Edit multiple files in one task.
11. Execute command line prompts.
12. Run long AI tasks in the background and notify on completion.
13. Customize AI behavior to team style.
14. Track tasks and version control assistance.
15. Generate code documentation.
16. Support Jupyter Notebooks.
17. Import extensions, themes and keybindings in one click.
18. Offer local and privacy modes for sensitive code.
19. Continue text for writing tasks.
20. Analyze datasets and produce visualizations.
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 code changes linked to their sources with source references and unresolved questions.

Everything these tools do, in one app

• AI code generation Generates code from scratch or based on natural language instructions.Found in Cursor, M9 Developer, Fynix and 2 more
• Context-aware code completion Provides intelligent autocompletion and suggestions based on the current coding context.Found in Fynix, CodeX-Editor
• Bug detection and debugging Detects bugs and offers debugging suggestions to fix errors.Found in SOTA SWE, M9 Developer, Cursor 1.0 and 2 more
• Automated code review Automatically reviews code or pull requests to catch issues and suggest improvements.Found in SOTA SWE, Cursor 1.0
• Code refactoring Suggests and performs code refactoring to improve readability and maintainability.Found in Fynix, CodeX-Editor
• Real-time collaboration Enables team members to collaborate on code in real time.Found in SOTA SWE, M9 Developer
• Version control integration Integrates with version control systems like Git for code management.Found in SOTA SWE, M9 Developer, Fynix and 1 more
• IDE integration Integrates seamlessly into popular integrated development environments.Found in M9 Developer, Fynix, Kilo Code for VS Code
• Natural language commands Allows interaction with the tool using plain English commands.Found in Cursor, Fynix, Kilo Code for VS Code
• Multi-file editing Supports creating and modifying multiple files automatically.Found in Kilo Code for VS Code, Zed Agentic Editing
• Command line execution Runs command line prompts directly from the tool.Found in Kilo Code for VS Code
• Background task management Runs long-running AI tasks in the background and notifies upon completion.Found in Zed Agentic Editing, Cursor 1.0
• Customizable AI settings Allows customization of AI behavior to match user preferences or coding style.Found in Aide, Fynix, CodeX-Editor
• Project management tools Includes task tracking and version control assistance for project management.Found in M9 Developer
• Automated documentation Automatically generates code documentation to maintain clarity.Found in SOTA SWE
• Jupyter Notebook support Enables AI-assisted coding in Jupyter Notebooks for data science.Found in Cursor 1.0
• One-click migration Imports extensions, themes, and keybindings from VSCode with one click.Found in Cursor
• Local security and privacy Provides local options and privacy mode to protect sensitive code.Found in Cursor
• Text continuation Generates coherent text continuations for writing tasks.Found in Continue 1.0
• Data analysis and visualization Interprets complex datasets and provides clear visualizations.Found in Aide

What goes in, what comes out

The workflow

1. In
   Start with

   Repository code, tickets, tests, dependency manifests and coding standards

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository code

4. 3

   Tickets

5. 4

   Tests

6. 5

   Dependency manifests and coding standards

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewer-approved code changes linked to their sources

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 and framework set; final architecture, security and merge decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Repository and task intake, Editable change preview, Review and delivery. Use a thumbnail gallery for repositories and tasks, a large central editing canvas, and a right-hand panel for sources, constraints and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a reviewer preview link with comments anchored to the relevant file and line. Make the task-specific outcome reviewer-approved code changes linked to their sources visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, repository versions, reviewer comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. 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, authorized tickets and permitted documentation sources. Cloud code storage, IDE import/export and deployment destinations. Start with file exchange and validate destination specifications before promising direct deployment. 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: generate code from natural language instructions; complete code from current context. 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

   10 days

   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 and administrators building and maintaining codebases use it to solve "development teams rent several coding assistants, lose context between them, and cannot trace AI suggestions back to the code, ticket or policy they came from"?
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 changes per developer hour and rework after merge.
4. Measure, then decide. Track accepted changes per developer 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 and framework set; final architecture, security and merge decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: generate code from natural language instructions; complete code from current context. Support the third module with operator review: detect bugs and suggest fixes. 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 changes linked to their sources. Retain the explicit scope boundary: One repository language and framework set; final architecture, security and merge decisions remain human.

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 development QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One repository language and framework set; final architecture, security and merge decisions remain human.

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 migrations, security reviews or specialist integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved code changes linked to their sources. 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 tool sprawl and review effort while keeping code decisions traceable. Demonstrate a concrete reviewer-approved code changes linked to their sources using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Software teams and administrators building and maintaining 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 code changes linked to their sources from a small authorized input set, with a transparent calculation of accepted changes per developer hour and rework after merge and no promised savings.

The first 30 days

1. Week 1: interview five software teams and administrators building and maintaining codebases and inspect a recent example of development teams renting several coding assistants, losing context between them, and being unable to trace AI suggestions back to the code, ticket or policy they came from.
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 changes per developer 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 changes per developer 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 changes per developer 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 changes linked to their sources. 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 coding patterns, review examples and repository constraints, together with reliable delivery for a narrow development niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for software teams and administrators building and maintaining codebases. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Aide, SOTA SWE, M9 Developer, Cursor 1.0, Cursor, Fynix, Kilo Code for VS Code, Zed Agentic Editing, CodeX-Editor and Continue 1.0. Compare this product with the buyer's present method on accepted changes per developer 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

Generation attempts, code processing, 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 code changes linked to their sources. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve code ownership, source attribution, license accuracy and usage permissions. Reviewers approve substantive changes and merge scope. One repository language and framework set; final architecture, security and merge decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.