Source-linked development assistant and admin console

For

Software teams and engineering organizations building and maintaining codebases

Solves

Development work is spread across separate AI coding tools, so context, review and task tracking fragment and teams lose control of their own code and workflow.

Delivers

Source-linked code changes with named-owner approval

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Consolidate coding assistance, review and task management into one owned console.

1. Execute autonomous development tasks with minimal input.
2. Analyze existing code for context-aware assistance.
3. Generate code from requirements or prompts.
4. Run automated tests on generated code.
5. Create pull requests for code changes.
6. Support interactive code review with comments and revisions.
7. Improve suggestions from recorded interactions.
8. Connect with Linear and Jira task platforms.
9. Support Python, JavaScript, Java and other languages.
10. Provide real-time code completion as you type.
11. Detect errors in code in real time.
12. Offer context-aware suggestions from current code.
13. Suggest code optimization improvements.
14. Integrate with popular editors and IDEs.
15. Allow cloud, local or self-hosted model backends.
16. Accept voice commands for interaction.
17. Operate applications under human oversight with permissions.
18. Provide offline dictation with local speech recognition.
19. Let users build custom widgets and extensions.
20. Browse the web to gather information during coding.
21. Make API calls to external services.
22. Execute terminal commands and scripts.
23. Hand off sessions to the cloud after local shutdown.
24. Onboard quickly with minimal setup.
25. Track benchmark and real-world task performance.
26. Identify relevant context files automatically.
27. Modify multiple files across large projects.
28. Match generated code to existing style.
29. Scale to codebases with millions of lines.
30. Provide an integrated chat for questions and documentation.
31. Offer open-source availability for community contribution.
32. Run locally for deployment and data control.
33. Accept natural language project requirements.
34. Automate planning, coding, testing, debugging and deployment.
35. Use specialized agents for reviews and pair programming.
36. Generate frontend and backend from specifications.
37. Choose cloud or own infrastructure for deployment.

Everything these tools do, in one app

• Autonomous task execution AI agents independently handle complete development tasks with minimal user input.Found in Fine, OpenDevin, Zero Setup AI Coding with OpenHands and 1 more
• Codebase understanding Analyzes your existing code to provide context-aware assistance.Found in Fine, Jolt AI
• Code generation Automatically writes code based on requirements or prompts.Found in Fine, GoCodeo, Pythagora AI
• Automated testing Runs tests on generated code to ensure functionality.Found in Fine, Pythagora AI
• Pull request creation Generates pull requests for code changes.Found in Fine
• Interactive code review Allows developers to comment and request revisions from the AI.Found in Fine
• Continuous learning Improves code quality over time by learning from interactions.Found in Fine
• Task management integration Connects with platforms like Linear and Jira to manage tasks.Found in Fine
• Multi-language support Supports programming languages including Python, JavaScript, and Java.Found in GoCodeo
• Real-time code completion Provides code suggestions as you type.Found in GoCodeo
• Error detection Identifies errors in code in real-time.Found in GoCodeo
• Context-aware suggestions Offers suggestions based on the current code context.Found in GoCodeo
• Code optimization tips Suggests ways to improve code performance.Found in GoCodeo
• IDE integration Integrates with popular code editors and IDEs.Found in GoCodeo
• Multi-model backend Allows choosing between cloud, local, or self-hosted AI models.Found in OpenCode Superapp, OpenDevin
• Voice control Enables interaction with the AI using voice commands.Found in OpenCode Superapp
• Supervised computer use Agents operate applications under human oversight with permission settings.Found in OpenCode Superapp
• Offline dictation Local speech recognition works without an internet connection.Found in OpenCode Superapp
• Custom widgets and extensions Users can build their own widgets and integrations.Found in OpenCode Superapp
• Web browsing AI can browse the web to gather information during coding.Found in Zero Setup AI Coding with OpenHands
• API calls Supports making API calls and integrating with external services.Found in Zero Setup AI Coding with OpenHands
• Terminal command execution Executes terminal commands and scripts directly.Found in Zero Setup AI Coding with OpenHands, Devin for Terminal
• Cloud session handoff Continues tasks in the cloud after local machine is shut down.Found in Devin for Terminal
• Fast onboarding Quick setup time to get started.Found in Devin for Terminal
• Benchmark performance Proven capabilities on engineering benchmarks and real-world tasks.Found in Devin for Terminal
• Automatic context file identification Automatically finds relevant files for the prompt.Found in Jolt AI
• Multi-file code modifications Supports changes across multiple files in large projects.Found in Jolt AI
• Code style matching Generates code that matches existing style.Found in Jolt AI
• Scalability for large codebases Handles codebases with millions of lines of code.Found in Jolt AI
• Integrated chat interface Provides a chat for questions, brainstorming, and documentation.Found in Jolt AI
• Open-source availability Encourages community contributions and transparency.Found in OpenDevin, Zero Setup AI Coding with OpenHands
• Local execution Can be run locally for control over deployment and data privacy.Found in OpenDevin, OpenCode Superapp
• Natural language interaction Describe project requirements in plain language to generate code.Found in Pythagora AI
• End-to-end development automation Covers planning, coding, testing, debugging, and deployment.Found in Pythagora AI
• Multiple specialized AI agents Dedicated agents handle specific tasks like code reviews and pair programming.Found in Pythagora AI
• Frontend and backend creation Generates frontend and backend based on specifications.Found in Pythagora AI
• Deployment options Choose between cloud or own infrastructure for deployment.Found in Pythagora AI

What goes in, what comes out

The workflow

1. In
   Start with

   Repository code, task trackers, terminal sessions and model choices

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository code

4. 3

   Task trackers

5. 4

   Terminal sessions and model choices

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Source-linked code changes with named-owner approval

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 arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One repository and one task tracker; final merge and deployment checks remain engineering. 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 project list for repositories and tasks, a large central diff and chat canvas, and a right-hand panel for sources, model choice and permissions. Let users compare agent runs side by side. Display draft, changes requested and approved states. Provide a review link with comments anchored to the relevant file and line. Make the task-specific outcome source-linked code changes with named-owner approval visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, repository versions, review comments, approval states, model allowances, task limits, download 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

Developer-owned repositories, authorized task trackers and permitted model providers. 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: execute autonomous development tasks with minimal input; analyze existing code for context-aware assistance. 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 engineering organizations building and maintaining codebases use it to solve "development work is spread across separate AI coding tools, so context, review and task tracking fragment and teams lose control of their own code and workflow"?
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 engineering hour and rework after merge.
4. Measure, then decide. Track accepted changes per engineering 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 and one task tracker; final merge and deployment checks remain engineering. Implement one approved input format, a bounded representative case set and the first two task modules: execute autonomous development tasks with minimal input; analyze existing code for context-aware assistance. Support the third module with operator review: generate code from requirements or prompts. 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 source-linked code changes with named-owner approval. Retain the explicit scope boundary: One repository and one task tracker; final merge and deployment checks remain engineering.

What the build depends on. Code 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 and one task tracker; final merge and deployment 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 engineering separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked code changes with named-owner approval. 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

Consolidate coding assistance, review and task management into one owned console. Demonstrate a concrete source-linked code changes with named-owner approval using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Software teams and engineering organizations 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 source-linked code changes with named-owner approval from a small authorized input set, with a transparent calculation of accepted changes per engineering hour and rework after merge and no promised savings.

The first 30 days

1. Week 1: interview five software teams and engineering organizations building and maintaining codebases and inspect a recent example of development work spread across separate AI coding tools, so context, review and task tracking fragment and teams lose control of their own code and workflow.
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 engineering 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 engineering 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 engineering 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 source-linked code changes with named-owner approval. 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, 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 software teams and engineering organizations building and maintaining codebases. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Fine, Amazon Q Developer, Helix, GoCodeo, OpenCode Superapp, Zero Setup AI Coding with OpenHands, Devin for Terminal, Jolt AI, OpenDevin and Pythagora AI. Compare this product with the buyer's present method on accepted changes per engineering 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 inference, repository processing, storage, reviewer hours, client revision rounds and licensed source code. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of source-linked code changes with named-owner approval. 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 deployment scope. One repository and one task tracker; final merge and deployment checks remain engineering. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.