Source-linked AI coding and review console

For

Software teams that write, review and manage code with AI assistance

Solves

Coding, review, pull request and agent work are split across several rented AI tools, so context and approvals are scattered.

Delivers

Reviewer-approved code changes and release records linked to source

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

Reduce tool sprawl while keeping code changes under team review.

1. Generate code suggestions and completions.
2. Provide in-editor AI code review feedback.
3. Manage pull requests for collaboration and review.
4. Support editor extension, command-line interface, cloud agents and version-control integration.
5. Connect external AI model subscriptions.
6. Offer built-in local models without external subscriptions.
7. Link notes, calendar, projects, search and drive context to coding tasks.
8. Show a Kanban-style agent management dashboard across local and cloud environments.
9. Group agent sessions, pull requests, files and context by project.
10. Run autonomous cloud agents on their own VM from repo files.
11. Pause, resume and review agent progress from one interface.
12. Support simultaneous data exploration and production-ready code in a stable environment.
13. Provide a collaborative coding environment with engineering controls.
14. Run multiple code blocks in parallel.
15. Allow Python, R, SQL and Markdown interchangeably on one canvas.
16. Deploy directly or export code for other teams.
17. Embed workflow management in Microsoft Teams.
18. Automate pull requests, tasks and release management.
19. Act as an AI virtual teammate for developers.
20. Provide an open-source agent template for custom workflows.
21. Keep development conversations and project tracking in one platform.
22. Compare the reviewed result with the recorded baseline and value assumptions.
23. Capture corrections and named-owner approval before consequential use.
24. Export a versioned reviewer-approved code changes and release records linked to source with source references and unresolved questions.

Everything these tools do, in one app

• AI-assisted code generation Generates code suggestions and completions to speed up writing code.Found in Terramind Nucleus
• In-editor code reviews Provides AI-powered code review feedback directly inside the editor.Found in Terramind Nucleus
• Pull request management Handles or integrates with pull requests for code collaboration and review.Found in Terramind Nucleus, Windsurf 2.0, Athena
• Multiple access surfaces Lets users work through an editor extension, command-line interface, cloud agents, and version-control integration.Found in Terramind Nucleus
• External model subscriptions Connects to external AI model subscriptions for additional capabilities.Found in Terramind Nucleus
• In-platform local models Offers built-in models for local AI assistance without external subscriptions.Found in Terramind Nucleus
• Workspace app integration Links with notes, calendar, projects, search, and drive so project context informs coding tasks.Found in Terramind Nucleus
• Agent management dashboard Provides a Kanban-style view to monitor and manage AI agents across local and cloud environments.Found in Windsurf 2.0
• Project context grouping Groups agent sessions, pull requests, files, and related context by project for easy resumption.Found in Windsurf 2.0
• Autonomous cloud agent Runs tasks on its own cloud VM, continuing work even when the local machine is off and setting up environments from repo files.Found in Windsurf 2.0
• Per-agent controls Allows pausing, resuming, and reviewing agent progress and results from one interface.Found in Windsurf 2.0
• Stable data exploration Enables simultaneous data exploration and writing of production-ready code in a stable environment.Found in Zerve AI
• Collaborative coding environment Supports team collaboration with engineering controls similar to VSCode for safe and efficient teamwork.Found in Zerve AI
• Parallel code execution Runs multiple code blocks in parallel to reduce wait times and improve productivity.Found in Zerve AI
• Multi-language support Allows using Python, R, SQL, and Markdown interchangeably on the same canvas.Found in Zerve AI
• Flexible deployment options Deploys directly from the platform or exports code in formats suitable for other teams.Found in Zerve AI
• Teams integration Embeds development workflow management directly into Microsoft Teams.Found in Athena
• Automated task and release management Automates handling of pull requests, tasks, and release management.Found in Athena
• AI virtual teammate Acts as an AI assistant that supports developers like a virtual teammate.Found in Athena
• Open-source agent template Provides the Dex template for creating custom AI agent workflows.Found in Athena
• Centralized communication Keeps development conversations and project tracking in one platform.Found in Athena

What goes in, what comes out

The workflow

1. In
   Start with

   Repository code, issue trackers, pull requests, workspace documents and agent logs

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository code

4. 3

   Issue trackers

5. 4

   Pull requests

6. 5

   Workspace documents and agent logs

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewer-approved code changes and release records 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 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 host and one supported language set; final merge and release checks 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 task intake, Editable code and agent workspace, Review and release console. Use a project list for repositories, a large central editing and diff canvas, and a right-hand panel for agent sessions, pull requests, files and comments. Let users compare agent branches side by side. Display draft, changes requested and approved states. Provide a client or stakeholder preview link with comments anchored to the relevant change. Make the task-specific outcome reviewer-approved code changes and release records 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, 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 issue trackers and permitted workspace documents. Cloud code storage, version-control 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 suggestions and completions; provide in-editor AI code review feedback. 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 that write, review and manage code with AI assistance use it to solve "coding, review, pull request and agent work are split across several rented AI tools, so context and approvals are scattered"?
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 review hour and post-merge corrections.
4. Measure, then decide. Track accepted changes per review hour and post-merge corrections; 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 and one supported language set; final merge and release checks remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: generate code suggestions and completions; provide in-editor AI code review feedback. Support the third module with operator review: manage pull requests for collaboration and review. 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 and release records linked to source. Retain the explicit scope boundary: One repository host and one supported language set; final merge and release checks remain with the engineering team.

What the build depends on. Repository upload and preview, asynchronous agent 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 and one supported language set; final merge and release checks 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-repo or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved code changes and release records 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 tool sprawl while keeping code changes under team review. Demonstrate a concrete reviewer-approved code changes and release records linked to source using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Software teams professional communities; specialist engineering consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant developer events.

Lead magnet

A reviewed sample reviewer-approved code changes and release records linked to source from a small authorized input set, with a transparent calculation of accepted changes per review hour and post-merge corrections and no promised savings.

The first 30 days

1. Week 1: interview five software teams that write, review and manage code with AI assistance and inspect a recent example of coding, review, pull request and agent work split across several rented AI tools.
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 review hour and post-merge corrections, 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 review hour and post-merge corrections. 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 review hour and post-merge corrections; 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 and release records 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 coding patterns, review rules and agent configurations, 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 that write, review and manage code with AI assistance. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Terramind Nucleus, Windsurf 2.0, Zerve AI and Athena, plus manual coding and review workflows. Compare this product with the buyer's present method on accepted changes per review hour and post-merge corrections. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model inference, cloud agent VM time, 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 and release records 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 leads approve substantive changes and release scope. One repository host and one supported language set; final merge and release checks 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.