Source-linked development agent console

For

Software teams and IT administrators running internal development and operations work

Solves

Development tasks, bug fixes and project tracking are split across several rented AI tools, so code, transcripts and decisions sit outside the team's own systems.

Delivers

Reviewable, production-ready updates linked to their 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, transcripts and approvals inside the team's own environment.

1. Capture voice task descriptions with reviewable transcription.
2. Generate code from instructions against the connected repository.
3. Flag and help resolve coding errors.
4. Track project progress and development tasks.
5. Report analytics on delivery performance.
6. Integrate with existing systems and workflows.
7. Produce clear natural-language outputs.
8. Summarize long documents and threads.
9. Apply customizable output templates.
10. Support real-time multi-user collaboration.
11. Prioritize tasks and send deadline reminders.
12. Summarize meetings and extract action items.
13. Configure custom team workflows.
14. Send real-time notifications and progress dashboards.
15. Run code locally over a private network.
16. Support multiple code-aware models and CLI agent workflows.
17. Ship an open source codebase with short setup.
18. Review transcripts before sending.
19. Automate browser tasks.
20. Connect to external APIs.
21. Build internal apps and lightweight dashboards.
22. Automate recurring ops and long-running tasks.
23. Operate directly on real repositories.
24. Assign role-aware frontend and backend agents.
25. Return reviewable, production-ready updates.
26. Flag uncertain or high-risk cases for human review.
27. Compare the reviewed result with the recorded baseline and value assumptions.
28. Capture corrections and named-owner approval before merge.
29. Export a versioned reviewable, production-ready updates linked to their source with source references and unresolved questions.

Everything these tools do, in one app

• Voice-driven task description Allows users to describe tasks verbally instead of typing.Found in Devin Voice, Sled
• Autonomous code generation Automatically writes code snippets or entire functions based on instructions.Found in Devin Voice, Devin AI, Ovren
• Debugging assistance Identifies and helps resolve coding errors.Found in Devin AI, Ovren
• Project management Aids in tracking project progress and managing development tasks.Found in Devin AI, Devin by Cognition
• Data analysis Provides insights and analytics to optimize performance and decision making.Found in Devin AI
• Systems integration Seamlessly integrates with existing systems and workflows.Found in Devin AI, Devin 1.2 by Congition, Devin by Cognition and 1 more
• Natural language generation Creates clear and coherent text outputs.Found in Devin 1.2 by Congition
• Automated summarization Condenses lengthy documents into concise summaries.Found in Devin 1.2 by Congition, Devin by Cognition
• Customizable templates Tailors outputs for different content needs.Found in Devin 1.2 by Congition
• Real-time collaboration Allows multiple users to work on projects simultaneously.Found in Devin 1.2 by Congition
• Task prioritization AI-driven prioritization and deadline reminders.Found in Devin by Cognition
• Meeting summaries Automated meeting summaries and action item extraction.Found in Devin by Cognition
• Customizable workflows Custom workflows to fit different team needs.Found in Devin by Cognition
• Real-time notifications Real-time notifications and progress tracking dashboards.Found in Devin by Cognition
• Local execution Runs code locally over a secure private network to keep data on your machine.Found in Sled
• Multi-model compatibility Works with multiple code-aware models and command-line agent workflows.Found in Sled
• Open source Open source codebase with a short setup flow.Found in Sled
• Transcription review Voice messages are transcribed and can be reviewed before being sent.Found in Sled
• Browser automation Agent can control a browser to perform tasks.Found in Fabi
• API connections Connects to APIs to integrate with other services.Found in Fabi
• Internal app building Tools to build internal applications and lightweight dashboards.Found in Fabi
• Workflow automation Automates recurring ops and long-running tasks.Found in Fabi
• Repository connection Connects directly to a repository and operates on real codebases.Found in Ovren
• Role-aware agents Role-aware AI agents for frontend and backend tasks.Found in Ovren
• Reviewable updates Returns reviewable, production-ready updates rather than simple suggestions.Found in Ovren
• Confidence signaling Flags uncertain or high-risk cases for human review.Found in Ovren

What goes in, what comes out

The workflow

1. In
   Start with

   Repositories, voice task descriptions, tickets, meeting recordings and API credentials

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repositories

4. 3

   Voice task descriptions

5. 4

   Tickets

6. 5

   Meeting recordings and API credentials

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewable, production-ready updates linked to their 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 connected repository and one approved model set; final merge, deployment and security decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Task intake and voice capture, Agent run and diff review, Admin console and audit. Use a queue of tasks with owner and state, a large central diff and transcript view, and a right-hand panel for sources, confidence and comments. Let users compare agent versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant file or transcript line. Make the task-specific outcome reviewable, production-ready updates linked to their source visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, repository versions, client 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 meeting recordings. Cloud code storage, CI/CD 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: capture voice task descriptions with reviewable transcription; generate code from instructions against the connected repository. 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 IT administrators running internal development and operations work use it to solve "development tasks, bug fixes and project tracking are split across several rented AI tools, so code, transcripts and decisions sit outside the team's own systems"?
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 corrections after merge.
4. Measure, then decide. Track accepted changes per developer hour and corrections 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 connected repository and one approved model set; final merge, deployment and security decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: capture voice task descriptions with reviewable transcription; generate code from instructions against the connected repository. Support the third module with operator review: flag and help resolve coding errors. 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 reviewable, production-ready updates linked to their source. Retain the explicit scope boundary: One connected repository and one approved model set; final merge, deployment and security decisions remain human.

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 connected repository and one approved model set; final merge, deployment and security 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 multi-repository or regulated environments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewable, production-ready updates linked to their 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, transcripts and approvals inside the team's own environment. Demonstrate a concrete reviewable, production-ready updates linked to their source using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Software teams and IT administrators running internal development and operations work professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewable, production-ready updates linked to their source from a small authorized input set, with a transparent calculation of accepted changes per developer hour and corrections after merge and no promised savings.

The first 30 days

1. Week 1: interview five software teams and IT administrators running internal development and operations work and inspect a recent example of development tasks, bug fixes and project tracking are split across several rented AI tools, so code, transcripts and decisions sit outside the team's own systems.
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 corrections 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 corrections 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 corrections 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 reviewable, production-ready updates linked to their 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 workflows, 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 IT administrators running internal development and operations work. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Devin Voice, Devin 1.2 by Congition, Devin AI, Devin by Cognition, Sled, Fabi and Ovren. Compare this product with the buyer's present method on accepted changes per developer hour and corrections 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 calls, code execution, 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 reviewable, production-ready updates linked to their 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 deployment scope. One connected repository and one approved model set; final merge, deployment and security decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.