Source-linked code assistant and admin console

For

Software teams and developers working in existing codebases

Solves

Developers switch between several AI coding tools and lose project context, standards and review evidence.

Delivers

Reviewed, source-linked code changes

Built in

about 5 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 tool switching while keeping code, context and review evidence in one owned workspace.

1. Generate code from project context or requirements.
2. Use the whole codebase for relevant suggestions.
3. Answer plain-language questions about code and reasoning.
4. Support multiple programming languages.
5. Save and organize reusable snippet libraries.
6. Integrate with common editors and IDEs.
7. Keep snippet collections updated with current standards.
8. Generate production-grade code that passes peer review.
9. Reuse existing components and hooks in the codebase.
10. Accept Figma files, Jira tickets, screenshots and plain text.
11. Enforce team coding standards and design system consistency.
12. Convert requirements into backend-ready code.
13. Gather entire files or folders from the local codebase.
14. Enhance prompts with additional context using the team's own API key.
15. Copy code context to web-based LLMs in one click.
16. Keep code data on the machine where required.
17. Report lines, characters and estimated token usage.
18. Generate non-coding content such as launch or marketing material.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before merging.
21. Export a versioned reviewed, source-linked code change set with source references and unresolved questions.

Everything these tools do, in one app

• AI code generation Generates code based on your project context or requirements.Found in Code2.AI, Code Snippets AI, Fei
• Codebase context awareness Uses your entire codebase to provide relevant suggestions and assistance.Found in Code2.AI, Fei, EchoComet
• Natural language queries Lets you ask questions in plain language to explain code or reasoning.Found in Code2.AI
• Multi-language support Works with multiple programming languages like Python, JavaScript, and Java.Found in Code Snippets AI
• Custom snippet libraries Allows you to save and organize your own reusable code snippets.Found in Code Snippets AI
• IDE integration Integrates with popular code editors and IDEs for a seamless workflow.Found in Code Snippets AI
• Regular snippet updates Keeps snippet collections up to date with new programming trends and standards.Found in Code Snippets AI
• Production-grade code Generates code that meets high standards and passes peer review on the first attempt.Found in Fei
• Codebase integration Works directly within your existing codebase, reusing components and hooks.Found in Fei
• Multiple input types Accepts inputs like Figma files, Jira tickets, screenshots, and plain text.Found in Fei
• Coding standards adherence Maintains your team’s coding standards and design system consistency.Found in Fei
• Requirement to code conversion Converts requirements into backend-ready code in minutes.Found in Fei
• Local code gathering Instantly gathers entire files or folders from your local codebase for AI analysis.Found in EchoComet
• API key enhancement Enhances AI prompts with additional context using your own API key.Found in EchoComet
• One-click copy to LLMs Copies code context to popular web-based LLMs like ChatGPT, Claude, Gemini, and Grok.Found in EchoComet
• Local processing privacy Keeps all code data on your machine without sending it externally.Found in EchoComet
• Token-count statistics Provides insights into lines, characters, and estimated AI token usage for better prompt management.Found in EchoComet
• Non-coding content generation Helps with tasks beyond coding, such as generating marketing content or product launch materials.Found in Code2.AI

What goes in, what comes out

The workflow

1. In
   Start with

   Repository files, requirements, design inputs and team standards

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository files

4. 3

   Requirements

5. 4

   Design inputs and team standards

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, source-linked code changes

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. Final code review, security checks and merge decisions remain with the development team. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Project and repository setup, Source-linked assistant workspace, Administrator console. Use a project list with repository status, a central editor and chat area, and a right-hand panel for sources, snippets, standards and review state. Let users compare generated code against the existing file. Display draft, changes requested and approved states. Provide an admin view of users, API keys, retention and export logs. Make the task-specific outcome reviewed, source-linked code changes visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, repository connections, user roles, API keys, snippet libraries, standards profiles, approval states, usage allowances, retention rules, export logs and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls and explicit approval for external actions.

Integrations and data access

Team-owned repositories, issue trackers, design files and permitted documentation sources. Code hosting, editor and IDE plugins, and CI destinations. Start with file exchange and validate destination specifications before promising direct commits. 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 project context or requirements; use the whole codebase for relevant suggestions. 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 and developers working in existing codebases use it to solve "developers switch between several AI coding tools and lose project context, standards and review evidence"?
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 code changes per developer hour and corrections after review.
4. Measure, then decide. Track accepted code changes per developer hour and corrections after review; 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 type and one primary language; final code review, security checks and merge decisions remain with the development team. Implement one approved input format, a bounded representative case set and the first two task modules: generate code from project context or requirements; use the whole codebase for relevant suggestions. Support the third module with operator review: answer plain-language questions about code and reasoning. 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 languages and repository types only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed, source-linked code changes. Retain the explicit scope boundary: One repository type and one primary language; final code review, security checks and merge decisions remain with the development team.

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 code review and security QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One repository type and one primary language; final code review, security checks and merge decisions remain with the development 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 500-2,500 fixed pilot for one defined repository package. Offer a monthly developer-seat allowance after repeat demand. Quote complex multi-repository or regulated-environment work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, source-linked code change set. Recurring fees must specify seat count, 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 switching while keeping code, context and review evidence in one owned workspace. Demonstrate a concrete reviewed, source-linked code change set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Software teams and developers working in existing codebases professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, source-linked code change set from a small authorized input set, with a transparent calculation of accepted code changes per developer hour and corrections after review and no promised savings.

The first 30 days

1. Week 1: interview five software teams and developers working in existing codebases and inspect a recent example of developers switching between several AI coding tools and losing project context, standards and review evidence.
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 code changes per developer hour and corrections after review, 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 code changes per developer hour and corrections after review. 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 code changes per developer hour and corrections after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, source-linked code changes. 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 standards, repository patterns and review examples, 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 developers working in existing codebases. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Code2.AI, Code Snippets AI, Fei and EchoComet, plus manual coding and generic AI chat tools. Compare this product with the buyer's present method on accepted code changes per developer hour and corrections after review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, repository indexing, storage, reviewer hours, developer revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed, source-linked code changes. Track cost per accepted change, including correction work, unsuccessful cases and support.

Preserve code ownership, source attribution, license compliance and usage permissions. Development teams approve substantive changes and merge scope. One repository type and one primary language; final code review, security checks and merge decisions remain with the development team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.