We build custom AI coding tools that understand your architecture, enforce your standards, and give your engineering team a knowledgeable pair programming partner.
Tools like GitHub Copilot offer generic code completion, but they do not understand your specific codebase, architecture patterns, or coding standards. A custom AI code assistant is trained on your repositories, documentation, and conventions, giving it the context to generate code that actually fits your project rather than generic suggestions that need extensive modification.
Custom code assistants are particularly valuable for large engineering organizations where consistency matters. When your codebase has established patterns for error handling, logging, database access, and API design, a custom assistant enforces these patterns automatically. New engineers get productive faster because the assistant guides them toward the right patterns from their first day.
Arthiq builds AI code assistants that go beyond completion to provide code review, bug detection, refactoring suggestions, documentation generation, and test creation. These capabilities are customized to your technology stack, coding standards, and architectural principles.
Making an AI assistant codebase-aware requires sophisticated indexing and retrieval systems. We build RAG pipelines that ingest your entire codebase, including source files, documentation, configuration, and commit history. The system understands not just the code but the relationships between components, the project structure, and the conventions that govern how code is written.
Our indexing strategy goes beyond simple file-level chunking. We parse code into semantic units like functions, classes, and modules, preserving their structural relationships. This lets the assistant understand that a particular function belongs to a specific module, implements a certain interface, and follows a defined pattern. When generating new code, it can reference the most relevant existing implementations.
We also integrate with your development workflow tools. The assistant can read issue descriptions from your project tracker, reference pull request discussions, and access your internal documentation wiki. This broader context helps it make suggestions that are aligned with your current development priorities and team conventions.
Arthiq builds AI code review capabilities that catch issues before they reach human reviewers. The assistant analyzes pull requests for potential bugs, security vulnerabilities, performance issues, style violations, and deviations from established patterns. It provides specific, actionable feedback with references to relevant coding standards or similar code in the repository.
For security-sensitive codebases, we train the assistant to recognize common vulnerability patterns including SQL injection, cross-site scripting, insecure deserialization, and secrets in code. These checks complement static analysis tools by understanding the semantic context of code patterns rather than relying solely on syntactic matching.
Test generation is another high-value capability. The assistant can generate unit tests for new code, suggest edge cases that should be tested, and identify existing tests that might be affected by changes. This improves test coverage while reducing the time engineers spend writing routine test cases.
A code assistant is most useful when it integrates seamlessly into existing development workflows. We deploy assistants through IDE extensions, CLI tools, and CI/CD pipeline integrations so engineers can access AI assistance wherever they work. IDE integrations provide real-time suggestions as engineers type. CLI tools support batch operations like documentation generation or codebase analysis. CI/CD integrations automate code review and quality checks.
We also build web-based interfaces for conversational interaction with the codebase. Engineers can ask questions like "how does the payment processing work" or "show me examples of how we handle database transactions" and get contextual answers with code references. This is particularly valuable for onboarding new team members and for cross-team knowledge sharing.
All assistant interactions are logged and analyzable, giving engineering leadership visibility into common questions, frequent code patterns, and areas where engineers need the most assistance. These insights inform documentation improvements, architecture decisions, and training investments.
Arthiq engineers build AI code assistants because we are engineers ourselves. We understand the workflows, frustrations, and needs of development teams from firsthand experience. Our assistants are designed by engineers for engineers, with a focus on providing genuine value rather than superficial automation.
We deliver code assistant projects in iterative phases. The first phase indexes your codebase and enables basic Q&A and code search. Subsequent phases add code generation, review automation, and workflow integrations based on your team feedback and priorities.
Contact us at founders@arthiq.co to discuss how a custom AI code assistant can accelerate your engineering team and improve code quality across your organization.
Our engineers will build an AI code assistant that understands your codebase, enforces your standards, and makes your development team significantly more productive.