Engineering Productivity
Engineering Tools That Know Your Codebase
AI assistants for code review, documentation, and operations—trained on your conventions, not generic patterns.
The Productivity Multiplier
Your engineering team spends significant time on activities that aren't writing code: reviewing pull requests, writing documentation, maintaining runbooks, answering questions about existing systems.
Generic AI coding assistants help at the margin—but they don't know your architecture, your conventions, or your specific systems. The suggestions require heavy editing to match your standards.
Custom AI tools, trained on your codebase and conventions, provide leverage that generic tools can't match.
What We Build
Not generic linting—specific feedback based on how your team writes code.
Code Review Assistance
AI that reviews PRs against your standards: coding conventions, security practices, performance patterns, and architectural guidelines. Human reviewers focus on design decisions.
Documentation Generation
Transform code into documentation that follows your templates: API references, README files, architecture decision records, and onboarding guides.
Runbook Automation
Generate operational runbooks for new services: deployment procedures, monitoring setup, incident response, and rollback processes.
Architecture Q&A
Answer questions about your systems: "How does authentication work?" "What calls this service?" "Why is this implemented this way?"
Test Generation
Produce test scaffolding based on your testing patterns: unit tests, integration tests, and E2E scenarios that follow your conventions.
Typical Impact
Training on Your Context
Engineering AI tools are only useful if they understand your specific environment.
Codebase Learning
The assistant learns your patterns from your actual code—not generic examples from public repositories.
Convention Encoding
Your style guides, architectural principles, and best practices are encoded in how the assistant operates.
System Understanding
Context on your services, their interactions, and their operational characteristics.
Continuous Updates
As your codebase evolves, the assistant's understanding updates accordingly.
Prompt Engineering for Engineering Tools
Style Matching
Generated code and documentation match your conventions—formatting, naming, and structural patterns.
Context Awareness
Suggestions consider the specific file, service, and broader system context.
Safety Boundaries
The assistant never suggests patterns you've explicitly prohibited—security antipatterns, deprecated approaches, or architectural violations.
Explanation Depth
Feedback includes reasoning, not just suggestions. Junior engineers learn from the assistant's explanations.
Integration Points
Engineering tools integrate into your workflow:
IDE integration
Suggestions and assistance in VS Code, JetBrains, or your preferred environment
PR workflow
Review comments on GitHub, GitLab, or your platform
Slack/Teams
Q&A access where your team communicates
CI/CD
Quality checks as part of your pipeline
Privacy and Security
Your code is sensitive. Our engineering tools:
- Run in your infrastructure — code never leaves your environment
- Use your access controls — the assistant sees only what authorized users can see
- Log appropriately — queries and responses are auditable
- Respect boundaries — sensitive repositories can be excluded
We design for environments where code security is non-negotiable.
Ready to give your engineering team AI-powered leverage?
We'll analyze your workflows, identify high-impact opportunities, and show you what custom engineering tools can accomplish.