Internal developer copilot powered by Gemini Code on Vertex AI — trained on your codebase, architecture docs, and runbooks, deployed in your IDE and developer portals. Codebase-aware code completion, Q&A, test generation, and service scaffolding — all within your GCP environment.
We implement a production-grade internal developer copilot using Gemini Code and Vertex AI Search — indexed over your repositories, architecture decision records, runbooks, and API documentation. Developers get codebase-aware assistance in their IDE and developer portal without sending any code outside your GCP environment.
New engineers and cross-team contributors lose 30–40% of their time searching for the right functions, understanding patterns, and decoding undocumented legacy code — a massive drag on velocity.
Architecture decisions, runbooks, and API documentation are spread across Confluence, Notion, GitHub, and email threads — impossible to surface at the moment a developer needs them.
Scaffolding new services, writing standard error handling, generating unit tests, and producing API clients consumes engineering hours that should be spent on product logic.
Gemini-powered search over your entire repository — ask questions about how a feature works, find the function that handles a specific task, or locate all usages of an internal API in plain English.
Code completions that understand your internal libraries, naming conventions, and architectural patterns — not just generic suggestions based on general training data.
Ask the copilot about deployment procedures, incident response runbooks, and architectural decisions — it retrieves the relevant documentation and explains it in context.
Generates unit tests for your functions based on your existing test patterns — covering happy paths, edge cases, and error conditions, written in your test framework of choice.
Generate new service stubs, API routes, repository patterns, and integration boilerplate that follow your internal standards — reducing service creation from hours to minutes.
No. The entire pipeline runs within your Google Cloud project under VPC Service Controls: repositories are indexed in Vertex AI Search within your VPC, Gemini inference runs on Vertex AI within your IAM perimeter, and no code or queries are used for Google model training. This is a contractually isolated, enterprise-grade environment — fundamentally different from consumer coding tools.
We build a RAG pipeline over your codebase using Vertex AI Search: repositories are indexed at the function and file level with semantic embeddings. When a developer queries, the agent retrieves the most relevant internal code context, architecture docs, and runbook sections, then passes them to Gemini as grounding context. The copilot knows your patterns without fine-tuning, and stays up to date as your codebase evolves.
We integrate as a VS Code extension, JetBrains plugin (IntelliJ, PyCharm, WebStorm), or via a REST API consumable by any IDE or developer portal. For Backstage or custom developer portals, we provide a chat widget component. All channels share the same Vertex AI backend.
A production-ready internal developer copilot with codebase indexing, Gemini-grounded Q&A, IDE plugin, and usage analytics typically takes 3–4 weeks from scoping to go-live. The primary variable is repository size and the number of documentation sources to index.
Give your developers a codebase-aware copilot that lives in their IDE, knows your internal patterns, and never sends your code outside your GCP environment. Deployed in 3–4 weeks.