Inject your context into any conversation, informed by everything you've already built, with the click of a button.
Every time you start a new AI conversation, you're starting nearly from scratch. The AI only has partial memory of your project, your constraints, your past decisions, or the solutions you've already tried, and struggles bringing it back to the forefront. Context injection closes that gap — bringing the right background into every new conversation automatically.
Search your knowledge base from within any AI conversation or your personal tools (like VS Code) and inject the most relevant past context directly into your prompt — without leaving the page or copy-pasting across tabs.
Solved a problem in ChatGPT last week? Inject that solution into a new Claude conversation today. Context injection works across platforms — your knowledge is not siloed by which AI you happened to use.
If your codebase is synced via the VS Code extension, you can inject relevant code snippets and file context alongside conversation history. Give the AI the full picture — what you discussed and what you built.
Context injection is always deliberate — you choose what gets injected and when. ContextBridge surfaces the most relevant results; you decide what's worth including.
Context injection is built on top of the same hybrid retrieval system that powers semantic search. When you trigger a search from within an AI platform, the in-page modal runs a retrieval query against your stored knowledge base and returns ranked chunks ready to be inserted into your prompt.
The Chrome extension injects a lightweight search modal directly into supported AI platforms (ChatGPT, Gemini, Claude, Grok). The modal communicates with the ContextBridge backend via authenticated API calls, retrieves ranked results, and surfaces them without any page navigation or context switching.
Results are returned at the chunk level — individual message segments or code functions — rather than entire conversations. This keeps injected context focused and within the LLM's effective context window, avoiding noise from tangential content in the same conversation.
Before injection, retrieved chunks are passed through a synthesis step that generates a structured summary of the most relevant information — rather than dumping raw context, the AI receives a focused, actionable brief.
Retrieval draws from both conversation chunks and code chunks, fused via RRF into a single ranked result set. Source diversity quotas prevent any single conversation or file from dominating the injected context.
Today, context injection is deliberate — you search, you choose, you inject. The next evolution will be proactive: a system that monitors your new conversation as it unfolds, understands what the AI is being asked to solve, and automatically surfaces the most relevant prior context before you even think to look for it. Combined with broader source coverage and smarter intent detection, the goal is a context layer that behaves less like a search tool and more like a knowledgeable collaborator who was in every previous conversation you ever had. Come and build it with us: hiring exceptional talent at [email protected]
Install ContextBridge and every new AI conversation starts informed.