Gradien develops AI platforms that model and improve an organization’s internal workflows. The system first observes how information and decisions move through existing processes, then structures and coordinates that data before applying machine‑learning to return completed work and outcomes back into the workflow, enabling the next cycle to start further ahead. By continuously learning from real‑world operations, Gradien’s technology helps enterprises automate and accelerate routine tasks.
Funding
Funding not disclosed
Founders
Product
Problem
Teams that rely on multiple AI assistants and collaboration tools lose the context of prior decisions, corrections, and outcomes when switching between applications. This forces users to repeatedly re‑explain background information, leading to wasted time and fragmented knowledge.
Solution
Gradien’s Core acts as a continuity layer that sits beneath the AI surfaces and work tools teams already use. By connecting to platforms such as ChatGPT, Claude, Cursor, Gmail, Google Drive, Notion, Slack, GitHub, and others, Core observes the flow of information and captures the workpath behind completed tasks—decisions, preferences, revisions, and results—once a user approves the content. The approved knowledge is then indexed and made instantly retrievable inside any connected AI tool, so the next conversation starts with the relevant context already present. Core does not replace existing assistants, nor does it retrain underlying models; it simply provides a unified, approved knowledge store that follows the user across tools, reducing repetitive prompting and accelerating workflow cycles.
Target Audience
Primary customers are knowledge‑intensive teams—such as product development, marketing, engineering, and consulting groups—that use multiple AI assistants and collaboration software to create and iterate on work.
Features
- OAuth‑based connections to major AI assistants (ChatGPT, Claude, Cursor, Codex, Claude Code) and collaboration platforms (Gmail, Google Drive, Notion, Slack, Microsoft Teams, GitHub, Linear, etc.)
- Context capture workflow that records decisions, drafts, corrections, and outcomes, requiring explicit user approval before the information becomes part of the knowledge store
- Unified knowledge retrieval that surfaces approved context directly within any connected AI surface, eliminating the need to re‑enter background information
- Continuity layer architecture that operates beneath existing tools without introducing a separate chatbot interface
- Secure handling of captured data with per‑user access controls and compliance with OAuth scope limitations
- Real‑time synchronization of knowledge across all connected applications, ensuring the latest approved information is always available
- Administrative dashboard for managing connections, approvals, and usage analytics