In Parallel

Overview

In Parallel maintains a live, shared understanding of your organization for AI models and teams. It captures goals, decisions, meetings, and progress automatically to eliminate context drift. By connecting popular AI assistants through the Model Context Protocol, it ensures every tool instantly accesses corporate history. The platform delivers enterprise-grade security with permission-scoped access and verified citations for every response.

Founder story

In Parallel was founded to solve the perpetual frustration of corporate alignment meetings and scattered context. Recognizing that workers waste countless hours re-explaining company goals to different AI models, the founders built a unified memory layer that keeps teams and agents synchronized automatically.

What it does

• - Passively captures micro-updates, decisions, and discussions across enterprise workflows to maintain a single source of truth
• - Exposes an MCP server that provides AI assistants like Claude and ChatGPT with persistent organizational context
• - Attaches verified source citations to every generated response to eliminate hallucination risks
• - Enforces permission-scoped access control so agents only retrieve data authorized for specific users.

Who it's for

• Engineering Teams
• Product Managers
• Operations Leaders
• Enterprise AI Developers

Why it works

• Eliminates repeated manual briefing of separate AI tools
• Standardizes context sharing via the open Model Context Protocol
• Provides exact source tracking and provenance for compliance
• Maintains strict enterprise security with EU hosting and granular permissions.

Growth strategies

• Integration with native MCP client ecosystems like Cursor and Claude
• Open developer sandbox for rapid prototyping and adoption
• Word- of-mouth adoption among engineering and product teams
• Content marketing targeting enterprise knowledge fragmentation

Alternatives

• Unabyss
• Pensieve
• Mem0
• Zep

Comparison overview

• Standard vector memory tools rely strictly on raw text embeddings without native cross-meeting synthesis.
• In Parallel integrates directly with workflow channels to maintain structured operational context over time.