Solving the Context Amnesia Problem in Modern AI Workflows

The Stateless Nature of Large Language Models

Large language models process tokens within a fixed context window and produce output. When a session ends, nothing persists. The next conversation begins with no knowledge of prior interactions, user preferences, or organizational context. This is not a capability failure — it is an architectural constraint. As one benchmark study observed: LLMs are stateless, they start each interaction without any context or memory, leaving power untapped and insight lost.^[1]

For professionals using AI as a core part of their daily workflow — developers, product managers, researchers, founders — this is a compounding productivity tax. Users spend 10–15 minutes per session re-establishing context they have already established dozens of times before.

Scale of the Problem

The Multi-Platform Compounding Effect

The problem is not confined to a single model. Professional AI users move fluidly between platforms — Claude for long-context reasoning, GPT-4 for integrations, Gemini for Google Workspace, Copilot inside developer toolchains. Each platform switch resets context entirely. Even if one platform implemented persistent memory, that memory would not follow users to other platforms. The context problem is structural and cross-platform — it cannot be solved by any single AI provider.

Product Overview

Conxt is a persistent, cross-platform memory layer for AI users. It operates as a Chrome extension that passively monitors AI conversations and extracts structured memory — without requiring users to change how they work. Extracted memories are stored in a user-controlled dashboard and made available across every AI session the user opens.

The core value proposition: the model starts knowing who you are, what you have decided, and how you work — every time, on every platform.

How Capture Works

The Conxt Chrome extension monitors conversation streams on supported AI platforms. When a session ends or a sufficient unit is detected, the extension transmits the session hash and content to the Conxt extraction engine — a two-stage pipeline:

Extracted memories are written to Supabase with pgvector embeddings, enabling semantic retrieval in addition to structured filtering. Users review and approve memories before injection.

Memory is not monolithic. Different categories of context have different value, shelf life, and retrieval patterns. Conxt classifies all captured memory into seven distinct types — derived from analysis of hundreds of professional AI sessions.^[4]

System Components

Authentication & Privacy

Authentication uses Supabase Auth with a supplementary JWT verification endpoint via PyJWT. No raw conversation text is stored. Memory records are user-scoped by default — team sharing is opt-in and explicit. Users can archive, edit, or delete any record at any time.

Individual memory is powerful. Shared memory is transformative. When a team works with AI tools, the context problem multiplies — each person loses their own context between sessions, and cannot share what they have built with teammates. The result is redundant re-onboarding and knowledge locked in individual conversation histories.

How Team Workspaces Work

The most significant use case is onboarding. When a new team member joins, they immediately have access to accumulated AI context — decisions made, tools chosen, coding rules, workflows. What previously required weeks of knowledge transfer becomes available on day one.

The memory problem in AI has attracted significant attention from research institutions and startups. The landscape divides into three categories: developer infrastructure tools, single-platform memory features, and general capture products.

Conxt occupies a distinct position: platform-neutral, consumer-facing, with structured classification and a human-in-the-loop review step. Developer tools like Mem0^[4] and Zep^[10] require technical integration. Single-platform solutions like OpenAI Memory are siloed. Conxt is the only product combining passive capture, structured seven-type memory, cross-platform injection, and a consumer dashboard with no technical setup required.

Primary Market: AI-Native Professionals

Secondary Market: AI-Native Teams

"40% of enterprise applications will feature task-specific AI agents by 2026, up from less than 5% in 2025" (Gartner, August 2025).^[8] These agents fail without memory. Conxt provides the memory layer that makes agent deployment viable at team scale.

Shipped

Next 90 Days

6–12 Months

The context amnesia problem is the most consequential unsolved friction in professional AI usage today. The models are extraordinary — the infrastructure around them has not kept pace. Every session starting from zero is a compounding tax on the productivity that AI is supposed to unlock.

Conxt solves this with a simple insight: memory should live outside the models, belong to the user, and work across every platform simultaneously. The seven-type classification system, passive capture architecture, and team workspace design together create a memory layer that is immediately useful, deeply personalized, and progressively more valuable with every session.

The models will continue to improve. Conxt builds the memory that makes that intelligence permanent.

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