Science has journals. Law has case law. Software has git. Every serious discipline has infrastructure that turns individual effort into cumulative progress. One scientist dies, but their papers survive. One judge retires, but their rulings become precedent.
The problem
Knowledge work in the AI era has no equivalent. Strategy sessions with Claude. Research deep-dives with ChatGPT. Debugging marathons with Claude Code. Architecture discussions with Gemini. A million tokens of reasoning, gone when the session ends. The next conversation starts from scratch. No way to build on what came before.
This isn't a storage problem. It's a publishing problem. What's missing isn't bigger context windows. It's better publishing - the infrastructure that turns one session's thinking into context the next session can build on.
Our thesis
We build for the edge. The people who push AI tools harder than anyone thought reasonable. The ones spending thousands on tokens, running agent stacks, treating AI as infrastructure for thinking - not a toy. They expect a lot from these systems, and they know when something is watered down.
Nessie makes your thinking legible to every AI you use. We sync conversations automatically across ChatGPT, Claude, Gemini, Perplexity, Claude Code, OpenClaw, Cowork, and Codex - and structure them into context that humans can scan and agents can query. Full CLI and MCP access. Open infrastructure, not a closed workspace.
The line between consumer and developer infrastructure is disappearing. The people who use AI most seriously are both consumers and operators. They use Claude Code all day and ChatGPT in the evening. They run their own agent harnesses and also think out loud in voice conversations with Gemini. They need infrastructure that meets them everywhere they think - not another workspace to move into.
Design philosophy
We don't offload taste to the user
Organization is our job, not yours. What gets structured, what's stale, what context to surface, what matters and what doesn't - Nessie decides. Every tool in the knowledge management space gives you building blocks and says "design the system yourself." We designed the system. That's the product.
Contexts are living artifacts, not snapshots
A context that was useful last week might be stale today. Freshness, provenance, and correction propagation are our problem to solve - the user shouldn't have to audit their own context layer. Nessie should know what each claim came from, what superseded it, and when to surface uncertainty.
Meet people where they already think
We don't try to become your AI tool. You think in ChatGPT, Claude, Gemini, Claude Code - wherever you already are. Nessie is the layer above, not another surface competing for your attention. And within this wedge - AI conversations - we capture everything. Every provider, every conversation, every agent session. Because 80% of the picture is 5x worse than 100%.
Design for both humans and agents
Every artifact Nessie produces should be readable by a person scanning it and queryable by an agent that needs the context. The format is the protocol. This is how the product scales from one person with a few AI tools to one person with 500 agents - without a redesign.
Where this goes
Knowledge work is about to restructure around AI the way software development restructured around git. Before git, code lived on individual machines. After git, every change was versioned, attributable, and composable across teams. Git didn't start as enterprise infrastructure. It started as one person's tool for tracking their own work. Then it became the substrate for all of software.
AI-native work is at the "before git" stage. Your reasoning lives in chat sessions that evaporate. Your decisions have no version history. Your context can't compose with anyone else's. The infrastructure for cumulative AI-native knowledge work doesn't exist yet.
Nessie starts where thinking actually happens: individual AI conversations. One person, a few AI tools, thinking that compounds. But the primitives we're building - reliable recall, structured entities, source attribution, trajectory tracking, and artifacts readable by both humans and agents - are the same primitives a team of 5 humans and 50 agents would need to stay coherent.
We think the context layer for knowledge work has to be built for a new category of companies - AI-native, small, fast-moving, where the stack is entirely in AI from day one. It's not just a software problem. It's a political and ambient problem. But a more distributed org structure has a better shot at it. That's why we start with the individual and scale through use, not through procurement.
The current approach - connect Slack, Notion, and email from the top down and hope synthesis happens - gets the direction of adoption backwards. The actual thinking now happens in AI conversations, not in documents and workspaces. Those conversations are private, personal, scattered across providers. No IT team is going to index them. The context layer has to start where the thinking starts.
Nessie starts by making individual thinking legible. It scales into an orchestration layer: agents query grounded context, corrections propagate, stale claims get surfaced, and the system maintains itself in the background. We are building the infrastructure for cumulative AI-native knowledge work from the bottom up.