Your AI tools should
share a brain.
Claude doesn't know what you told ChatGPT. Sulci is the infrastructure layer that fixes it: a dual-indexed knowledge layer that captures context from every AI interaction and serves it back, structured and ranked.
sul·ci /ˈsʌlsaɪ/ · the folds and grooves of the cerebral cortex. Where memory lives.
Every session
starts from zero.
You use multiple AI tools every day. None of them share memory. Every decision you made in ChatGPT is invisible to Claude, and vice versa. You re-explain context you already explained. Decisions get made twice. The AI that knows your stack is not the one you are talking to right now.
Today
5 tools. 5 isolated memories.
With Sulci
One knowledge layer. Every tool.
Four stages.
One pipeline.
Capture, extract, store, serve. Every interaction follows the same path. Every piece of knowledge ends up structured, indexed, and available.
Capture
Every interaction is queued for processing. MCP server, API proxy, REST, or Chrome extension — the source does not matter.
Extract
An LLM analyzes each conversation and extracts structured knowledge atoms, classified by type and scored by importance.
Store
Dual-indexed: Postgres for structured queries, pgvector for semantic search. Each index does what it does best.
Structured queries need relational storage. Semantic retrieval needs vector indexes. A single store means compromising both. Sulci uses two.
Serve
CORE atoms injected first, regardless of query. Then ranked retrieval by semantic similarity, confidence, and freshness.
Seven types of
structured knowledge.
Every piece of information extracted from your conversations is classified, scored, and stored as an atom. Not raw text. Not a full conversation. A discrete, typed unit of knowledge that can be retrieved and ranked on its own.
Injected into every context response, regardless of query. For standing instructions and non-negotiable constraints.
Architectural choices already made. Gets retrieval priority when the query touches the decision domain.
Stack choices, implementation details, configuration. High weight on technical queries.
Style and behavior preferences. Ensures the AI works the way you work.
Temporal Validity
Atoms carry an expiry date. Expired atoms are excluded from all context responses automatically. Useful for sprint goals, code freezes, and time-limited constraints.
Atom Supersession
A new atom can supersede an old one. The old atom is marked expired on save. When a decision changes, the new one surfaces and the old one disappears. No cleanup needed.
The hard questions.
Written down.
Why atoms and not raw embeddings?
Raw embeddings preserve conversations but lose structure. An embedding of "we decided on PostgreSQL" sits semantically close to "we considered PostgreSQL." The atom model forces classification: one is a DECISION, one is noise. Classification changes what gets injected and when.
Why dual indexing?
A vector store finds semantically similar content but cannot answer "give me all CORE atoms" or "what expired this week." A relational store handles structured queries but cannot rank by semantic similarity. Both indexes run on the same data. Each query type hits the right one.
Why is Core Knowledge separate from retrieval?
Retrieval is query-dependent. An atom about your preferred test framework will not score high when you ask about deployment. But that preference should always be in context. CORE atoms bypass retrieval entirely. They go in first, every time.
Six ways in.
One knowledge layer.
MCP Server
Claude Desktop & Claude Code
Native Model Context Protocol integration. 8 tools covering knowledge CRUD, context queries, conflict resolution, and project management.
Available Tools
Chrome Extension
ChatGPT, Claude, Gemini, Perplexity
Passive background capture. Conversations processed automatically. Nothing manual.
VS Code Extension
Visual Studio Code
Context sidebar in the editor. Relevant knowledge surfaces as you work, with a query box for your knowledge base.
API Proxy
OpenAI & Anthropic APIs
Transparent proxy that intercepts API calls, injects relevant context, and captures interactions. Zero code changes required.
REST API
Any Application
Full HTTP API for knowledge CRUD, context queries, interaction ingestion, data export, and privacy controls.
Dashboard
Visual Management
Browse knowledge, view entity graphs, inspect injection audit logs, manage exports.
Built on convictions,
not compromises.
Privacy-First
Your knowledge stays on your machine by default. No cloud dependency. Export or purge at any time.
Provider Agnostic
Works across providers, tools, and workflows. Your context belongs to you, not to a platform.
Intelligent Decay
Relevance is a function of time, frequency, and confidence. Old decisions fade. Current context surfaces.
Fully Transparent
Every piece of injected context is logged and auditable. You can see exactly what Sulci told your AI, when, and why.
Multi-Tenant Ready
Sulci Cloud supports full multi-tenancy with Supabase Auth. Every user's knowledge is isolated. Tier-based quotas and team sharing built in.
Stop re-explaining yourself.
Two ways to get started. Self-host with full data control, or sign up and go in the cloud.
hello@lopez.fi
Why I built this
I spend my days moving between Claude, ChatGPT, and Copilot. Re-establishing context became the bottleneck, not the AI itself. Sulci started as a personal tool. It is now live at sulci.xyz, and every developer who does the same faces the same invisible tax.
Let's build something
worth talking about.
I take on a limited number of advisory and fractional engagements. Only projects where I can make a real difference. If you're navigating growth, AI, or revenue challenges in a technical B2B environment, let's talk.