Your AI forgets.
Kernal remembers.
An ambient knowledge graph that turns every conversation, meeting, and email into structured memory. AI memory built like a graph, not a blob. Local-first, MCP-native, SQL-simple, open source.
The Problem
Smarter models won't save your AI program.
The substrate will.
“Through 2028, more than 50% of AI initiatives will halt — not because the models stop improving, but because the plumbing fails.”
— Gartner · Litan, Ramirez, Powledge · 2025-2026
The bottleneck has moved. It's no longer model intelligence — it's the substrate the model sits in. Unresolved agentic identity. Untracked machine identities. Knowledge that starts at zero every session.
Most teams respond by adding layers: RAG, vector DBs, embedding services, “memory” SaaS, eleven kinds of orchestration. It works at demo time. It fails at year two. That's the 50%.
Kernal is the other path. A structured, persistent, ambient knowledge graph that lives below every agent you run. Same SQLite file on your laptop, your server, and your cloud. Same MCP protocol. Same answers, every session.
Proof of Concept
Now imagine this is
your client portfolio.
Fifty meetings. A hundred and ninety people. Fifteen hundred relationships. Every name, every connection, every promise your team has ever made — structured and queryable by your AI agent.
Real production data from enterprise consulting engagements.
Graph Intelligence
Ask questions that
search can't answer.
Vector search finds documents. Kernal traverses relationships.
Philosophy
Context is a feature.
Ambient is an architecture.
The first wave of AI products was context-aware: you tell the model what to know, it responds. ChatGPT memory. Claude projects. RAG-as-feature. The model is the brain; context is something you feed it.
The next wave — the one that survives Gartner's 50% cull — is ambient. The knowledge graph is there before you ask. Persistent across sessions, agents, products. You don't prompt it. It's resident.
Ambient is harder. It requires structure, not vibes. Schema, not vector blobs. A database that doesn't forget — and never assumes you'll re-tell it what it should already know.
Kernal is built ambient-first. Every conversation strengthens the graph. Every agent that connects via MCP inherits the same memory. Better models make Kernal more valuable, not less — because the graph is the durable layer, and the model is the rented one.
Architecture
Five layers of intelligence.
Every entity connects to every other via the relationship graph. The graph is the product.
Capabilities
What Kernal does.
Entity Extraction
Drop in a transcript. Kernal extracts people, organizations, topics, and the relationships between them. No templates. No configuration. Just structure.
Graph Search
Not just keyword matching. Query across relationships: “Who at Nordic Tech has influence over the SAP decision?” Kernal traverses the graph.
MCP Protocol
Plug Kernal into Claude, Cursor, or any MCP-compatible tool. Your agent gets structured context about your world — people, relationships, history — in every conversation.
Meeting Prep
Before any call, Kernal surfaces: who you're meeting, their org chart position, recent interactions, open action items, and strategic context. Automatically.
Data Sovereignty
Your graph. Your machine.
Your models.
Most AI knowledge tools send your data to someone else's servers. Kernal doesn't. No DPA needed. No data residency concerns. No vendor training on your client intelligence.
Local processing
Entity extraction runs via Gemma 4 on your machine. Your transcripts, client names, and deal details never leave your infrastructure.
Local storage
Your knowledge graph lives in a SQLite file on your device. No cloud database. No vendor access. Full portability.
Open protocol
MCP is an open standard, not a proprietary API. Connect Kernal to Claude, Cursor, or any compatible tool. No vendor lock-in.
Built For
Professional services that
run on relationships.
Kernal is designed for people whose work depends on knowing who connects to whom, what was said, and what it means.
Executive Search
Map candidate networks, board relationships, and organisational dynamics. Know who knows who before the first call.
Management Consulting
Build institutional memory across engagements. Every meeting, every stakeholder, every strategic decision — structured and searchable.
Strategic Advisory
Track deal pipelines, stakeholder influence, and client goals across your portfolio. Your AI agent knows the full picture.
Law Firms
Matter context that compounds. Client relationships, precedent connections, and engagement history — locally stored, never shared.
Get Started
Use the tool you already use.
Kernal is the brain underneath.
Same principle as Outlook or shared drive — a company asset issued on day one, surfaced through whichever AI host the team already trusts.
Self-hosted (free, open source)
Install locally with one command. Your machine, your data, zero dependencies.
Then add Kernal as an MCP server in whichever host you already use — Claude, Cursor, Codex, Copilot, ChatGPT.
Kernal Cloud (managed)
No install needed. Connect directly from your existing AI tool and start building your graph in seconds. Limited managed-access while the product stabilizes.
Talk naturally
Start having conversations. Kernal extracts and connects entities automatically. The graph compounds.
Community
Join the builders.
Book a demo
See Kernal in action. 30 minutes with the founder to explore how it fits your workflow.
Book a meeting →SubStack
Technical deep dives on agent architecture, knowledge graphs, and the MCP ecosystem.
Subscribe →Build agents
that remember.
See how Kernal works with your data. Book a 30-minute demo or start building with the open-source core.