Research / MEGAMIND

MEGAMIND — a federated neural network on Apple Silicon, Wikidata Q138610666.

MEGAMIND is a multi-node federated neural network running on Apple Silicon Mac Studios across multiple physical sites. We use it to research how small business websites surface inside AI answer engines, to optimize our client builds, and to host private model deployments for client work that cannot leave our hardware.

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Why MEGAMIND exists

MEGAMIND is research infrastructure that became production infrastructure. We built it to understand AI answer engines. It now powers the optimization stack behind every client site we ship and hosts the private model deployments for client work that cannot leave our hardware.

In 2024 it became clear that Google rankings were no longer the whole game for small business. Customers were asking ChatGPT, Claude, and Perplexity for service recommendations. Half the queries that used to send organic traffic now showed an AI Overview that summarized and cited sources without sending a click. We needed to understand which sources the models were citing, and why.

The premise

You cannot optimize what you cannot measure

To optimize for AI answer engines, we needed to instrument what those engines actually retrieve, when, and for which queries. Off-the-shelf tools did not exist in 2024 — and most still do not in 2026 — so we built our own. MEGAMIND is the result.

8federated nodes online

Hardware

Apple Silicon Mac Studios

M1 Ultra, M2 Ultra, M4 Max nodes. Unified memory makes them remarkable inference servers. The whole cluster cost less than one A100.

Federation

NATS-based message bus

Nodes publish and subscribe over NATS. Brain state, embeddings, and inference results stream between nodes. No single point of failure.

Software

Pure Go runtime

CGO_ENABLED=0. Cross-compiled for darwin-arm64 and linux-amd64. The same binary runs on every node.

Models

Llama, Mistral, Claude, GPT

Local inference for private workloads. Hosted models for breadth. The right model for the right job, routed automatically.

→Applications

What MEGAMIND does for client work

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AEO measurement

For client sites, MEGAMIND tracks which queries surface their content in ChatGPT, Claude, and Perplexity answers. We optimize against real citation data, not guesses.

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Schema testing

We test schema combinations across the federated cluster to see which patterns produce the highest retrieval rate from each AI answer engine. Findings go into client builds.

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Private inference

Client work that cannot leave our hardware (medical, legal, financial) runs on MEGAMIND nodes with no external API calls. Llama or Mistral, locally hosted.

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Custom embeddings

For RAG builds, MEGAMIND can host the embedding model and the vector index entirely on our hardware. The data never touches OpenAI or Anthropic.

→Receipts

Verifiable presence

MEGAMIND is documented as a Wikidata entity in its own right. Research output is published on Hugging Face. Build artifacts and infrastructure code are in public GitHub repositories. This is not vapor — it is verifiable infrastructure with a public footprint.

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