Use Case: Offline / Regulated Work

What you’re solving for

You work in a regulated industry (healthcare, finance, defense, legal), on an air-gapped machine, or under a policy that forbids sending source code to third-party services. You still want AI-assisted code understanding — without the code leaving the building.

Why NeuralMind fits

• No API calls by default. Indexing, embeddings, retrieval — all local. The one exception is opt-in and documented below.
• No cloud account required. No sign-up, no telemetry, no outbound network in the default configuration.
• No code uploaded anywhere by the default path. ChromaDB runs in-process. The one opt-in LLM path (below) is a narrower but real exception — read it before assuming “never.”
• Pairs with local LLMs. Use with Ollama, llama.cpp, vLLM for an end-to-end local stack.

The one opt-in exception — read the fine print. Setting both NEURALMIND_LLM_SEED=1 and ANTHROPIC_API_KEY reads whatever bytes exist at two fixed paths — README.md and docs/architecture.md relative to the project root — and sends up to the first 8,000 characters (combined) to Anthropic’s API to seed synapse edges. This is a path-based read, not a content classifier: the code does not verify the file is prose before sending it. In the overwhelmingly common case these are ordinary documentation files, but a README.md that embeds real code snippets, API keys in example blocks, or internal architecture detail — or a README.md/docs/architecture.md that is itself a symlink to something else — would have that content sent unfiltered. If your policy prohibits any client file reaching a third party regardless of its usual contents, don’t opt in, and audit what’s actually at those two paths before you do. Both env vars are unset by default; leave them unset and this path never runs at all. Fail-open: any error returns 0 and never blocks indexing. Full disclosure, code-cited: docs/compliance/THIRD_PARTY_LLM_DISCLOSURE.md.

Fully local stack

pip install neuralmind
neuralmind build .              # local embeddings, local vector store

Pair with a local model:

# Example: Ollama + NeuralMind
ollama pull llama3.1:70b
CONTEXT=$(neuralmind query . "how does auth work?")
echo "$CONTEXT" | ollama run llama3.1:70b "Explain the auth flow"

Nothing in NeuralMind’s own build/query runtime touches the public internet under the default configuration (NEURALMIND_LLM_SEED unset, which it is unless you set it) — that’s distinct from the setup commands above (pip install, ollama pull), which do need internet access the first time you run them, same as installing any package.

Compliance-friendly properties

Air-gapped install

1. On a connected machine:

   pip download neuralmind -d ./offline-bundle
   

2. Copy ./offline-bundle/ to the air-gapped machine.
3. Install:

   pip install --no-index --find-links ./offline-bundle neuralmind
   

ChromaDB pulls its embedding model on first use — download it in advance or point HF_HOME at a pre-populated directory.

Turning off query memory (if your policy forbids local logs)

export NEURALMIND_MEMORY=0
export NEURALMIND_LEARNING=0

Or decline the TTY consent prompt the first time neuralmind query runs. No events are logged.

Audit trail

Every action is a local file operation — easy to log via existing endpoint monitoring:

• Knowledge graph: graphify-out/graph.json
• Vector store: graphify-out/neuralmind_db/
• Query events (only if opted in): .neuralmind/memory/query_events.jsonl
• Learned synapse memory: .neuralmind/synapses.db (the Hebbian graph the synapse layer learns automatically from usage)

Delete any of these at any time — nothing persists outside your project.

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