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Nabla-X EdgeMind

Expert intelligence, no connection required.

We train a custom "expert" model on your organization's own systems and data, then package it to run fully offline — on-prem, air-gapped, or embedded directly on-device. Built for regulated finance, healthcare, research, and industrial environments where the data simply cannot leave.

See EdgeMind in action: Industrial Diagnostics Case Study →
What ships
One compose stack. No outbound calls.
Inside the box
App · Postgres · Ollama
Plus a local REST layer, so the same code that runs hosted runs here unchanged.
Sign-in
Local account
No identity provider on the internet. The session never leaves your network.
Models
Yours to choose
Any Ollama model. CPU-friendly defaults; a GPU makes it fast.
Health refuses to pass until every model is localpull the cable and it keeps working — that is the test

Core Capabilities

Runs Fully Offline

No API calls, no cloud dependency, no data ever leaving your network — on-prem server, air-gapped facility, or embedded on-device.

Trained on Your Systems

Not a generic foundation model — an expert distilled from your own schemas, logs, documents, and domain vocabulary.

Updates Without the Cloud

Retrain and redeploy new model versions on a schedule you control, packaged for delivery to disconnected environments.

How EdgeMind Works

From your live systems to a deployable expert model, with nothing ever leaving your boundary.

1. Capture Your Systems' Data

We work inside your environment to profile schemas, documents, logs, and domain vocabulary — the raw material the expert model will learn from.

2. Train & Distill the Expert Model

A capable base model is fine-tuned and distilled into a smaller, specialized expert — accurate on your domain, small enough to run without a data center.

3. Package & Deploy On-Prem / Edge

Delivered as a self-contained package — Docker image, appliance, or embedded runtime — for your air-gapped server, industrial PC, or device.

Model Distillation Pipeline

Your Systemsschemas · logs · docsEdgeMind EngineDomain Fine-TuningKnowledge DistillationQuantizationOffline ValidationOpen-Weight Base ModelModel PackageOn-Prem / EdgeDeployment

How EdgeMind Thinks

The reasoning and methodology behind turning a general-purpose model into a small, specialized, deployable expert.

Domain Distillation

Compresses a capable general model down to what your systems actually need — narrower scope, smaller footprint, no loss on the tasks that matter.

  • •Task-specific fine-tuning on your real queries and documents
  • •Knowledge distillation from a larger teacher model
  • •Quantization for CPU/edge-hardware inference

Air-Gap Verification

Every package is tested with outbound network access physically blocked, so "offline" is a verified property, not a claim you have to trust.

  • •Firewall-blocked integration testing before delivery
  • •No telemetry, no phone-home, no silent updates
  • •Reproducible offline build artifacts

Accuracy Honesty

A small local expert model is not the same as a frontier cloud model — we measure and report the real gap rather than paper over it.

  • •Structured eval suites against your real questions
  • •Side-by-side offline vs. cloud accuracy reporting
  • •Clear disclosure of where the local model is weaker

Update Without Reconnecting

New model versions are trained centrally, then delivered as signed offline packages your team installs on your own schedule.

  • •Versioned, signed model packages
  • •Manual or scheduled offline rollout
  • •Full rollback to any prior model version
Edge Model Intelligence

Technical Architecture

A distillation and packaging pipeline that turns a general-purpose model into a small, verified, fully offline expert — deployable anywhere from a server rack to a single device.

Capture
Profile your systems
Distill
Train the expert model
Verify
Air-gapped accuracy tests
Deploy
Ship the offline package

Distillation Engine

Teacher → student compression
Domain Fine-Tuning95%
Knowledge Distillation90%
Int8 / GGUF Quantization93%

Offline Verification

Proof, not a promise
Firewall-Blocked Testing100%
Structural Accuracy Eval88%
Zero-Telemetry Confirmation100%

Deployment Targets

Air-Gapped Server
Industrial PC
Medical Device
On-Prem Cluster
Embedded Runtime
🔌

Zero-Connection Inference

No API calls, no telemetry, no phone-home — verified by testing with outbound network access physically blocked.

📦

Portable Model Packages

Docker image, signed binary, or embedded runtime — sized to fit an air-gapped server, industrial PC, or single device.

🎯

Measured, Not Marketed, Accuracy

Every deployment ships with a real offline-vs-cloud accuracy comparison, so your team knows exactly what trade-off it's making.

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Applied intelligence for healthcare, finance, and research. Building AI that understands how complex systems move.

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