← All use cases
Private AI, at work

Developers, AI Engineers & Builders

Digital Twin Pro private AI appliance on a developer's desk beside a keyboard and monitors

Private AI fabric and local agent orchestration

You could build this — flash the board, quantize the models, wire the gateway, maintain it yourself from then on. Digital Twin Pro is the version where that weekend already happened: a tuned NVIDIA Jetson appliance running local models with messaging hooks and an API you can hit from your laptop. Own the whole stack, skip the yak-shave, and keep every token on your own silicon.

Private AI + frontier AI

Everyday work runs privately on hardware you own — no subscription, even offline. When you want the most capable models, your assistant can call Claude, GPT, or Gemini through your own account.

Kept at the cutting edge

AI moves fast. Covered fixes keep the supported software version stable, and a one-time Software Refresh is available whenever you want the latest image. You own it outright and stay in control — all hardware and software required to use it is included.

A supercomputer on call

For approved large jobs, Private Large-Job Processing is available as a one-time service — finished results return to your appliance.

NVIDIA hardware on your desk

Your docs, ADRs, and notes — one private index

Design docs, ADRs, runbooks, and years of technical notes — indexed locally on real NVIDIA silicon you control. Ask why the architecture decision was made, which docs contradict the current implementation, and what the postmortem actually concluded. Full local control, no data agreement required.

Getting it loaded is a one-time service: with Life Upload, your approved archive is prepared and imported privately — and from then on, asking is instant. For truly heavy jobs, approved one-time large-job processing is available, and Digital Twin Pro Spark brings 128GB of unified memory when the work outgrows the desk unit.

A day with your private AI

Waking up to a fully compiled report on last night’s code updates from your assistant, you dive straight into debugging with her real-time analysis. By midday, she’s already scheduled meetings, optimized your project roadmap, and even initiated a secure data transfer protocol test. Post-lunch, she handles incoming queries from clients while you fine-tune an AI model, all seamlessly coordinated without lifting a finger.

Saving over hours each week, this setup isn’t just about efficiency—it’s about privacy. Your sensitive client data and proprietary algorithms stay entirely within your premises, untouched by external clouds. Owning Digital Twin Pro means having a dedicated assistant that is always available, retains what you approve, and stays true to your mission without the strings of subscription services or shared cloud risks.

What your agent handles

Thirty ways to put your agent to work — each one private, local, and yours to approve.

Use case 01
Local agent orchestrator

Orchestrate preprocessing, inference, and aggregation agents to cut dataset processing latency by 30%.

Try this prompt
“your assistant, run my preprocessing, inference, and aggregation agents in parallel to speed up this dataset.”
Needs: Works out of the box
Use case 02
Compute node discovery

Returns each node’s IP, status, GPU/CPU specs, and current workload for developers to schedule jobs.

Try this prompt
“your assistant, list all compute nodes on my LAN that are idle and have at least 16 GB GPU memory for model training.”
Needs: Works out of the box
Use case 03
Model endpoint registry

Stores and retrieves endpoint metadata—name, version, framework, hardware—for instant local model lookup.

Try this prompt
“your assistant, display the metadata I saved for the Falcon‑40B endpoint, including its version and required GPU count.”
Needs: Works out of the box
Use case 04
NIM/vLLM/Ollama routing

Inference requests route across your vLLM, Ollama, and NIM endpoints based on measured latency once your nodes are registered.

Try this prompt
“Register my three inference endpoints, then route each prompt to whichever has the lowest latency, Ollama as fallback.”
Needs: Endpoints registered
Use case 05
Repo summarization

Provides developers a bullet‑point overview of a repo’s architecture, modules, and exposed interfaces.

Try this prompt
“your assistant, summarize this repo’s main components, their responsibilities, and the public APIs they expose in bullet points.”
Needs: Repo on device
Use case 06
Issue/PR summarization

Open pull requests summarize into a triage view — titles, reviewers, and status — from your on-device repo mirror.

Try this prompt
“From my local repo mirror, which PRs are open, who's reviewing each, and which have sat unreviewed the longest?”
Needs: Repo mirrored on device
Use case 07
Codebase Q&A

Delivers citation-backed, confidence-scored answers about code logic via the local your assistant LLM.

Try this prompt
“your assistant, show me where the JWT verification function is defined and list its dependencies with line numbers.”
Needs: Codebase folder connected
Use case 08
Local documentation search

Instantly locate any technical term across your local docs with regex, case‑insensitive, and line‑number precision.

Try this prompt
“your assistant, search my docs for 'API rate limiting' using case‑insensitive regex and return file paths with line numbers.”
Needs: Documents folder connected
Use case 09
PDF/research paper indexing

For developers, your assistant instantly indexes PDF and EPUB papers, delivering sub‑second keyword retrieval on‑device.

Try this prompt
“your assistant, index my PDF and EPUB research papers for instant keyword search with sub‑second latency.”
Needs: Documents folder connected
Use case 10
Container health monitoring

Tracks CPU, memory, and restart metrics for every container, triggering instant alerts when thresholds are breached.

Try this prompt
“your assistant, give me real‑time CPU, memory, and restart counts for each Docker container and alert if any exceed thresholds.”
Needs: Works out of the box
Use case 11
GPU/node utilization dashboard

Real‑time GPU/node utilization dashboard with 1‑second refresh and interactive heatmap graphs for developers.

Try this prompt
“your assistant, show me a live GPU utilization dashboard updating every second with a heatmap across all my nodes.”
Needs: Works out of the box
Use case 12
Jetson fleet management

Helps AI engineers monitor, analyze, and balance Jetson fleet workloads locally for efficient model deployment.

Try this prompt
“your assistant, show me each Jetson's current inference latency, power draw, and temperature, then suggest the best node for my next batch.”
Needs: Works out of the box
Use case 13
Route jobs to your best LAN node

Your assistant dispatches batch jobs to whichever registered LAN node reports the most headroom right now.

Try this prompt
“Send this batch inference job to whichever of my registered nodes has the most GPU headroom at the moment.”
Needs: LAN nodes registered
Use case 14
Route vision/robotics pipelines

Vision and robotics pipelines route to a vision-capable tier while this node keeps orchestrating.

Try this prompt
“Move my stereo-vision SLAM pipeline to the vision tier and confirm it holds thirty frames a second.”
Needs: Vision tier registered
Use case 15
Local RAG experiments

Executes end‑to‑end RAG pipelines, measuring retriever recall and generator BLEU/ROUGE on locally indexed docs.

Try this prompt
“your assistant, run a RAG test on my documents, output retriever hit‑rate@10 and generation ROUGE‑L scores.”
Needs: Documents folder connected
Use case 16
Agent tool gateway

Manages API wrapper tools via JSON/YAML config for local agent workflows on Digital Twin Pro.

Try this prompt
“your assistant, load my API wrapper definitions from config.yaml and expose them as agent tools for my pipeline.”
Needs: Works out of the box
Use case 17
Secure support demo environment

Creates isolated VM‑based demo environments with strict network and RBAC controls for secure client support demos.

Try this prompt
“your assistant, spin up a sandboxed VM with read‑only network and role‑based access for my client support demo.”
Needs: Works out of the box
Use case 18
Hardware temperature monitoring

Monitors CPU and GPU temperatures every 2 seconds, alerting when either exceeds 85 °C to protect training runs.

Try this prompt
“your assistant, start logging my CPU and GPU temperatures every two seconds and warn me if they go above eighty‑five degrees Celsius.”
Needs: Works out of the box
Use case 19
Build/test automation

Automates local build and test pipelines using Make, CMake, Bazel with pytest and JUnit for rapid developer feedback.

Try this prompt
“your assistant, trigger a full rebuild and test suite for my microservice repo, caching dependencies and reporting failures instantly.”
Needs: Project folder connected
Use case 20
DevOps assistant

Generates ready‑to‑use Docker Compose configurations with health checks and resource limits for multi‑service apps.

Try this prompt
“your assistant, generate a Docker Compose file with health checks and resource limits for my three‑service microservice app.”
Needs: Project folder connected
Use case 21
NAS dataset indexing

Indexes NAS datasets via NFS/SMB with sub‑second latency for rapid developer access.

Try this prompt
“your assistant, index the /data/ml folder on my NAS using SMB, updating only new files for fast retrieval.”
Needs: NAS mounted
Use case 22
Local benchmark runner

Runs MLPerf and HuggingFace benchmarks locally, delivering instant charts and tables for developers to evaluate model performance.

Try this prompt
“your assistant, run MLPerf and HuggingFace benchmarks locally and output results as charts and tables.”
Needs: Works out of the box
Use case 23
Model download manager

Downloads and version‑controls model files from local folders or private registries, letting AI engineers swap versions instantly.

Try this prompt
“your assistant, download the latest version of the Llama‑2‑7B model from my models folder and keep the previous version as backup.”
Needs: Models folder connected
Use case 24
Prompt/version manager

Tracks prompt revisions in a local git repo, letting developers diff and restore any version instantly.

Try this prompt
“your assistant, show me the git diff between version 3 and version 5 of my custom prompt for the summarization agent.”
Needs: Works out of the box
Use case 25
Local eval runner

Runs accuracy, F1, and latency metrics on local models and compares results against a stored baseline.

Try this prompt
“your assistant, evaluate my fine‑tuned model and report accuracy, F1, latency compared to the baseline checkpoint.”
Needs: Works out of the box
Use case 26
Homelab inventory

Catalogs homelab hardware and software, recording model, serial numbers, versions, and exports results to CSV or JSON.

Try this prompt
“your assistant, generate an inventory report of all hardware and software in my homelab, including model numbers, serials, and versions, and export it as CSV and JSON.”
Needs: Works out of the box
Use case 27
Research assistant

Your assistant extracts key sentences from research papers and returns a three‑bullet summary per document.

Try this prompt
“your assistant, give me a three‑bullet summary of the methodology and results for each paper in my docs folder.”
Needs: Documents folder connected
Use case 28
Customer demo appliance

Client demos boot from preloaded scenarios in minutes — a self-contained pitch box that needs no venue Wi-Fi.

Try this prompt
“I'm demoing at the client's office at nine — load our retrieval demo scenario and have the walkthrough ready offline.”
Needs: Demo scenarios loaded
Use case 29
Client-site deployment platform

Enables developers to securely package and deliver AI services to on‑site client infrastructure with zero‑touch rollout.

Try this prompt
“your assistant, bundle my inference service into an encrypted OTA package and push it to the client's gateway using mutual TLS.”
Needs: Works out of the box
Use case 30
Private AI fabric controller

Configures networking, storage, and security layers of the private AI fabric and validates each component.

Try this prompt
“your assistant, provision the private AI fabric by setting up VLANs, encrypted storage, and role‑based access, then run health checks.”
Needs: Works out of the box

Set up in an afternoon

  1. 1Place your Digital Twin Pro on the dev LAN with Ethernet.
  2. 2Pair phone and desktop dashboard.
  3. 3Give your agent a name — Atlas, Friday, anything you like. It answers to whatever you choose.
  4. 4Install the Digital Twin Pro node agent on a larger Digital Twin Pro tier, a vision-capable tier, Mac Studio, or Jetsons.
  5. 5Register local model endpoints.
  6. 6Connect repos, docs, and NAS datasets.
  7. 7Enable compute routing and health dashboards.

Works with

NIMvLLMOllamaDockerGitHubNAS datasetsJetson fleetLarger tiersVision tierMonitoring

Where it lives

Desk, homelab rack, network shelf, lab bench, or next to dev workstation.

The right hardware

Digital Twin Pro serves as the always-on controller. A larger Digital Twin Pro tier strongly recommended. A vision-capable tier recommended for physical AI, robotics, cameras, and sensors.

Three ways to say hello

Copy a prompt, send it to your agent, and watch it get to work.

Introduce yourself 
I am setting up my agent for Developers, AI Engineers & Builders.
My primary goal is: [your main goal].
My location/device placement is: [where the appliance will sit].
My approved integrations are: [your approved integrations].
My privacy requirements are: [local-only or cloud-allowed-with-approval].
Create a setup plan, recommended automations, and the first 10 prompts I should try.
The first look around 
Agent, scan only my approved local network for the Developers, AI Engineers & Builders setup.
Do not control anything yet.
Show me discovered devices grouped by type, risk, and recommended action.
Ask for approval before enabling any integration or automation.
Your daily briefing 
Agent, give me my daily Developers, AI Engineers & Builders briefing.
Include: important changes, open tasks, devices or systems needing attention, recommended next actions, and anything requiring approval.
Keep it practical and prioritize what matters today.

Begin with Digital Twin Pro

One appliance. One considered purchase. A private AI that works for you — with humans to help you get set up.

Shop Digital Twin Pro — from $1,699
30-day home trial✓ Secure checkout✓ Real humans on support

Common questions

Straight answers about privacy, cost, and what to expect. One-time support services are available whenever you need them — see services.

Where does inference actually run, and can I verify nothing leaves my network?

Inference runs on the appliance by default; nothing reaches the cloud unless you explicitly route a task to a provider you enabled. Your assistant runs locally, and any logs or model files stay on the device’s encrypted storage. Your data stays on the appliance; you can copy backups to your own drive whenever you like, and a one-time service can set that up with you.

There are no required ongoing fees — optional one-time services (setup help, Software Refresh, Life Upload) are available whenever you want them.

The appliance costs $1,699 upfront. Every appliance includes setup assistance, documentation, a factory-reset image, and covered fixes for the supported software version. Beyond that, one-time services with defined deliverables — from a Remote Setup Session to a full Life Upload — are available whenever you want them. Nothing recurs.

How much setup is needed before I can start using it?

Unbox the unit, connect power and Ethernet, then use the companion phone app to pair and complete the initial configuration. No additional hardware or software installation is required beyond that.

What are some things the Digital Twin Pro cannot do?

The Digital Twin Pro can't make phone calls or send texts, unless you set it up to do so. It also can't handle really big tasks that need more computer power than it has. For those tasks, you'll need extra hardware or cloud resources, like large-scale distributed training clusters.

Can I load my own machine‑learning models onto the assistant?

Yes. You can copy model files to the appliance’s storage and run them with your assistant using standard frameworks like TensorFlow or PyTorch. The system provides a local API for inference, so you integrate your models just as you would on any Linux workstation.

The appliance keeps working exactly as purchased; anything extra is a one-time choice.

Updates are released as signed packages that you can download from the vendor’s portal and apply via the admin web interface or command line. The process is similar to updating any on‑premise server: download, verify, and restart the service.

One easy decision

$1,699 — once. About $1.55 a day over three years. 30-day home trial, 1-year warranty, real-person setup included.

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