Department of Energy

The Genesis Mission

A Strategic, Operational, and Geopolitical Analysis of the American "AI Manhattan Project".

Top Secret // SCIEst. 2025Eyes Only
Capabilities

Strategic Offerings

GEN-001

GridConnect AI

The "Interconnection Accelerator"

Target:Renewable energy developers (Solar/Wind/Battery), Data Center builders (Hyperscalers), and Utility operators (RTOs/ISOs).
Problem

The "interconnection backlog" crisis. New power projects take years to connect to the grid due to manual, error-prone reviews of thousands of pages of technical documents against complex, changing utility rulebooks.

Solution

An agentic vertical SaaS that automates the ingestion, validation, and submission of interconnection applications. It uses Large Multimodal Models (LMMs) to cross-reference engineering diagrams with local utility tariffs and GIS data.

Core Tech Stack

PythonGPT-4o or Claude 3.5 SonnetGIS API integrations+1
GEN-002

GovMind Compliance

The "RegTech for Sovereign AI"

Target:AI Startups, Defense Contractors, and University Research Labs aiming for federal contracts.
Problem

The crushing regulatory burden of the Genesis Mission, including the $10^{26}$ FLOP reporting threshold, NIST AI Risk Management Frameworks, and export controls.

Solution

A "Compliance-as-Code" platform that lives in the developer's CI/CD pipeline. It automatically audits compute usage, scans code for non-compliant libraries, and generates mandatory federal reports.

Core Tech Stack

GitHub Actions / GitLab CI runners.RustKubernetes/Slurm integration+1
GEN-003

Genesis Bridge

The "Federated Science Platform"

Target:Pharmaceutical companies, Material Science firms, and the DOE/National Labs.
Problem

The "Trust Gap." Private companies have the models, and the government has the data (or vice versa), but neither trusts the other with raw assets due to IP theft or security concerns.

Solution

A middleware platform for Federated Learning. It provides standardized "Sovereign Containers" that allow models to train on classified data without ever "seeing" the raw data, returning only gradients/weights.

Core Tech Stack

Docker with TEE supportIntel SGX or NVIDIA H100 Confidential Compute mode.PySyft or Flower+1
GEN-004

ThermoSched

The "Carbon-Aware Workload Manager"

Target:AI Engineers, Data Center Managers, and Supercomputing Facility Administrators.
Problem

The physical cooling and energy limits of the grid. Running massive training runs during peak heat or peak energy pricing threatens grid stability and incurs massive costs.

Solution

A "Smart Scheduler" API that acts as a gatekeeper for training runs. It queries real-time grid telemetry (carbon intensity, LMP pricing, temperature) to pause or resume workloads automatically.

Core Tech Stack

APIs from PJMTime-series forecastingPlugins for Slurm
GEN-005

Patriot Code Academy

The "Cleared Talent Pipeline"

Target:Software Engineers seeking transition to National Security AI, Veterans, and STEM students.
Problem

The severe shortage of engineers who possess both high-level AI skills and the necessary security clearances ("L" and "Q" levels).

Solution

An EdTech platform combining technical training (PyTorch for Supercomputing) with automated security clearance coaching. It includes an AI agent to help fill out the complex SF-86 security forms.

Core Tech Stack

LMSBrowser-based coding environmentsFine-tuned LLM with RAG
GEN-006

Veritas Provenance

The "Truth in AI Browser Extension"

Target:Enterprise IT Administrators, Government Agencies, and Privacy-Conscious Consumers.
Problem

The difficulty in distinguishing between "American-made, Sovereign compliant" AI content and foreign propaganda or adversarial deepfakes.

Solution

A browser extension and API that scans content for the digital watermarks mandated by the Genesis Mission, providing a "Traffic Light" verification system (Green = Sovereign Compliant, Red = Adversarial/Unknown).

Core Tech Stack

Chrome/Edge/Firefox Extension Manifest V3.C2PAFast lookup database for authorized watermarking signatures.
GEN-007

SciTensor Foundry

The "Dirty Data to AI-Ready" Converter

Target:National Labs, Universities, and R&D divisions of legacy industrial companies.
Problem

The DOE has petabytes of valuable data locked in "dirty" formats: scanned PDFs of lab notebooks, legacy FORTRAN codebases, and unstructured sensor logs that modern AI cannot ingest.

Solution

An automated pipeline that ingests raw scientific artifacts and converts them into structured Knowledge Graphs and Tensor-ready formats optimized for the ASSP.

Core Tech Stack

Tesseract / Google Vision APITree-sitterNeo4j+1