ARES-E is a unified evaluation harness and toolkit designed to standardize telemetry, events, and operational metrics across the "Critical Quad" of national security: Data/Energy, Water, and Public Health. This suite provides the deterministic engines, plug-in architectures, and privacy-aware schemas necessary to evaluate AI agents and human-machine teams under real-world operational stress.
The analytics dashboard provides a single-pane-of-glass operational view across all three modules with:
- Real-time streaming telemetry (synthetic data engine at 0.5 Hz)
- 18 configurable threshold alert rules with deduplication
- 8 interactive Chart.js visualizations (dual-axis, radar, area, bar)
- LOE composite benchmark scorecard
- 12-week score forecasting with 95% confidence intervals
- DDIL simulation results summary
- 100% client-side — zero backend, zero tracking, zero PII/PHI
Focus: Data Center Planning, Grid Operations, and AI Workload Efficiency.
EWIS is a plug-and-play Python toolkit for operators and researchers managing the intersection of high-performance compute (HPC) and energy markets.
- Grid & Market Intelligence: Standardizes payloads for grid stress signals, carbon intensity, and real-time energy pricing.
- Data Center Optimization: Tools for capacity planning, PUE (Power Usage Effectiveness) assisted attribution, and cooling optimization.
- AI Efficiency Metrics: Benchmarking "Energy per Token" and model workload efficiency to assess the environmental and operational cost of AI deployment.
- Weather Integration: Open-source RSS and weather-driven intelligence to forecast impacts on load, pricing, and infrastructure reliability.
- Extensible Plugin Framework: Support for Python entry points and local plugin discovery for "hot-swappable" industry adapters.
- CLI Diagnostics: Command-line interface for rapid batch runs and system diagnostics.
- Visualization Helpers: Plotly-first (interactive) and Matplotlib (fallback) support for analytics.
Focus: Water Treatment, Distribution, and Hydraulic Operational Metrics.
WOIK standardizes telemetry and events across municipal and industrial water infrastructure, providing a "digital twin" logic for agentic evaluation.
- Infrastructure Telemetry: Standardized schemas for treatment plants, distribution networks, lift stations, and storage tanks.
- Operational Risk Assessment: Reference metrics for leak likelihood, water quality risk, and pump specific energy.
- Energy-Water Nexus: Integrated accounting for carbon and energy consumption associated with water pumping and treatment.
- Event Standardization: Normalizes disparate sensor data into a strict Pydantic payload schema.
- Deterministic Engine: A local execution environment that runs plug-ins without external dependencies.
- Interactive Local Dashboard: An air-gapped HTML/JS dashboard that visualizes report JSON for sensitive site operations.
- Pydantic Schema Enforcement: Ensures data integrity across heterogeneous sensor networks.
Focus: Privacy-Aware Health Operations and Early Warning Systems.
PHIAK is a plug-in oriented toolkit for aggregated public health operations analytics, designed specifically to avoid the ingestion of PII/PHI.
- Capacity Signaling: Tracks ED beds, ICU occupancy, ventilator availability, and staffing/supply levels.
- Incidence & Surveillance: Standardizes signals for cases, test positivity, and syndromic surveillance (wastewater, outbreak indicators).
- Privacy Guardrails (Non-Negotiable):
- Zero Individual Data: No identifiers, free-text notes, or address-level geolocation.
- Aggregation by Design: All metrics are counts, rates, or rolling summaries.
- Cell Suppression: Optional minimum cell count suppression to prevent re-identification in small populations.
- Static Dashboard: Air-gapped HTML + JS + CSS dashboard—perfect for secure, JWICS-level environments.
- Deterministic Engine: Provides reproducible early warning indices and report generation.
- Documentation-First: Requires specific documentation for every metric and data source to ensure transparency and safety.
All three kits share a "Plugin Contract," allowing different teams to integrate proprietary systems (SCADA, EHR, Data Center Management) without rewriting the core analytics engine.
- Notebook-ready: Designed for Data Scientists and Researchers to iterate in Jupyter/VS Code.
- Air-Gapped Ready: All dashboards and reports are generated as static files, requiring zero internet connectivity for visualization.
- Vendor Agnostic: Standardizes payload schemas so that any AI model or agent can be evaluated against these metrics via the ARES-E harness.
For inquiries regarding DIU CSO teaming or implementation, contact: ARES-E@dascient.com
A notebook-ready toolkit for:
- Deep and Generative AI analytics and open-source metrics design
- Data center interoperability performance metrics and mitigation protocols under energy and grid stress
- Open-source RSS and weather driven energy intelligence for data center planning
notebooks/DaScient_DeepGenAI_Analytics_OpenSource_Metrics_Package.ipynbnotebooks/DaScient_DataCenter_Interop_EnergyCrisis_Notebook.ipynbnotebooks/DaScient_Weather_News_Energy_DataCenter_Planning.ipynbewis-toolkit/notebooks/01_grid_stress_eda.ipynbewis-toolkit/notebooks/02_genai_metrics.ipynbpublic-health-infra-analytics-kit/notebooks/00_PHIAK_Tour.ipynbwater-ops-interop-kit/notebooks/00_WOIK_Tour.ipynb
python -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
pip install -r requirements.txt
jupyter labnotebooks/- the main packagessrc/dascient_suite/- reusable modules (RSS, weather, energy proxies, reporting I/O)docs/- playbooks and glossaryreports/- generated exports (optional)data/sample/- sample schemas
MIT - see LICENSE.
MIT License — see LICENSE for details.
DaScient, LLC — Systematically addressing deep tech issues in critical infrastructure
Planning-grade analytics and operational scaffolding. Not a substitute for facility engineering, safety review, or regulatory compliance.