Rapid-Deployment Databases

Day-One Infrastructure for
Disaster Relief & Crises

When disaster strikes, you need to track resources that didn't exist 24 hours ago. Spin up dynamic pop-up schemas instantly, collaborate securely across agencies, and use AI to forecast critical supply burn rates.

Clone Crisis Management Blueprint Read the Docs

The Chaos of "Day One" Data

Local governments and NGOs scramble during floods or wildfires because traditional databases require weeks of engineering to build. Without an immediate system, responders resort to WhatsApp groups and shared Excel files, leading to lost data, zero accountability, and misallocated rescue teams.

✗ You cannot predict the exact schema shape of a crisis before it happens.
✗ Spreadsheets fail when multiple agencies try to edit them concurrently.
✗ No automated forecasting for critical supplies like MREs (Meals Ready-to-Eat) or fresh water.
✗ Zero accountability trail for life-and-death resource routing decisions.

Instant "Pop-Up" Schemas

Omnismith's "Zero-Migration" engine allows you to define new asset types instantly. In minutes, you can deploy relational templates for Evacuation Centers, Rescue Squads, and Medical Tents via the Blueprint Marketplace or API.

✓ Runtime Schema Generation: Create new templates immediately.
✓ Mix static entity data (Shelter Location) with live telemetry (Current Occupancy).
✓ Relational mapping to track which Rescue Squad is assigned to which Shelter.
✓ Expose a secure REST API instantly for field-worker mobile apps.

Template: Evacuation Center

Shelter Name String

Status List — Open / Full / Closed

On-Site Doctor Boolean

Current Occupancy Metric (Time-Series)

MRE Stockpile Metric (Time-Series)

Template: Rescue Squad

Squad Callsign String

Vehicle Type List — Boat / Truck / Heli

Assigned Shelter Reference → Evacuation Center

AI-Powered Metric Forecasting

Because Omnismith natively integrates continuous time-series metrics directly with your entity graph, "prognosable" metrics like MRE burn rates or rising water levels can be instantly analyzed by our built-in AI Assistant to forecast critical shortages.

Omnismith Copilot

Evacuation Center Analytics

YOU

When will the "High School Gym Shelter" run out of MREs based on the current occupancy growth?

Tool Call: search_entities

{ "template_id": "evac_center_xyz", "query": "High School Gym" }

Tool Call: get_entity_chart

{ "entity_id": "31b2-4f9a", "metrics": ["occupancy", "mre_stock"] }

Based on the telemetry from the last 12 hours, the High School Gym is burning through MREs at an accelerating rate due to a 15% hourly increase in occupancy.

If this trend continues, the shelter will completely exhaust its MRE stockpile in exactly 9.5 hours (Approx 02:30 AM local time). I recommend immediately dispatching a supply truck.

Inter-Agency Collaboration (RBAC)

During a disaster, data must be shared across local police, federal agencies, and NGOs without creating massive security liabilities. Omnismith's Scoped Workspaces allow you to invite external partners into specific datasets.

✓ Invite Red Cross volunteers to update shelter capacities.
✓ Grant local police view-only access to Rescue Squad locations.
✓ Ensure data integrity with strict Role-Based Access Controls.

Unified Truth, Divided Access

Maintain a single source of truth for the crisis, while strictly controlling which agencies can view or modify specific entity templates.

Immutable Accountability

When chaos subsides, post-mortem reviews are inevitable. Omnismith logs every single interaction automatically, ensuring you always know who made a critical resource-routing decision.

✓ Log exactly who updated a shelter's status to 'Closed'.
✓ Track when a rescue squad was reassigned to a new sector.
✓ Export the entire immutable ledger for federal reviews.

Incident Audit Log

volunteer-desk@ngo.org 30 mins ago

Changed Status: Open → Full

dispatcher@police.gov 2 hours ago

Changed Assigned Shelter: Downtown Arena → High School Gym

API-First Telemetry Ingestion

Stream high-frequency metric data from automated water-level sensors or manual volunteer apps directly into the high-speed `/metrics` endpoint to power the AI forecasting.

# Python: Automated river sensor pushing metrics

import requests

url = "https://api.omnismith.io/v1/entities/SENSOR_UUID/metrics"
headers = {
    "Authorization": "Bearer omni_sensor_token_xxx",
    "Content-Type": "application/json"
}

data = {
    "attribute_values": [
        { "attribute_id": "water_level_cm", "value": 412.5 }
    ]
}

requests.post(url, headers=headers, json=data)
# 202 Accepted