Proving the Mission

Our mission is to build DeepLoom. Each project below validates a critical technical component we need—satellite data fusion (MJOLNuR), model serving infrastructure (Cortex), and cross-domain forecasting (SideCast).
Initial wedge: defense/government pilots (AFTAC relationships + cleared/on‑prem readiness), then expansion into adjacent commercial verticals.

Data Fusion & Atmospheric Physics

MJOLNuR: Satellite Data Fusion

Phase I SBIR validating our multi-source satellite data fusion and real-time atmospheric modeling. The digital twin technology for nuclear plumes directly informs how we'll model any atmospheric phenomenon in DeepLoom.

Status: DTRA SBIR Phase I

Next: Advance to follow-on work and expand the digital twin capability

Partner: Defense Threat Reduction Agency

Model Serving & Deployment

Cortex: Model Infrastructure

Production-ready model serving platform proving our ability to deploy AI systems at scale with monitoring, APIs, and authentication. This infrastructure will power DeepLoom in production.

Status: Open-source release

Next: Grow adoption and harden enterprise deployment patterns

Partner: Open Source Community

Cross-Domain Prediction

SideCast: Multi-Domain Forecasting

Adaptive ML system for dependent-variable contexts (weather, agriculture, economics) directly addressing the multi-domain prediction challenges central to DeepLoom.

Status: Research partnership

Next: Publish results and integrate learnings into DeepLoom training

Partner: National Science Foundation (Partner)

From Mission to Vision

Our vision: Enable humanity to see how one system moves another, anticipate cascading crises, and act with true foresight. Our mission: Build the WeaveCast Platform & DeepLoom Model to achieve it.

Example strategic questions

“Over the next 14 days, as heat risk evolves across Texas, what is the probability (P10/P50/P90) that ERCOT operating reserves fall below 5%—and in those tail scenarios, what’s the expected distribution of day‑ahead price spikes and gas basis risk?”

“Over the next 90 days, where do we see the highest‑risk corridors where drought stress is likely to translate into crop shortfalls and staple price inflation—and where do those economic shocks raise instability risk? What are the lead times and confidence bands for intervention windows?”

“Over the next 6–8 weeks, what is the risk that Mississippi River low-water conditions constrain barge throughput by >30%—and if that happens, how does it propagate into fertilizer availability, grain export timelines, and regional price dislocations?”

“Over the next 30 days, what is the probability and duration of extreme wet-bulb heat across key Persian Gulf export hubs—and how does that translate into operational throughput risk, shipping delays through the Strait of Hormuz, and downstream oil product price volatility?”

“Over the next 21 days, if the North Atlantic storm corridor intensifies, what is the risk of port disruption at Rotterdam/Antwerp and LNG terminal congestion—and what are the knock-on impacts on EU gas storage drawdown rates and power price tail risk?”

“Looking 3–6 months out, what is the probability of a persistent Southwest drought reducing hydro generation and tightening cooling-water availability—and how might that cascade into industrial power curtailment risk and semiconductor fab output constraints?”

“Over the next 120 days, how likely is monsoon variance to push rice yield below trend in key Indian states—and what is the projected distribution of food inflation pressure and associated stability indicators at state and national levels?”

“Over the next 60 days, what is the combined risk that weather and security constraints disrupt Black Sea logistics—and how would that shift grain export timelines, fertilizer availability, and regional commodity price dislocations?”

“Over the next 45 days, where does Western Pacific typhoon risk intersect with key semiconductor manufacturing and air/sea logistics nodes—and what are the expected loss and disruption tails for insurers and supply-chain planners?”

“Over the next 10 days, what is the probability of a Texas freeze scenario that stresses gas supply and drives grid outage risk—and what’s the expected distribution of industrial downtime and downstream market impacts?”

“Over the next 14 days, as heat risk evolves across Texas, what is the probability (P10/P50/P90) that ERCOT operating reserves fall below 5%—and in those tail scenarios, what’s the expected distribution of day‑ahead price spikes and gas basis risk?”

Frequently Asked Questions