One core — AI agent services — powering everything we do: live drought and flood forecasting, bespoke risk assessments that go beyond standard metrics, and tender intelligence for water-sector opportunities. Connect an agent in minutes, with a single prompt.
👋 Hi, I'm Mohana, your AI guide.
AI4Water helps companies modernize their water and climate risk assessment. We connect satellite data, hydrological models, and AI to show you — in plain language — how drought and flood risk affects your supply chain, your portfolio, and your bottom line. Connect an agent and ask me anything below.
Droughts and floods don't just damage ecosystems — they disrupt supply chains, move commodity prices, and change insurance liabilities overnight. Our models connect hydrological events to financial exposure across crops, regions, and sectors.
A drought is not a single event. It propagates through layers: meteorological deficit → soil-moisture depletion → agricultural stress → hydrological shortage → socioeconomic impact. Each layer has its own onset, duration, and recovery — and they don't move in lockstep. Our models track the cascade, not just the headline.
The same atmospheric patterns that cause drought in one region often drive flooding in another. Cross-region synchrony — what we call Concurrent Drought Analysis — reveals the teleconnections that matter for portfolio risk. A drought in Brazil and a flood in Vietnam may share the same ENSO trigger.
We don't just run black-box ML. Our Hydroinformatics Foundation Architecture (HFA) couples satellite foundation models (TerraMind, Clay, Hydro FM, Prithvi) with verified physics simulators (Delft3D, SWAN, XBeach) and Lagrangian moisture tracking — so every prediction has a physical explanation.
Every market signal passes through evidence gates: causal attribution → cross-region synchrony → cascade-layer verification → regional teleconnection → market confirmation. We track 23 companies across 11 sectors, with 112K trade records and 31K crop-price observations. No signal ships without evidence.
AI agents are the foundation of AI4Water. Connect one with a single prompt, and it can forecast, assess risk, and surface opportunities — pulling from the same satellite, hydrological, and market intelligence that powers our platforms.
Drought and flood risk signals for monitored cities and basins, refreshed continuously and explained in plain language.
FORECASTBespoke assessments that go beyond standard metrics — compound events, cross-region teleconnections, and cascading impacts almost no one else measures.
RISKGlobal water risk at a glance, with vapour-transport animation and per-city exposure — served from our own self-hosted tile stack.
MAPBenchmark 9 geospatial foundation models on water-body segmentation. Open, reproducible methodology for any region.
PLATFORMLagrangian moisture tracking — from atmospheric river to rainfall object — for source-to-sink water attribution.
PLATFORMEvidence-gated water-risk signals connecting hydrological events to financial exposure across crops and sectors.
MARKETSNo API keys to configure. An AI agent connects to AI4Water with a single prompt — the same way you pair a host with a QR code.
Connect to AI4Water agent services. As my AI4Water agent, you can:
1) query water & climate risk (drought, flood, crop exposure, market impact)
2) request live forecasts for monitored cities and basins
3) request a climate-risk assessment for a company, basin or portfolio
4) browse tender opportunities (World Bank, EU TED, grants)
Ask me: what region, asset, or question should we start with?
Point your phone camera at the code to load the agent prompt.
Most climate-risk reports stop at single-hazard scores. We measure what almost no one else includes — and that's where the real exposure hides.
Drought followed by flood, heat + water stress, or a storm that triggers a supply-chain failure. We model the chain, not just the single event.
A drought in Brazil and a flood in Vietnam can share one ENSO trigger. Portfolio risk lives in the synchrony other reports miss.
From hydrological event to balance-sheet impact — crops, utilities, insurers, and infrastructure, quantified against your holdings.
Foundation models coupled to verified simulators (Delft3D, SWAN, XBeach), so every figure traces back to a physical cause.
Select any city below. Our AI analyst cross-references satellite precipitation, soil moisture, ENSO indices, and crop-price databases to tell you: what's the risk, which crops are exposed, which companies feel it, and where else in the world the same pattern is appearing.
Data pipeline: GPM IMERG v7 (30-min precip, 0.1°) → ERA5-Land (soil moisture, temp, 0.1°) → ONI/MEI ENSO (NOAA CPC) → CHIRPS anomaly (0.05°) → FAOSTAT crop prices → Company financials. AI analysis runs on-demand with the latest available indices. LIVE · refreshed hourly FFG: SIMULATION · using synthetic QPE Research output — not financial advice.
Equal Earth keeps every region in true relative size — the projection the UN General Assembly adopted in Sep 2026 (res. A/80/L.104, “Correct the map”). Shown here: monthly precipitation vs the 1981–2010 CHIRPS normal — red = drier than normal (drought signal), blue = wetter.
Monthly water-vapour transport (NCEP reanalysis, Mar 2025 – Feb 2026). Red shows columns losing vapour — subsiding, drying air. The basin overlay is an AI4Water analysis of evaporation anomalies per river basin (HydroBASINS) — red basins drying, blue basins wetter — served from our own tile stack. Click any city marker to load its AI forecast card.
Every forecast card on this page is backed by a six-layer system that ingests, models, and refines its understanding of water risk continuously.
Every 30 minutes, the system pulls satellite precipitation (GPM IMERG 0.1°), hourly soil moisture and temperature (ERA5-Land 0.1°), ENSO indices (NOAA CPC), and rainfall anomaly fields (CHIRPS 0.05°). Crop prices update daily from FAOSTAT. Company financials update quarterly. No single source is trusted alone — cross-validation against historical climatology flags sensor drift before it reaches the model.
The Hydroinformatics Foundation Architecture (HFA) couples geospatial foundation models (Hydro FM, TerraMind, Prithvi) with verified physics simulators (SWAN, Delft3D, XBeach) and Lagrangian moisture tracking. For flash flood guidance, a basin-scale SAC-SMA-style soil moisture accounting model computes dynamic FFG thresholds at 1h/3h/6h durations per catchment. The ensemble runs multiple models in parallel; disagreement triggers deeper investigation.
Every signal passes through five evidence gates before surfacing: causal attribution → cross-region synchrony → cascade-layer verification → teleconnection confirmation → market impact correlation. A drought signal in Colombia must show consistent patterns across IMERG, soil moisture, reservoir levels, and ENSO before triggering a crop exposure alert. The system currently tracks 23 companies across 11 sectors with 112K trade records.
Raw numbers don't help decision-makers. The AI analyst generates a concise natural-language explanation for each city: what the risk is, why the models flagged it, which crops are exposed, how markets are reacting, and where else the same pattern is emerging. Every statement is traceable to its source data — no black-box claims. For flash flood basins, it explains which catchments are close to exceedance and why upstream conditions matter.
The system learns from every event. When a forecasted drought materialises, the model records which signals preceded it correctly and which were noise. When a flash flood threshold is exceeded, the actual streamflow (where gauges exist) is compared to the FFG prediction. Over time, the system refines its per-basin Thresh-R values, soil moisture scaling curves, and exceedance probability distributions — becoming more accurate for each specific catchment without losing global transferability.
All benchmark results are public. Code is on GitHub. Forecast summaries are available as structured JSON for integration into your own dashboards. The FFG pipeline outputs GeoJSON with per-basin FFG, QPE, exceedance ratios, and tier assignments — ready for any GIS or web mapping tool. Research methodology is documented and reproducible. We learn in the open.
The system does not require manual calibration per site — it starts from global defaults and improves through continuous validation against observations. This is the statistical-distributed approach: run the model on historical data, establish flood/drought frequency per basin, then compare real-time output to those baselines.
Systematic comparison of 9 geospatial foundation models (Hydro FM, TerraMind, Clay, Prithvi, Sapiens) on water-body segmentation. Interactive dashboard with per-model IoU, F1, precision-recall across 17 Colombian coastal sites. Open benchmark methodology, reproducible in any region.
benchmark (contact for access) ATMOSPHERIC INTELLIGENCECombined Eulerian-Lagrangian platform for atmospheric river detection and moisture source attribution. ERA5 back-trajectories, object-based rainfall feature clustering, and moisture convergence fields. Real-time monitoring for Colombia, with CONUS and Mekong expansions planned.
orbita (tailnet — contact for access) FINANCIAL INTELLIGENCEEvidence-gated commodity risk signals connecting hydrological events to financial exposure. 23 companies across 11 sectors (fertilizer, grain, equipment, protein, CAT bonds). 112K trade records, 31K crop-price observations. CDA → cross-region synchrony → cascade layers → market confirmation pipeline.
trading (contact for access) OPERATIONAL SHELLDelft-FEWS-class operational shell for countries and river basins. Sector-of-use analytics across 5 system layers × 7 dimensions. Agent protocol for governed forecast triggers, risk classification, confirm/reject warning pipeline. First deployment: El Salvador. Built in Rust.
watria (contact for access)ORBITA tracks atmospheric moisture from source to sink: ERA5 back-trajectories, atmospheric river detection, and moisture-source attribution for Colombia — with CONUS and Mekong expansions planned. Explore the live platform below.
Live interactive dashboard: trajectory analysis, moisture convergence fields, and rainfall feature clustering. Open it in a new tab for the full experience.
Embedded view of ORBITA (Firebase-hosted, repo: github.com/corzogac/ORBITA). If the frame appears blank, the platform may need to load scripts — use the Open ORBITA button instead.
We design every system to be consortium-ready: open protocols, documented APIs, reproducible pipelines, and GDPR-compliant data handling. If you're building a Horizon Europe, UKRI, or national research proposal that needs AI-for-water expertise, we're ready.
Production ML pipelines on Google Cloud Run. Geospatial foundation model benchmarking. Real-time ERA5 + IMERG data ingestion. CARAVAN hydrological dataset (16K+ gauges). PostgreSQL + pgvector knowledge infrastructure.
Coupling satellite foundation models with verified physics simulators. Flash Flood Guidance system from IMERG + ERA5-Land + HydroSHEDS. Lagrangian moisture tracking. Basin-scale exceedance thresholds at 1h/3h/6h durations.
Evidence-gated drought-to-market signals. 23 companies across 11 sectors. Flood↔drought symmetry for portfolio hedging. 112K trade records and 31K crop-price observations. CDA → cross-region synchrony → market confirmation pipeline.
We contribute to open science: all benchmarks are public, code is on GitHub, and our training materials are freely available. Looking for partners in drought finance, climate adaptation, and AI-for-science.
AI4Water turns global hydroclimate data — drought, soil moisture, ENSO, crop exposure — into daily, verified market intelligence. A macro-regime committee, a multi-agent analyst committee, and a financial foundation model turn the world's water into trading context: where supply chains break, which commodities are exposed, and what the risk posture should be. Every figure carries provenance; every model version is disclosed.
The full brief every trading day: macro-regime posture, per-ticker committee memo (Buy / Hold / Overweight) with reasoning, Kronos commodity forecasts (corn, wheat, soy, sugar, coffee, cocoa), drought-crop exposure, and combined advice — historical + predicted.
€90/month
The same verified dataset without advice: structured JSON (macro regime, committee decisions, Kronos forecasts, drought-crop exposure) with provenance — sources, timestamps, staleness flags. For analysts and AI agents that run their own reasoning.
€30/month
For AI agents: GET /v1/daily returns the report or the raw data with full provenance. Machine
payments via x402 (USDC) or Stripe MPP — no human in the loop.
$2 report · $0.50 data
Founding offer: the first 10 clients lock €30/month for life on any tier, in exchange for honest feedback that shapes the product. Payments go live shortly — join the founding list via the contact form or email [email protected].
NVDA — Buy. Bullish momentum, 0.6 PEG, 17.6 forward P/E, ~75% gross margins, Blackwell cycle; staged pullback references near $217 and $206.50 ahead of 26 Aug earnings; inventory growth and priced-for-perfection risk noted as caveats.
AAPL — Hold (bridge) / Overweight (audit). ~48.6% gross margin, $25B quarterly buyback support; bearish MACD and ~35x P/E argue for patience; support $293–297 rather than chasing near $305. Both outcomes disclosed — the two runs disagreed, neither was cherry-picked.
MSFT — Overweight. FY2026 revenue growth 17.8%, 40.3% net margins, Azure/AI capex; RSI near 71.8 creates pullback risk — support $450–465 preferred over momentum chasing.
Operational disclosure: requested Gemini 2.5 Pro, actually ran Gemini 3 Flash Preview; FRED macro data used fallback inputs (key unavailable); no numeric confidence scores invented. Safety boundary: research only, no trades executed.
Every decision is recorded with its realised outcome against the benchmark (SPY) — including the mistakes and the lessons learned. Paper trading, research only: the system never executes an order.
| Date | Ticker | Decision | Result | vs SPY | Horizon |
|---|---|---|---|---|---|
| 2026-07-15 | NVDA | Hold | -4.3% | -2.7% | 3d |
| 2026-07-28 | NVDA | Buy | +4.9% | +2.6% | 4d |
| 2026-08-03 | NVDA | Buy | +2.6% | +0.8% | 1d |
| 2026-08-03 | MSFT | Overweight | +1.1% | -0.7% | 1d |
| 2026-08-03 | AAPL | Hold | +2.0% | +0.2% | 1d |
| 2026-08-04 | AAPL | Overweight | +0.5% | +0.7% | 1d |
| 2026-08-04 | MSFT | Overweight | -1.1% | -0.9% | 1d |
| 2026-08-04 | NVDA | Buy | +3.4% | +3.6% | 1d |
| 2026-08-05 | MSFT | Overweight | +2.5% | +2.7% | 1d |
| 2026-08-05 | NVDA | Buy | -0.1% | +0.0% | 1d |
| 2026-08-05 | AAPL | Hold | +0.5% | +0.6% | 1d |
| 2026-08-06 | MSFT | Overweight | +0.0% | -0.6% | 1d |
| 2026-08-06 | NVDA | Overweight | +2.3% | +1.7% | 1d |
| 2026-08-06 | AAPL | Hold | +0.3% | -0.3% | 1d |
| 2026-08-07 | MSFT | Overweight | +1.2% | +1.2% | 1d |
| 2026-08-07 | AAPL | Overweight | -1.6% | -1.6% | 1d |
| 2026-08-07 | NVDA | Buy | -2.9% | -2.8% | 1d |
| 2026-08-10 | AAPL | Overweight | -1.1% | -0.8% | 1d |
| 2026-08-10 | NVDA | Buy | -0.0% | +0.3% | 1d |
| 2026-08-10 | MSFT | Overweight | -0.4% | -0.1% | 1d |
| 2026-08-11 | NVDA | Buy | +3.0% | +2.8% | 1d |
| 2026-08-11 | MSFT | Overweight | -2.3% | -2.5% | 1d |
| 2026-08-11 | AAPL | Hold | -0.9% | -1.1% | 1d |
| 2026-08-12 | NVDA | Buy | +0.5% | -0.2% | 1d |
| 2026-08-12 | AAPL | Overweight | +1.0% | +0.3% | 1d |
| 2026-08-12 | MSFT | Overweight | +0.9% | +0.2% | 1d |
| 2026-08-13 | NVDA | Overweight | -0.1% | +0.1% | 1d |
| 2026-08-13 | AAPL | Hold | +0.2% | +0.4% | 1d |
| 2026-08-13 | MSFT | Overweight | -0.3% | -0.1% | 1d |
| 2026-08-13 | MSFT | Overweight | -0.3% | -0.1% | 1d |
| 2026-08-13 | NVDA | Buy | -0.1% | +0.1% | 1d |
| 2026-08-13 | AAPL | Hold | +0.2% | +0.4% | 1d |
Ledger stats: 17 of 32 outcomes with positive alpha (53% win rate); average realised alpha +0.13% per outcome vs SPY; best +3.6%, worst −2.8%. The full ledger including the written reflections — lessons encoded after each decision — is available on request.
Whether you manage an agricultural supply chain, an insurance portfolio, or a research consortium — we build the intelligence layer you need.
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London, England, E17 3NU
Company No. 15070420
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