Invest in Climate Resilience Infrastructure
Aithera Labs is building the UK's next-generation flood prediction platform — turning open government data into real-time intelligence for the communities that need it most.
The Opportunity
Flooding is the UK's most costly natural hazard. As climate change increases flood frequency, the tools councils and emergency services rely on have not kept pace. Aithera Labs fills this gap with affordable, high-frequency, multi-factor flood intelligence built entirely on open data.
£6.1B
Annual cost of flooding in England
Environment Agency, 2022
5.2M
Properties at flood risk in England
Environment Agency, 2022
6 hrs
Typical update cycle of existing EA tools vs our 15-minute cycles
Internal comparison
£250M+
Estimated annual local authority spend on flood risk management
Defra, 2021
Technology Edge
Unlike legacy alert systems that rely on single threshold checks, Aithera Labs fuses multiple real-time data streams and measures its own prediction accuracy against observed outcomes.
Multi-Source Data Fusion
Combines Environment Agency river gauge readings (15-min intervals) with Open-Meteo soil moisture at two depths — surface (0-7cm) and subsurface (7-28cm) — to model runoff behaviour more accurately than river level alone.
Zero Proprietary Data Cost
Built entirely on Open Government Licence data and free open APIs. No data licensing fees. The competitive advantage is in the fusion model and delivery layer, not in owning expensive data streams that competitors can replicate.
Verified Accuracy
Every prediction is automatically reconciled against actual EA readings 24 hours later. Our performance metrics are measured — not claimed. This audit trail supports both investor due diligence and HMRC R&D tax credit evidence requirements.
R&D Programme
Aithera Labs is conducting systematic R&D to improve flood prediction accuracy — genuine scientific investigation under technological uncertainty, qualifying for HMRC R&D tax relief under the SME scheme.
The following research questions are being investigated empirically using reconciled prediction data. The outcomes are unknown in advance — which is the statutory test for qualifying R&D activity.
Soil moisture lag-time optimisation
Does soil moisture measured at T-6h or T-12h improve 24-hour river level predictions vs current-time readings? The optimal lag window is unknown and requires empirical testing across different soil types and catchments.
Station-specific threshold calibration
Current flood stage thresholds (HIGH/MEDIUM/LOW) are manually set. The research question is whether statistical analysis of historical EA readings can identify thresholds that measurably reduce false positive alert rates.
Continuous vs stepped soil moisture functions
The current model uses three discrete soil moisture classes. Does a continuous function of soil moisture percentage produce lower mean absolute error than the current stepped approach? The answer requires A/B testing against reconciled actuals.
Multi-station spatial correlation
Does a reading at an upstream station (e.g. Severn at Buildwas) predict risk at downstream stations with a measurable time lag? This spatial propagation model is genuinely uncertain and requires data-driven investigation.
For R&D tax credit purposes: This programme constitutes systematic investigation to resolve scientific and technological uncertainty, under HMRC's definition in CIRD81900. The company maintains a logged evidence trail of hypotheses tested, methods used, and measured outcomes. Investors should be aware that qualifying R&D expenditure may reduce the company's effective tax burden materially.
Team
Kishia Wolfe
Founder & CEO
MSc Global Public Policy, SOAS University of London. Former COO at Targeted Technology Solutions. Deep expertise in technology operations, public policy, and bringing data-driven products to government and enterprise clients.
Founded Aithera Labs (trading as Ascendant Ventures Ltd) to make sophisticated flood intelligence accessible to every local authority — not just those with six-figure data contracts.
Investment Details
Stage & Structure
- Pre-seed / seed stage — first institutional round
- UK company (Ascendant Ventures Ltd, Companies House registered)
- Eligible for SEIS / EIS investor tax relief
- Target customers: UK local authorities, county councils, emergency services
What We Offer Investors
- Regular updates on model performance metrics
- Access to the R&D evidence trail and whitepapers
- Quarterly founder calls
- Introduction to Innovate UK and UKRI grant opportunities
Use of Funds
Expanding river coverage, improving model accuracy, mobile alerting
Additional data sources, database scaling, uptime SLAs
Sales to local authorities, council partnerships, government contracts
First full-time hire (ML engineer or sales)
Request the Pitch Deck
Complete the form below and we'll send it directly to your inbox.
Request Pitch Deck
We'll be in touch shortly
Prefer to talk first? Get in touch directly →