Pipe Inference: Generative Intelligence for Data Gaps
Unlock hidden network knowledge with our generative technology that fills critical asset data gaps using geospatial context, domain expertise and engineering-grade rules
1Spatial’s Inference solution is a generative technology that intelligently reconstructs missing or incomplete asset data by applying engineering-grade rules codified from expert knowledge, at scale. This flexible technology can address absent geometries, connectivity issues, material types, or other asset attributes, transforming uncertain, fragmented datasets automatically into high-confidence network models to enable safer operations, regulatory compliance, and smarter decision-making across utility networks.
Key Benefits
ü Generative intelligence – the solution applies codified logic based on domain expertise and spatial relationships, ensuring results are explainable, repeatable and auditable.
ü Flexible across domains – originally developed for water assets, the same framework can be adapted to infer missing customer connections, land parcel joins, drainage systems, or any context with spatial dependencies and partial data.
ü Accelerates data improvement programmes – fills gaps that would otherwise require costly site visits or manual interpretation – supporting faster progress towards an analytics-enabled future.
ü Reduces operational risk – enables better modelling, decision-making and network planning by improving the completeness of asset datasets without compromising integrity.
The Challenge
Many network and infrastructure organisations rely on legacy datasets that are incomplete, outdated or missing entirely – particularly for older assets, customer connections or low-voltage or low-pressure networks. These gaps increase operational risk, slow digitalisation programmes, and lead to costly manual investigation or assumptions made without auditability.
Our Solution
1Spatial’s Inference solution uses generative, rules-based intelligence to predict missing network information. The technology is flexible, applicable to not just underground assets, but any scenario where spatial logic and domain expertise can be applied to fill data gaps. Built on a foundation of topology, connectivity, context and configuration, the approach mimics the way human experts interpret partial data – but at speed, at scale, and with full traceability.
Why It Matters
Whether in gas, electricity or renewables, missing information is one of the most persistent barriers to operational efficiency. Pipe Inference shows how expert reasoning can be codified into a geospatial process – enabling scalable, defensible automation of insight where once only human intuition was available.
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Explore how generative spatial intelligence can help you close your data gaps – reliably, transparently, and at scale.
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