Pre Squid
Pre Squid is applying vertical data moats to industrial, representing a pre seed vertical AI play with unclear generative AI integration.
With foundation models commoditizing, Pre Squid's focus on domain-specific data creates potential for durable competitive advantage. First-mover advantage in data accumulation becomes increasingly valuable as the AI stack matures.
Squid offers a workspace for planning, modeling, and testing power grid networks with tools for network analysis and simulation.
Unified, AI-powered network model in a browser-based collaborative workspace, built by founders with deep experience inside leading grid and energy companies.
Vertical Data Moats
Squid leverages deep domain expertise and likely proprietary data from the electricity grid sector, using industry-specific knowledge as a competitive advantage for AI-powered grid planning.
Unlocks AI applications in regulated industries where generic models fail. Creates acquisition targets for incumbents.
Pre Squid operates in a competitive landscape that includes ETAP, DIgSILENT PowerFactory, Siemens PSS®E.
Differentiation: Pre Squid emphasizes AI-powered, browser-based collaboration and unified network models, whereas ETAP is traditionally desktop-based and less focused on real-time collaboration or AI.
Differentiation: Pre Squid differentiates with a modern, web-based workspace and a focus on unified, collaborative network models, while PowerFactory is more traditional and desktop-centric.
Differentiation: Pre Squid is cloud-native, AI-driven, and designed for collaborative workflows, whereas PSS®E is legacy software with less emphasis on browser-based, collaborative features.
Squid positions itself as an 'AI-powered grid planning' tool that operates entirely in the browser. This suggests a commitment to cloud-native, possibly serverless or edge-compute architectures, which is unusual in the traditionally desktop-bound, on-premises world of grid simulation and planning.
Their thesis centers on a 'unified, trusted network model' for electricity grids, aiming to replace fragmented workflows (spreadsheets, static reports) with a collaborative, real-time, AI-augmented environment. This is a non-trivial data engineering challenge, involving versioning, consensus, and possibly graph-based data structures for grid topology.
The team claims deep domain experience (ex-National Grid, Octopus, AWS), which hints at insider knowledge of legacy pain points and the technical hurdles of grid modeling at scale.
The product appears to target high-trust, enterprise-grade use cases (National Grid, Octopus Energy as references), which implies a focus on security, auditability, and explainability of AI-driven recommendations—an area where most AI SaaS tools fall short.
There is no public codebase or open-source footprint, and the website is gated (waitlist only), suggesting a closed, proprietary approach with possible custom infrastructure.
The product is repeatedly described as 'AI-powered' and 'modern', but there are no specifics about the underlying technology, models, or proprietary methods. The technical claims are vague and buzzword-heavy.
There is no clear data advantage or technical differentiation described. The offering could be replicated by others with access to similar data and APIs.
The core value proposition appears to be a unified network model for grid planning, which could be absorbed by larger incumbents as a feature rather than a standalone product.
Pre Squid's execution will test whether vertical data moats can deliver sustainable competitive advantage in industrial. A successful outcome would validate the vertical AI thesis and likely trigger increased investment in similar plays. Incumbents in industrial should monitor closely for early signs of customer adoption.
Source Evidence(3 quotes)
"Squid is AI powered grid planning in your browser."
"AI-powered grid planning in your browser"
"Browser-based AI grid planning platform focused on unified, trusted network models for grid operators and planners."