Instead of manually updating spreadsheets and running slow simulations, Squid's AI builds a live digital twin of the grid and lets planners instantly test "what if" scenarios—like adding a new solar farm or upgrading a substation—without breaking a sweat.

Squid's platform uses AI agents to ingest grid, market, and flexibility data, automatically constructing and updating a digital representation of the network. Planners can then run rapid scenario simulations—testing the impact of new connections, asset upgrades, or demand changes—with AI-generated recommendations and evidence trails attached to every decision. This replaces slow, manual workflows and enables utilities to keep pace with the accelerating energy transition.

It's like having a SimCity AI that not only builds your city but also tells you exactly which roads will flood if you add a new power plant—before you make the mistake.

The AI looks at years of data about how much electricity people use, how old the equipment is, and where new solar panels are popping up—then predicts what the grid will need next year, or in ten years, so utilities can plan ahead.

Squid applies time-series forecasting and supervised learning to historical grid, weather, and market data. This enables utilities to predict future demand, identify assets at risk of failure, and assess the impact of DER growth on network capacity. These insights feed directly into the planning platform, ensuring investment decisions are evidence-backed and defensible to regulators and boards.

It's like a weather forecast for the grid—except instead of rain, it predicts where the lights might go out and what needs fixing before it happens.

The AI automatically pulls in data from dozens of sources, organizes it, and stamps every planning decision with a receipt showing exactly what information was used—so when regulators ask "why did you do that?", the answer is already there.

Squid's platform integrates data from GIS systems, SCADA, market feeds, and flexibility providers, using NLP and entity resolution to link assets and evidence across sources. Every change to the grid model is version-controlled, with supporting evidence attached. This creates a transparent, auditable decision trail that meets regulatory requirements and supports stakeholder communication.

It's like Google Docs for the grid—every edit is tracked, every decision has a footnote, and you can always see who changed what and why.