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ReTeam Energy
ReTeam Energy is an AI-enhanced energy efficiency management platform designed to help users assess and optimize energy consumption in buildings. Leveraged machine learning and LLM prompt chaining to automate building energy auditing, analyze utility bills, and dynamically generate retrofit recommendations (estimating payback periods, ROI, and carbon offset details).
The network for creativity
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Connect with clients, get discovered, and run your business 100% commission-free
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Energy teams need to make decisions quickly when demand, production, storage, and market conditions change.
Gridora explores a mobile SaaS experience for energy operators and renewable asset owners, helping them forecast demand, balance production, and decide when to store, sell, or reduce energy output.
The interface focuses on quick operational checks, clear recommendations, and minimal data visualization so users can understand energy conditions without digging through dense dashboards.
See the design:
https://dribbble.com/shots/27763825-Mobile-App-Energy-Balancing
The question I ask before automating anything: "What does this look like on a Tuesday in month four?"
Not the demo. Not launch day. Month four, when the person who championed it has moved on, the data has drifted, and the model has quietly started doing something slightly different.
The automations that survive month four have three things: a human somewhere in the loop, a log a non-engineer can read, and a kill switch that doesn't need me. The PO intake agent I posted last week is built that way on purpose. It registers and notifies, people decide, and every email in and out is logged.
If yours has all three, you're fine. If it has none, I'd love to hear how it's going. I collect these stories.
the month four test is so spot on zahid. everyone designs for the launch demo, but silent prompt drift kills more workflows than actual broken APIS.
At Procys, I design the exception path alongside document extraction.
Invoices and identity documents arrive in different layouts. OCR and LLM reasoning produce candidate fields. Deterministic checks validate headers, line items, and tax values. When confidence or record comparisons leave a field uncertain, the workflow sends it to a reviewer with the source context.
A good document AI system should make uncertainty visible before data moves into business systems.
If your team processes invoices or identity documents, which exceptions still take the most manual time?
edge cases are where doc AI earns trust, love that you design for them 👌
