# Solar Proposal Agent

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Diego Quinde](https://cerebralvalley.ai/u/diegoaquinde)
- **GitHub:** https://github.com/diegoqprobst/solar-proposal-agent
- **Demo video:** https://www.loom.com/share/229914d432d04e1387c05e4fdee9b3a1
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2863

Solar Proposal Agent turns a site's documents (datasheets, surveys, notes) into a solar + storage design in which every number cites its source, and hands whatever it cannot prove to a person.

The problem: in 2024 I wrote a proposal to keep a rural water plant and an evacuation footbridge running through power cuts in Valle Hermoso, Ecuador. It was set aside before anyone reviewed it. Rereading it, I saw why it was weak: the system was never sized and the budget totals did not add up. Solar plus storage is the obvious answer for communities like this; what blocks it is the technical proposal. This tool is the one that proposal needed.

How it works: Muse Spark 1.3 runs an agent loop with tools. It reads the case documents (text and PDF datasheets) and records inputs. A value taken from a document must quote that document exactly and appear inside the quote; code checks this, and an invented quote is rejected. Deterministic code, not the model, does all calculations (pumping energy, lighting, PV array, battery bank, CO2 avoided from IPCC-derived factors) and fetches the site's worst-month irradiance from NASA POWER. The proposal text is written by code, so it cannot contain a figure that was not computed. Anything unproven becomes an open question for a person. Assumptions are flagged, and when site facts such as water demand or pump head are only placeholders, the report is stamped ILLUSTRATIVE ONLY.

What I tested live: a synthetic example case, sized end to end with a result that matches a hand calculation, and a real product datasheet with notes from my 2024 proposal, where the agent declined to invent a design and returned eight specific questions about what must be measured. Live runs exposed defects (percentages recorded as 45 instead of 0.45, a status-tool loop, a run reported as finished without sizing); I fixed them and added tests (44 offline tests).

Honest limits: the worked example is synthetic. The agent does not yet recognise luminaires that carry their own panel and battery, compare products, or price a budget. Photo reading is implemented but is not part of the demo. It produces a preliminary design for a qualified engineer to review and sign.

## More from Meta Global AI Developer Hackathon

- [Tejasai](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2860)
- [Unity AI Mapper](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2861)
- [Solo-Arise](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2862)
- [TakeState](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2864)
- [ARTHUR LABS](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2865)
- [Nishant Jaiswal](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2866)

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Markdown version of https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2863. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt