Robots That Find Manuals (RTFM)
A collection of AI agents that actively research, update, and maintain a deterministic MCP server exposing solar-equipment fault, diagnosis, and remediation knowledge.
Solar O&M needs an AI native knowledge repo
Deployed solar is growing faster than the workforce that keeps it running. Every fleet commissioned today is a future stream of fault codes, blinking LEDs, and service calls, and the expertise to handle them is scarce, expensive, and walks out the door when a senior tech moves on.
43 GW
of new US solar capacity added in 2025, solar's fifth straight year as the #1 source of new generating capacity.
SEIA / Wood Mackenzie86%
of solar employers report difficulty hiring qualified workers, with insufficient skills and training the top cited obstacle.
IREC 2024 Solar Jobs CensusThe knowledge needed to diagnose any fault already exists. It's just buried in thousands of manufacturer PDFs: inconsistent formats, scattered vendor portals, a different document for every model revision.
Ramping up expertise can't mean reading manuals faster. It needs a single, structured, machine-readable repository of equipment knowledge: AI-native from the ground up, ready to plug into whatever agent platform an individual installer or a national O&M fleet already runs.
RTFM is an AI agent–maintained knowledge base
This service synchronizes daily with the California Energy Commission's equipment-certification registry: the canonical list of every string inverter, micro-inverter, and solar battery certified for sale in the largest solar market in the US.
From there, AI agents do the work no human team could sustain. A research agent scours the web for official documentation for every registered model. A triage agent verifies each candidate document and matches it to the exact equipment it covers. An extraction pipeline reads every page, text and figures, and distills it into structured fault knowledge.
CEC registry sync
Daily sync with ca.gov's certified-equipment lists
Research agent
Searches the web for official documentation per model
Triage agent
Verifies each document and matches it to exact equipment
Analysis & extraction
Reads every page, text and figures, into fault knowledge
Structured data
Served deterministically over MCP
206
manufacturers
3,031
models tracked
94%
of registered models documented
1,325
manuals ingested
35,254
pages indexed
16,124
alert codes
2,154
fault behaviours
24/7
monitoring & updates
Live numbers, straight from the database.
Document coverage of the US market
Providing your AI agents with up-to-date and comprehensive knowledge of US solar tech
Different problems need different altitudes. The data and MCP tool surface are designed so an agent can drop to the exact page of one specific manual, or rise above any single manufacturer and reason about solar faults in general.
The library tier
A fully processed, indexed library of every ingested document. Each page is converted to searchable markdown plus imagery, so multimodal agents don't just read the documentation, they see it: wiring diagrams, LED state tables, connector photos. Every retrieval cites its exact source page.
The structured tier
Pre-extracted tables of the data that matters in the field: 16,124 alert codes and 2,154 fault behaviours, organized by manufacturer and model, each carrying meaning, root cause, and recommended actions, with a page reference back to the manual that says so.
The abstraction tier
Across 14,703 analyzed alerts, every fault decomposes into four axes: the component involved, the state it's in, what the device did automatically, and what a human is told to do next. A conditional belief model built on the co-occurrence of those axes lets agents generalize across all manufacturers, surfacing the most likely diagnoses and best-documented paths to resolution even when the exact manual isn't on file.