Full-Stack Physical AI
Answers grounded in your technical reality — manuals, specifications, standards and logs — traceable to source, with citations and an evaluation loop that measures unsupported answers.
Ingest
Embed & index
Retrieve & re-rank
Ground & cite
A four-stage pipeline: ingest the corpus, then embed and index, then retrieve and re-rank, then ground the answer and cite sources.
Industrial knowledge lives in PDFs, specifications, standards and maintenance logs — not in a model's weights. Retrieval-augmented generation grounds a model in that corpus, so every answer is traceable to a source document. The result is a system engineers can trust, because they can check it.
Ingestion and chunking of the source corpus; embeddings and a vector store; retrieval with re-ranking; grounded generation that cites its sources; and an evaluation loop that measures faithfulness — whether answers are actually supported by the retrieved text.
Document parsing for PDFs, tables and scanned documents (OCR); embeddings over pgvector; hybrid retrieval (semantic plus keyword) with re-ranking; citation enforcement so claims link back to source; faithfulness and retrieval-quality evaluation; generation on Mistral or another open-weight model.
In industry, an answer is only useful if you can trace it to a source.
| Dimension | Industrial RAG | Generic LLM |
|---|---|---|
| Grounding | Your manuals, specs, standards | Parametric memory |
| Traceability | Answer linked to its source | Unsourced |
| Freshness | Index updated continuously | Frozen at training cut-off |
| Access control | Per-document permissions | None |
| Failure mode | "Not in the sources" | Confident hallucination |