Sibyl

LIVE DEMO

Sibyl reads what you've already filed.  Predictive document synthesis with deterministic numbers and page-level citations — no invented facts.

What this demo is
Three real questions, answered against three live public document corpora — Ishpeming, Marquette, and NMU. Every numeric claim traces back to a fact we extracted; every narrative claim traces back to a source page.

How Sibyl works

Sibyl pairs vector retrieval over source text with a temporal knowledge graph of structured facts. Numbers come from the graph (deterministic, queryable). Narrative comes from the source chunks (grounded explanation). The composition layer reads both and answers the question.

Layer 1
Ingest & structure
PDFs in. Per-page text, then Claude Haiku extracts structured facts: (subject, predicate, value, valid_from, valid_to, source_page, source_quote). Bitemporal — facts are never deleted, only invalidated when superseded.
Layer 2
Index & recall
Chunk text → embeddings (HNSW index). Facts → SQL on a temporal graph. At query time, top-k semantic chunks are retrieved alongside facts from the same source documents.
Layer 3
Synthesize
Claude Sonnet composes the answer using both. Every numeric claim cites a fact [F#]; every narrative claim cites a chunk [C#]. Charts are deterministic — drawn directly from the fact rows.

Source corpus

All public-source documents. Sibyl is built only on materials available at the issuer's website — no private or staff-shared records.