Research inputs that show their work
Research teams cannot use an answer they cannot trace. LyraMind returns grounded reads with sources, evidence, and reasoning attached, plus a knowledge graph, per-name track record, and multi-model verification, so every input carries its own provenance.
The problem: ungrounded reads cannot enter a research process
A research desk lives on provenance: where did this come from, how confident is it, what contradicts it. A fluent but ungrounded model answer fails that test immediately and cannot be cited in a note or defended in a review.
What is missing is not more text: it is structured reads that carry their sources, their evidence, and an honest confidence, plus the surrounding context of peers, sectors, and history.
What LyraMind gives you: provenance-first reads and context
Every response carries the Trust Layer: confidence, sources, evidence, reasoning, data-mode, and freshness. The ranked institutional digest orders a coverage list by confluence for triage. The Financial Intelligence Graph adds the neighborhood (peers, sector, ETFs, suppliers, macro exposure) so a read is about the name's context, not just the ticker.
Institution Memory reads each name's track record straight off the Trust Ledger (how often it has been read, how verdicts were distributed, which way confidence is trending), and multi-model verification measures agreement across independent reasoners and surfaces explicit contradictions so a disputed read is flagged, not hidden.
Honest scope: offline the graph is a clearly labeled curated seed of well-known relationships, with live sources pluggable behind the same contract, and the demo runs on sample data. This is educational analysis of public information, never advice or a prediction.
How you use it
Triage a coverage universe with the ranked digest, pull graph context to frame a name against its neighborhood, check Institution Memory for the name's track record, and run verification to catch contradictions before an input goes into a note.
Because every read cites its sources and records to the Trust Ledger, your notes carry an audit trail back to what each input was built from.
- Trust Layer on every read: confidence, sources, evidence, reasoning, data-mode, freshness
- Ranked institutional digest across a coverage list (highest confluence first)
- Financial Intelligence Graph: peers, sector, ETFs, suppliers, macro context
- Institution Memory: per-name track record read off the Trust Ledger
- Multi-model verification with agreement scoring and contradiction surfacing
- Auditable Trust Ledger behind every input
Questions
Is the knowledge graph real data or a demo seed?
Offline it is a clearly labeled curated seed of well-known relationships plus edges derived from it. Live sources such as filings and fund holdings plug in behind the same contract, and the data-mode is always stated.
How do contradictions get surfaced?
Multi-model verification runs a panel of independent reasoners, measures their agreement, and reports explicit contradictions, lowering confidence when the panel disagrees rather than hiding it inside one number.
Can I cite these inputs in a research note?
Every read carries sources, evidence, and reasoning and records to the Trust Ledger, so an input is traceable. It remains educational analysis of public information, not advice or a prediction, and your team owns the conclusions.