Transform unstructured legal briefs, statements, and forensic dossiers into self-correcting knowledge graphs. Quantify credibility and verify case theories automatically.
Burglary at Mehta residence. Stolen Gold watch and ₹2.5 lakh cash. Fingerprint matches Rohan Verma.
Priya claims she saw Rohan exit at 7:30 PM. However, gate CCTV records no movement until 8:47 PM. Investigation suggests Priya misjudged time due to the 7:20 PM sector power outage.
WS-003.json (Line 12)CogniVerdict replaces standard black-box AI outputs with structured, mathematical calculations grounded directly in document memory graphs.
Parses raw case documents (PDFs, JSON witness briefs), chunks texts semantically, and builds the initial entity-relationship graph in Cognee.
Queries the case dataset by traversing graph topologies and fetching vector chunks to ground the advisory chat and reasoning engines.
Enriches and optimizes graph representation in the background, restructuring entity relations based on submitted feedback.
Cleans and prunes discredited evidence nodes, invalid witness statements, or deletes entire case datasets securely.
Runs contradictions, motives, and credibility analyses concurrently via asyncio.gather, completing tasks in seconds.
Links every graph connection back to raw source lines (e.g. WS-005.json) so that all AI logic remains human-verifiable.
Evaluates suspect predictions, vector recall rates, and MAE scores against hidden judicial ground truths continuously.
Blends extracted qualitative signals with mathematical credibility and strength formulas for objective conviction likelihood.
Standardized engineering patterns that enable millisecond-level responsiveness and clean separation of concerns.
Ingest new legal dossiers, visualize the connection topology, and let the parallelized multi-agent analysis work for you.
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