KillerWhale Intelligence

Every supported blockchain-native object normalized and reconstructed, with its native structure preserved rather than flattened.

Deterministic feature fingerprints describing structure and behaviour. Same input range, same output, every time.

Lineage and historical relationships derived from real input/output consumption evidence.

Statistical context across defined datasets so any observation can be compared against its cohort.

Objects or relationships requiring deeper research. A sighting is a research candidate, never a conclusion.

Reproducible source provenance beneath every serious observation, down to block height and transaction identity.

Structural reconstruction of historical chain state from genesis forward.

Search reconstructed objects and transactions across indexed ranges.

Full structural detail and signature breakdown for a single object.

Transaction-level context including surrounding objects and relationships.

Consumption graphs and ancestry traversal between reconstructed objects.

Distributions, robust outlier methods and population summaries.

Multi-method triage of objects that deviate from their population.

Persistent lists of identifiers to revisit as new ranges are indexed.

Notification when a watched identifier shows subsequent activity.

Side-by-side comparison of objects, cohorts and time ranges.

Exportable research write-ups carrying provenance and methodology.

Machine-readable evidence manifests attached to exported results.

Research starts with blockchain-native data read directly from chain nodes, not from third-party summaries.

Objects and relationships are reconstructed deterministically, so the same source range produces the same result.

Source identifiers stay attached to analytical results — block height, transaction, and object identity travel with the finding.

Critical findings can be independently re-checked against the source chain rather than accepted on trust.

Research stages may be cryptographically bound through manifest hashes recorded alongside each run.

Operational failures are detected and recovered without silently rewriting evidence; integrity failures stop the pipeline instead.