ECHOCORE AI · FUSION COMPLIANCE ENGINE

EchoCore AI checks the evidence before it releases a result.

The engine associates sensor observations, checks trust and policy, and releases a result only through the fixed pipeline.

  • Fixed release path
  • Fails closed
  • Deterministic
  • Reviewable
System

EchoCore associates sensor observations and applies trust and policy checks before release.

Lab evidence

Automated tests cover trust, compliance and determinism. Valhalla scores the engine against synthetic ground truth.

Next direction

Extend the same evidence-first release path into live sensor integration, operator workflows and field evaluation.

Every candidate passes two gates.

The first gate limits confidence using the source and quality of the evidence. The second applies policy before and after fusion.

PROVENANCE TRUST

Provenance trust gate

EchoCore caps confidence at the assurance level of the supporting evidence. A spoofed broadcast, stale detection or synthetic stream cannot support a high-confidence track.

POLICY CONTROL

Compliance engine

Machine-readable policy runs before and after fusion. It can permit, restrict, downgrade, segregate or block. Missing labels cause a block. Audit records are hash-chained, and bundle files are covered by a SHA-256 integrity manifest.

Evidence quality limits the claim.

EchoCore orders claims as presence < bearing < position < track < identity. Each evidence combination sets the highest claim type and confidence the engine may release.

The scorer runs before engine changes are accepted.

Valhalla sends unchanged synthetic datasets through EchoCore AI. The scorer compares the output with simulation ground truth. We used it to find defects in stream handling, multi-target separation and cross-target association. We run it as the acceptance gate for engine changes.

echocore · closed loop
EchoCore AI interface: the scorer grading fusion output against simulation ground truth, beside the sealed verdict card with provenance, confidence ceiling, and the hash-chained audit trail (synthetic).
The capture shows engine behaviour and the sealed evidence record.
0.940.11

mean cross-target contamination

Share of observations associated across targets. Baseline run, then fusion-fixed run.

0.420.96

mean track purity

Share of observations from the correct target. Baseline run, then fusion-fixed run.

These figures describe pipeline behaviour across an 8-seed Valhalla sweep of the raid_demo scenario.

Evidence sample / raid_demo / 8-seed sweepechocore.fusion_score_sweep.v1
0.42→0.96
mean track purity
0.94→0.11
cross-target contamination
11
clutter-dominated tracks
0.69
tracked-epoch rate
8
evaluation seeds
per-target tracked-epoch: dji 0.45 / fiber 0.26 / shahed 0.24 / birds 0.006-0.06 / 8-seed sweep
Values from an 8-seed Valhalla sweep of the raid_demo scenario.
EchoCore AI Inc.
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