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OrangePeel is built on a custom deterministic-algorithmic engine (D.A.E.), bracketed by two ML models — one that parses raw input, one that shapes output. The math in the middle is deterministic. Same inputs reconstruct the same number, every time. The audit trail terminates in source events.
Same inputs, same number. Every time. The engine itself has no learned weights to drift; two ML models bracket it for input parsing and output shaping, both pinned per release. The math in the middle never moves.
Deterministic core · ML at I/OEvery metric on screen reconstructs from first inputs. Hand the underlying event log to a third party; they get the same numbers, byte for byte. No private weights, no proprietary scoring.
Byte-for-byteOpen the math. Every signal that fed every metric is reachable from the report — drill into the engine, not a model card. The audit trail terminates in source events, not “trust us.”
Audit trail → source eventsModern analytics is mostly probabilistic — useful, but the cost is opacity.
Deterministic engine in the middle, bracketed by two ML models for input parsing and output shaping. Versioned, pinned, replayable.
Append-only event stream from across the Suite. No silent mutations.
Deterministic-algorithmic engine with two ML models bracketing it — one parses raw input into engine-ready features, one shapes the deterministic output into human-ready reports. Engine in the middle is pure.
Reproducible end-to-end. Every cell drills back to the source event that produced it.
Questions
OrangePeel is Axis Meridi's deterministic analytics engine. Every metric it produces is reproducible from first inputs and carries a full derivation path, so any number can be traced back to the source events that produced it and audited end to end.
D.A.E. is the Deterministic Algorithmic Engine behind OrangePeel. It recomputes every metric from source events using fixed, versioned rules — no sampling, no learned weights, no randomness. Two ML models bracket it for input parsing and output shaping; the math in the middle never moves.
No. D.A.E. is deterministic, not machine learning. ML recognizes patterns probabilistically and its baselines drift as models retrain; D.A.E. reasons through explicit rules pinned per release. The same inputs always produce the same number, byte for byte, fully reproducible.
Those stacks are probabilistic: categories are inferred by a classifier you cannot see, and the same inputs can produce different scores between versions. OrangePeel's engine has no learned weights to drift, and every figure reconstructs from the source event log.
Operators who have to defend a number in the room — finance, compliance, and audit teams who need figures that are reproducible and traceable, not ninety-five percent confident. If because the model said so does not cut it, the engine is for you.
OrangePeel is in active development on engine version D.A.E. v0.12. Access today is by introduction through an advisor conversation. The same deterministic engine already powers Veydros' internal analytics.
Numbers you defend
OrangePeel works best for operators who have to explain a number to a regulator, an auditor, a board, or themselves. If “because the model said so” doesn’t cut it, the engine is for you.