SignalOps · Architecture
Better signals. Better decisions. Better outcomes.
A streamlined underlying architecture built for extension: deterministic algorithms plug in without touching the platform, ML refines what the signals say, and GenAI explains why, grounded in evidence.
Better Signals
Deterministic core, ML refinement
Versioned rules produce reproducible signals from governed evidence. ML layers refine them: statistical anomaly detection, change and drift detection, model scoring, and confidence aggregation across rules, enrichers, algorithms, and human evaluation.
Better Decisions
Explainability built into the pipeline
Every signal carries the facts and lineage behind it. Semantic enrichment (entity extraction, NLP, embeddings) turns raw observations into connected context, and narrative summaries may be generated with LLMs, grounded strictly in evidence references.
Better Outcomes
Signals graded by what happens next
The Signal Assurance Framework registers every eligible confirmed signal as a testable assertion and measures time-to-materialization, favorable and adverse excursion, and benchmark-relative results. Signal quality is empirically demonstrated, not assumed.
The runtime split
Two runtimes, one contract
Infrastructure wants predictability; algorithms want velocity. SignalOps gives each its own runtime and joins them with an explicit contract, so neither slows the other down.
The platform runtime
Source registration, APIs, delivery guarantees, replay, backpressure, persistence, pipeline orchestration, audit, and deployment lifecycle. The machinery that must never surprise you, tuned for predictability.
The analytics runtime
Detector logic, statistical and ML models, scoring, forecasting, clustering, anomaly detection, classification, and explainability outputs. The environment analytics actually wants, tuned for velocity.
A contract between them
An explicit event contract joins the two: each side communicates only through versioned, well-defined events. Neither executes the other’s internals. Each side scales, fails, and upgrades independently.
Plug-and-play algorithms
Extend by configuration, not by fork
The same extension model runs deterministic detectors, ML models, and GenAI summaries. Nothing about the platform changes when an algorithm changes.
Algorithms are isolated plug-ins
SignalOps never depends on a single algorithm implementation. Multiple detectors may run against the same event stream, and detectors are enabled, disabled, or replaced by configuration, not code changes.
Use-case packs extend without touching core
A pack brings its own schemas, normalizers, enrichers, rules, features, detector policies, graph mapping, and benchmarks. Packs cannot bypass core idempotency, replay, temporal storage, tenant isolation, or Engine contracts: extension with guardrails.
Source domains onboard by registration
A tenant registers a source with source_domain, source_adapter, and signal_family. Events route to the correct pipeline automatically. New domains are configuration and packs, not platform forks.
The assurance loop
Data → signals → evidence → opportunity → decision → outcome → assurance → learning
Every stage is persisted and replayable. Confirmed signals become testable assertions; assertions are graded against outcomes; grades feed the next version of the signal. The system earns trust the same way analysts do: by keeping score of itself.
Point-in-time correctness
Evaluation never uses future information. The grade a signal earns is the grade it would have earned live.
Path quality, not just endpoints
Maximum favorable and adverse excursion track what happened between confirmation and materialization.
Version-aware calibration
Results are attributed to the exact algorithm version that produced the signal, so improvements are measurable.
Governed knowledge assurance
See the architecture run.
We will walk the platform layers on live traffic: ingestion, a deterministic detector, ML refinement, and the assurance ledger it lands in.