Built for Intelligence.
Designed for Action.
Zygy brings together AI-powered signal analysis, regulator-compliant decision workflows, and full audit traceability — in one unified platform.

94%
Average AI decision confidence on critical actions
100%
Of decisions logged with full classification chain
<2 min
Signal to Action Engine alert latency
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AI classifies each signal against a fixed taxonomy: SEVERE_CHARGE, SUSPECT_IDENTIFIED, WITNESS_FLAG, EVIDENCE_LINK, INCIDENT_NARRATIVE, PROCEDURAL— aligned to jurisdictional standards
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The Action Engine automatically escalates signals that breach risk thresholds — no analyst needs to manually monitor dashboards.
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Every automated action carries a confidence score, rationale, and signal chain— reviewable by a compliance officer at any time.
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Human override is always available — analysts can change any AI-recommended decision type before it is logged.

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Multiple input sources (FIR documents, social signals, knowledge base, police reports) are normalised into a single signal schema— no manual data re-entry.
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Built-in confidence thresholds act as quality gates— signals below threshold are flagged for human review rather than auto-classified.
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Cross-portfolio checks automatically detect when the same entity appears across multiple issues — preventing duplicated investigation effort.
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Risk scores are recomputed on every new signal — the Outcome Monitor reflects the latest state in real time, with no manual refresh required.
11
Max cross-portfolio signal overlaps detected (House Crime pair)
5
Data source types connected to a single analysis pipeline
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A single pipeline run in Services flows into Control Tower signals, which generate Action Engine recommendations, which create Work Hub tasks — with no manual bridging.
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Cross-portfolio correlation ensures that a discovery in one issue (e.g. shared actor in Steal Crime) automatically surfaces in the related issue (House Crime) without the analyst having to know to look.
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Work Hub approvals can be triggered directly from an Action Engine recommendation — reviewers see the full AI rationale, signal chain, and confidence score in context.
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The Outcome Monitor closes the loop — showing whether actions taken actually moved the risk score and resolved the issue.

3
Platform modules coordinated in a single workflow (Services → CT → Work Hub)
78%
House Crime resolution progress after coordinated patrol action

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Decision Log records every classification with: decision type, risk score, sentiment, platform source, analyst name, and date — exportable for regulator submission.
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Action Timeline in each War Room shows every action logged, who logged it, and what changed in the risk score as a result.
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Baseline vs current comparison in the Outcome Monitor gives a defensible before/after view — key for post-incident reporting and internal review.
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AI override events are also logged — if an analyst changes a recommended decision, the original AI suggestion, the override, and the reason are all preserved.
9
Decisions in log — each with full classification chain
0
Untracked actions — every event is attributed to a user or system
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Multi-database architecture— Elasticsearch for search, Neo4j for graph relationships, MongoDB for documents, SQLite for lightweight data — all unified behind a single API.
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AI model agnostic— currently powered by Azure OpenAI (GPT-3.5-turbo-16k) with the architecture designed to plug in any future model without platform changes.
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SSO & identity federation— CAS SSO, Google OAuth, and Azure MSAL (OneDrive) supported out of the box. Analysts authenticate once and access everything.
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Document format interop— PDF, DOCX, XLSX, scanned images all processed via Tika, PDFBox, Mammoth, and Spire.xls — no pre-processing required from users.
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Streaming & real-time— SSE-based streaming search (port 7201) delivers live AI responses without page reloads, keeping analysts in flow.







