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Designed for Action.

Built for Intelligence.

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

End-to-End, Regulator-Compliant Automation

Every stage — from raw signal ingestion to formal decision classification — is automated and mapped to regulator-approved decision types. Nothing is ad-hoc. Nothing falls through the gaps. Use Case in Law Enforcement, Regulatory & Statutory Compliance

  • AI classifies each signal against a fixed taxonomy: SEVERE_CHARGE, SUSPECT_IDENTIFIED, WITNESS_FLAG, EVIDENCE_LINK, INCIDENT_NARRATIVE, PROCEDURAL— aligned to jurisdictional standards
     

  • The Action Engine automatically escalates signals that breach risk thresholds — no analyst needs to manually monitor dashboards.
     

  • Every automated action carries a confidence score, rationale, and signal chain— reviewable by a compliance officer at any time.
     

  • Human override is always available — analysts can change any AI-recommended decision type before it is logged.

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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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Seamless Data Flow & Checks

Data moves through Zygy without friction — from any source, through built-in quality gates, to actionable intelligence — with validation checks at every handoff point. Use Case in Public Relations/ Social Intelligence

Multiple input sources (FIR documents, social signals, knowledge base, police reports) to Multiple input sources (Social documents, social signals, knowledge base, Public Announcement reports) are normalized 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. To preventing duplicated decisions and response actions.

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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) to Max cross-portfolio signal overlaps detected (Common Actors in Topics of Interest)
5
Data source types connected to a single analysis pipeline

Multi-stage Workflow Coordination

Zygy orchestrates the full lifecycle — from signal collection through analysis, decision, and execution — with handoffs between Services, Control Tower, and Work Hub happening automatically. Use Case in Product Launch/ Market Research and Intelligence

  • 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 Product A Functional Requirements ) automatically surfaces in the related issue (Product B Functional Requirements) 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.

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3
Platform modules coordinated in a single workflow (Services → CT → Work Hub)
78%
Feature Compliance resolution progress after coordinated
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Full Traceability & Audit Trail

Every signal, decision, action, and outcome is permanently logged with a timestamp, user attribution, AI rationale, and full signal chain — creating an unbroken audit trail from first observation to final resolution.

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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

Integration & Interoperability

Zygy connects to the tools, databases, and cloud services your organisation already uses — ingesting data from any source and pushing decisions and reports to any downstream system.

  • 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.

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7
Microservices handling AI, search, reporting, and workflow
5+
Document formats natively ingested without user pre-processing
3
Identity providers supported (CAS, Google, Azure MSAL)
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