Advanced Analysis Framework

Overview#

A narcotics investigator has three hundred incident reports, financial records from four jurisdictions, and a working theory that two seemingly unrelated gangs share the same distribution logistics. Manually reading every document to test that hypothesis would take weeks. The Advanced Analysis Framework automates the journey from initial data ingestion through hypothesis testing, pattern recognition, and investigative path discovery, compressing that work into minutes of automated intelligence generation.

The system orchestrates complex analytical jobs, manages analysis lifecycles with real-time progress tracking, and delivers quantified intelligence products directly into investigator workflows. It is purpose-built for law enforcement command teams, intelligence organisations, financial crime units, and government programme managers who cannot afford confirmation bias or evidential gaps.

Key Features#

Analytical Job Orchestration#

Create, monitor, and manage analytical jobs with configurable parameters and real-time progress tracking via GraphQL subscriptions. Supported job types include summarisation, pattern detection, clustering, correlation analysis, anomaly detection, relationship extraction, and custom analytical workflows. Jobs support configurable time windows, confidence thresholds, analysis depth levels, and output format preferences.

Hypothesis Testing and Devil's Advocate#

Automated counterfactual scenario generation systematically challenges investigative theories to reduce confirmation bias. For each hypothesis the system generates three to five distinct alternative scenarios with likelihood assessments, impact analysis, and specific contradictory evidence recommendations. Counterfactuals convert directly into actionable investigative tasks.

Risk-Based Pathfinding#

Discover multi-hop relationship paths between entities with automated risk scoring and opportunity assessment. Returns multiple distinct paths ranked by risk score, with AI-generated investigative narratives explaining significance and recommending next steps. Supports up to five relationship hops across large-scale entity graphs stored in the platform's graph layer.

Pattern Detection and Correlation Analysis#

Behavioural pattern recognition across temporal, geographic, network, and transaction dimensions. Automated clustering uses HDBSCAN algorithms. Temporal and spatial correlation discovery, and entity correlation for identifying coordinated activities, all produce structured output that feeds directly into case timelines.

AI-Enhanced Intelligence Production#

Natural language analytical narratives, executive summaries, and intelligence briefings are generated using multiple AI providers with fallback redundancy. Complex analytical results convert automatically into human-readable intelligence products suitable for briefing command teams or presenting at court.

Use Cases#

• Financial Investigation Path Discovery: Trace relationships between suspects, shell companies, offshore accounts, and beneficial owners across multiple jurisdictions. Discover non-obvious connections and generate court-ready evidence documentation.
• Hypothesis Testing for Prosecution: Anticipate defence strategies by systematically generating and addressing counterarguments. Identify evidentiary gaps before trial preparation begins.
• Large-Scale Pattern Detection: Process thousands of incidents to identify distinct criminal operations, behavioural signatures, and organisational structures through automated clustering and pattern analysis.

Integration#

All analytical capabilities are available through a strongly-typed API with job management mutations, query operations, and subscription support for real-time progress updates via GraphQL. Custom analytics can be extended with organisation-specific workflows through flexible input/output schemas and Python analytics templates. Multi-tenant isolation is enforced at every data access point against the PostgreSQL primary data store.

Open Standards#

• GraphQL (June 2018 Specification): all job management, analytical queries, mutations, and real-time progress tracking are exposed through a strongly-typed GraphQL API implemented with Strawberry, including subscription support over WebSocket for live job status.
• JSON (ECMA-404 / RFC 8259): job parameters, analytical results, counterfactual outputs, and pathfinding payloads are all exchanged as JSON; the GraphQL schema exposes a dedicated JSON scalar for these structured result bodies.
• JSON Web Token / RS256 (RFC 7519): every GraphQL resolver enforces IsAuthenticated permission, which validates RS256-signed JWTs via a JWKS endpoint before any analytical operation is permitted.
• ISO 8601 / RFC 3339: all job lifecycle timestamps (created, started, completed) are serialised using Python's .isoformat() with UTC timezone, producing RFC 3339-conformant strings on the API boundary.
• RFC 4122 (UUID): analysis job identifiers, result identifiers, counterfactual scenario identifiers, and pathfinding route identifiers are all version-4 UUIDs, ensuring globally unique, collision-resistant references across multi-tenant workloads.
• STIX 2.1 / TAXII 2.1 (OASIS): the platform's threat-modelling job type feeds into and draws from STIX 2.1 indicator bundles stored via the STIX/TAXII domain, allowing analytical outputs to be expressed as structured threat intelligence objects.
• MITRE ATT&CK: attack-pattern profiles reference mitre_attack_id fields, enabling the threat-modelling and pattern-detection job types to annotate findings against the ATT&CK technique taxonomy.

Last Reviewed: 2026-02-05 Last Updated: 2026-04-14