AI/LLM Orchestration

Overview#

No single language model excels at every task. A model that produces outstanding executive summaries may be mediocre at complex multi-step reasoning. One that handles document extraction efficiently may be prohibitively expensive for high-volume batch work. The AI/LLM Orchestration module provides a single interface to multiple leading AI language models, routing each request to the right model, grounding responses in verified case evidence, and wrapping every interaction in safety guardrails and a complete audit trail.

Organisations access a full range of AI capabilities including report generation, evidence summarisation, threat assessment, and multi-agent collaboration without writing provider-specific integration code and without sacrificing governance over AI-assisted decisions.

Key Features#

Multi-Model Management: Access multiple leading AI language models through a single, provider-agnostic interface with automatic routing based on task complexity and cost.
Retrieval-Augmented Generation (RAG): Grounds AI responses in verified case-specific evidence from documents, transcripts, and organisational knowledge bases to minimise hallucinations.
Prompt Template Library: A library of professionally engineered prompts optimised for evidence analysis, report generation, threat assessment, and intelligence workflows, integrated with the platform's AI language model API connection.
AI Safety Guardrails: Enforces PII redaction, hallucination detection, toxicity filtering, and compliance policies before outputs reach end users.
Cost Optimisation: Reduces AI operational costs through semantic caching, prompt compression, and automatic model selection that balances quality against budget.
Multi-Agent Orchestration: Coordinates specialised AI agents for complex reasoning tasks requiring domain expertise, cross-validation, and synthesis.
Provider Failover: Automatic switching between AI providers when primary services experience degraded performance, maintaining continuous availability.
Vector Search: Semantic search across document embeddings enables conceptual queries that find relevant content even without exact keyword matches.
Real-Time Streaming: Token-by-token response delivery provides perceived speed improvements for interactive applications.
Continuous Improvement: Captures analyst feedback and corrections to continuously refine AI quality for organisation-specific tasks and terminology.
Complete Audit Trail: Token-level usage tracking, prompt versioning, model decisions, and output provenance for regulatory compliance and legal discovery.

Use Cases#

Automated Report Generation: Transforms large volumes of evidence into structured professional reports including executive summaries, threat assessments, and due diligence reports in minutes rather than days. Defence organisations and financial crime units use this to maintain output quality as caseloads grow.
Evidence Summarisation: Condenses thousands of pages of documents, transcripts, and communications into concise summaries that surface critical facts, entities, and relationships.
Threat Assessment: Analyses intelligence sources and adversary behaviour patterns to generate risk-scored threat profiles with recommended countermeasures for intelligence agencies and critical infrastructure operators.
Investigation Acceleration: Combines AI-powered evidence analysis, lead prioritisation, and knowledge retrieval to cut time-to-resolution on complex cases for law enforcement and financial crime investigators.
Multilingual Operations: Searches and analyses content across languages with cross-lingual retrieval and translation capabilities.

Integration#

This module connects with case management systems, investigation workflows, and document repositories through flexible APIs. It supports cloud, on-premises, and hybrid deployment models to meet varying data sovereignty and classification requirements.

Open Standards#

OpenAI Chat Completions API (LLM function-calling / tool-use specification): The module routes all model interactions through the published Chat Completions API format, including the tools array and tool_choice parameter for function-calling. Using the published open specification ensures provider-agnostic routing and avoids proprietary SDK lock-in.
OpenAI Embeddings API / text-embedding specification: Vector embeddings for Retrieval-Augmented Generation are produced via the published embeddings endpoint, returning fixed-dimension float arrays compatible with any standards-compliant vector store.
JSON Schema (IETF draft-bhutton-json-schema-01): Prompt template parameters and tool definitions are declared as JSON Schema objects, validated before execution. Schemas are published at https://json-schema.org/.
pgvector (Apache 2.0): Semantic similarity search over document embeddings is performed using the pgvector PostgreSQL extension, an open-source vector store that keeps embedding retrieval inside the existing PostgreSQL data layer with full tenant isolation.
W3C Server-Sent Events (SSE): Token-by-token streaming responses are delivered over persistent HTTP connections using the text/event-stream content type, enabling real-time output without a WebSocket dependency.
OAuth2 with JWT (RFC 6749 / RFC 7519): Every AI invocation is authorised with an OAuth2 bearer token carrying signed JWT claims. Organisation ID and user identity are verified before any model call is dispatched.
ISO 8601: All audit trail timestamps for prompt invocations, model routing decisions, and token-usage records are serialised in ISO 8601 format for interoperability with downstream SIEM and compliance reporting systems.
OpenTelemetry (CNCF): Latency, token counts, and model-routing spans are emitted as OpenTelemetry traces and metrics, enabling cost attribution and performance visibility through any OpenTelemetry-compatible observability backend.

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