ACCELERATOR · CODE ASSETS

Domain Reasoning Engine

Inject structured domain knowledge into LLM workflows to deliver accurate, explainable, and auditable AI reasoning.

A specialised engine that augments large language models with structured domain knowledge, business rules, and institutional context to produce reasoning that is accurate, explainable, and defensible under regulatory review. Rather than relying on general-purpose prompting, the engine grounds LLM outputs in verified domain models, turning probabilistic text generation into structured, evidence-backed decision support.

emergingAvailable

Key Features

Domain Knowledge Graph Integration

Connect LLM reasoning to enterprise knowledge graphs and ontologies, enabling the model to traverse relationships, validate facts, and cite authoritative sources in its outputs.

Rule-Augmented Inference

Overlay deterministic business rules on LLM outputs to enforce regulatory constraints, catch logical inconsistencies, and ensure conclusions align with organisational policy.

Explainability Layer

Generate structured reasoning chains that map every conclusion back to source evidence and applied rules, producing audit-ready explanations suitable for model risk review.

Retrieval-Augmented Generation Pipeline

Production-grade RAG implementation with hybrid search, re-ranking, chunk-level provenance tracking, and hallucination detection calibrated for financial domain documents.

Use Cases

Credit Decisioning Support

Banking

Augment credit underwriting workflows with AI-driven analysis that synthesises applicant data, policy rules, and market signals into structured recommendations with full audit trails.

Regulatory Change Impact Analysis

Financial Services

Automatically assess how new regulations affect existing policies, controls, and systems by reasoning across regulatory texts, internal documentation, and control frameworks.

Financial Crime Investigation Assist

Banking

Provide investigators with AI-generated case summaries, risk assessments, and evidence linkages grounded in transaction patterns, watchlists, and institutional typologies.

Technical Stack

PythonOpenAI / Anthropic APIsNeo4j / Amazon NeptuneElasticsearch / OpenSearchApache KafkaPostgreSQL

Deliverables

  • →
    Domain Reasoning Engine Core(Production code)
  • →
    Knowledge Graph Schema and Loaders(Production code)
  • →
    RAG Pipeline with Provenance Tracking(Production code)
  • →
    Explainability Report Templates(Documentation)

Expected Programme Outcomes

Time

14–20 weeks

saved on RAG and knowledge-graph build

Time

50–65%

faster domain-AI feature delivery

Risk & Compliance

Built in

explainability and provenance tracking

Cost

6–8 months

of RAG pipeline rework avoided

Prerequisites

  • →Identified domain corpus or knowledge base for ingestion
  • →LLM provider access with sufficient token quotas
  • →Subject-matter experts available for knowledge validation

Proven in

Case studies that used this accelerator.

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