Technical Architecture

How the AI-native workflow stack is structured, how data flows, and what integrates with what.

Design Principle

AI sits inside a controlled workflow architecture, not as an unconstrained front-end assistant.

Core Architecture Layers

1

Workflow Orchestration Layer

State transitions, checkpoints, gates

2

Policy Intelligence Layer

Versioned corpus, retrieval, checks

3

AI Decision-Support Layer

LLM summarization, drafting, triage

4

Control & Audit Layer

Immutable logs, observability

5

Domain System Integration Layer

Account, custody, trading systems

Segregated Environments
Model Registry + Rollback
Redaction + Least Privilege

Data Flow: Issuance Readiness Example

End-to-end lifecycle of a single workflow execution

1
System

Case opened with issuer metadata + required documents

2
AI-Assisted

Workflow engine requests policy context for product type/jurisdiction

3
AI-Assisted

Retrieval service returns cited policy snippets + version ids

4
AI

AI assistant drafts readiness summary and missing-item checklist

5
AI-Assisted

Deterministic checks validate hard constraints (eligibility, required fields, control rules)

6
HumanApproval Gate

If constraints pass, route to human approver with evidence package

7
Human

Approver decision + rationale recorded

8
System

Next state transition executed and all events logged

System
AI-Assisted
AI
Human
Approval Gate

Logging Requirements per Step

actor:
human/system/model
model/version:
if AI involved
source documents:
policy versions consulted
recommendation/action:
confidence/risk tier
approval identity:
timestamp
downstream effects:
system effects

Observability Metrics

Real-time dashboards tracking AI workflow health and performance.

Latency

<2s

p95 step latency

Stalls

3

active stall points

Exceptions
4.1%

4.1%

exception rate

Overrides

18%

override rate

Policy
passwarnfail

2.3%

failure rate

Integration Topology

Event-driven architecture connecting all domain systems through the AI workflow engine.

AI Workflow Engine

Orchestration · Policy · Control

Account & onboarding

Tokenisation/issuance

Custody platform

Trading/dealing

Compliance/risk

Document & policy repo

Topology Model

  • • Event-driven backbone for workflow updates
  • • API gateway for bounded command execution
  • • Policy and AI services as internal platform capabilities
  • • Central audit event store as source of truth

Integration Standards

Idempotent APIs
Signed Event Schemas
DLQ Handling
Correlation IDs

Security Posture

  • • Service-to-service auth with scoped tokens
  • • Data classification tags enforced at query time
  • • Full access logging + anomaly monitoring