Feasibility & Risk

What's buildable, what's hard, and how we test before we commit.

Feasibility Matrix

CapabilityFeasibilityConfidenceDependenciesNotes
AI-assisted PRD/spec/QA generationHighHighInternal docs, templates, guardrailsFastest AI-fluent win.
Policy-grounded RAG for compliance contextMediumMediumStructured corpus, versioning, retrieval evalsQuality depends on policy source hygiene.
Case triage + next-step recommendationHighMediumWorkflow taxonomy, labeled historical casesHuman approval remains mandatory for critical actions.
AI-guided issuance workflow copilotMediumMedium-LowWorkflow engine, policy service, UI integrationHigh value, needs careful rollout.
Deterministic policy check serviceMediumMediumRules engine + legal mappingNon-negotiable control layer.
Event-level AI action logging/provenanceHighMediumCentralized observability, immutable logsCritical for audit trust.
Autonomous critical-path action executionLowLowRegulatory clearance, mature controlsDefer until strong evidence and approval model.

Challenging Parts Map

1

Policy-to-system translation gap

Legal/compliance language is nuanced; system rules need determinism.

Policy ontology, legal-approved rule interpretations, versioned mappings.

2

Retrieval trustworthiness in regulated contexts

LLM output quality collapses if policy/document retrieval is stale or noisy.

Curated corpus, freshness SLAs, citation requirements, fallback handling.

3

Human approval orchestration

Preserving accountability without bottlenecking workflow speed.

Risk-tiered approval model and dynamic routing by action criticality.

4

Explainability and evidencing

"Why this action" must be auditable for internal and external review.

Standardized rationale schema + immutable event traces.

5

Multi-system integration complexity

Issuance/custody/trading and account systems are often loosely coupled.

Orchestration layer with reliable event contracts + retries/idempotency.

6

Organizational adoption

Teams may use AI inconsistently, reducing reliability.

Shared playbooks, training, usage telemetry, role-specific governance.

Spike Plan

Spike 12 weeks

Compliance RAG reliability

Goal: Test policy retrieval quality and citation integrity.

Success: >90% answer grounding with correct policy references on benchmark set.

Spike 22 weeks

AI-assisted requirement-to-test pipeline

Goal: Reduce spec and QA authoring time.

Success: 30% cycle-time reduction with acceptable review pass rate.

Spike 33 weeks

Ops case triage copilot

Goal: Classify and route high-volume exception cases.

Success: 25% lower triage handling time and stable error profile.

Spike 44 weeks

Issuance workflow copilot (internal alpha)

Goal: Guide internal teams through missing docs, policy checks, next actions.

Success: Measurable reduction in stalled steps and handoff latency.

Spike Governance

Every spike must clear these gates before proceeding — no exceptions

Risk Review

Independent risk assessment for each spike before execution begins

Compliance Sign-off Gates

Mandatory compliance team approval at each stage transition

Post-Spike Audit Packet

Complete evidence package generated and filed for audit readiness