AI · Cloud · Quant across the operational process

AI, Cloud and Quant, stage by stage into your operational process.

Not an off-the-shelf product. Each stage of your operational process has its own class of applications. In discovery we find the ones that matter for your bank or asset manager, and build them on a shared engineering core.

01

Stufe 1 / 5

Data & Onboarding

The pain today

Custodian, fund-administrator, and depositary data and documents are transferred by hand every cycle.

What AI does there

Extraction, mapping, and consolidation run automatically. The human reviews.

Example use cases

  • Structured extraction from custodian PDFs
  • Automatic mapping of inconsistent portfolio statements
  • Consolidation across multiple depositaries

02

Stufe 2 / 5

Knowledge & Research

The pain today

Knowledge sits in people's heads and in folders; finding a reliable answer takes hours.

What AI does there

A knowledge system over the firm's own documents answers questions with source citations.

Example use cases

  • RAG knowledge system over policies, regulation, and historical reports
  • Research synthesis from broker notes and market studies
  • Regulatory assistant (SFDR, MiFID)

03

Stufe 3 / 5

Analysis & Risk

The pain today

Analyses are produced manually; anomalies surface late.

What AI does there

AI-assisted analysis, anomaly and regime detection. The PM keeps the decision.

Example use cases

  • Anomaly detection across mandates
  • Regime signals visible early
  • AI-assisted preparation of attribution

04

Stufe 4 / 5

Reporting & Communication

The pain today

Reports, investor letters, and regulatory filings are produced by hand every cycle.

What AI does there

Governed drafting support with a traceable trail. The human approves.

Example use cases

  • Draft of the quarterly commentary per strategy
  • Factsheet narrative
  • Preparation of regulatory filings

05

Stufe 5 / 5

Compliance & Governance

The pain today

Auditor questions cost days; keeping an overview of regulation is laborious.

What AI does there

A compliance copilot and a governed in-house agent with approval gates and an audit trail.

Example use cases

  • Compliance copilot over your own regulation
  • Policy and limit-band monitoring per mandate
  • Governed agent that executes tasks safely

One engineering core. At every stage.

The method is constant, the use case varies, and both run on the same engineering core: AI (RAG, agents, local models), Cloud (Azure-native or on-premise) and quantitative methods (statistics, econometrics, deterministic analytics). What we build at stage 1 for one firm runs on the same primitives as stage 5 for another.

Every engagement runs the same way.

Discovery (where AI is worth it in your operations), then implementation on a fixed engineering core. The method is constant, the use case varies.

Which stage costs your firm the most time?

A 5-minute diagnostic clarifies exactly that, with no preparation on your side.

Start the diagnostic

© 2026 Nexqion · AI · Cloud · Quant: consulting and implementation for regulated financial institutions. Governed, on-premise-capable, regulatory-clean.