TECHNICAL WHITE PAPER — FEBRUARY 2026

The Intelligence Execution Layer
for the Autonomous Enterprise

A production case study: how ARPIA coordinated ML, GenAI, and Agentic AI to take a financial services collections pipeline from raw data to activated ERP strategy in 13 minutes.

INSIDE THE WHITEPAPER — PRODUCTION CASE STUDY

Raw Data to Activated ERP Strategy.
13 Minutes. In Production.

This is not a proof of concept. This is a financial services collections pipeline running in production — coordinating 4 types of AI, 5 concurrent agents, and activating a strategy in the enterprise ERP in under 1 second.

13 min
Raw data to
activated strategy
4 types
AI coordinated:
ML + GenAI + Agentic + Apps
5 agents
Running concurrently
in a single Reasoning Flow
<1 sec
ERP activation
from AI decision
PHASE 1 · ~6 min
ML Scoring

Python ML Worker processes the full receivables portfolio. Scores every account by collection likelihood. Output feeds directly into Phase 2.

PHASE 2 · ~4 min
Agentic Strategy Design

5 AI agents run concurrently — analyzing segments, benchmarking against ontology, designing collection strategies per portfolio cluster. Governed in real time.

PHASE 3 · ~3 min
Executive Reporting

GenAI Sequential Worker generates two executive-grade reports simultaneously — strategic overview and operational detail — with full auditability on every insight.

PHASE 4 · <1 sec
ERP Activation

Application Worker sends the approved collection strategy directly to the enterprise ERP. No manual handoff. No re-keying. Strategy is live in under a second.

The full 28-page technical breakdown — architecture diagrams, governance implementation, AI maturity assessment, and deployment methodology — is in the whitepaper.

Get the Full Whitepaper ↓ See How We Work →
28 Pages of technical depth
13 min Production proof pipeline

What's inside

  • The Intelligence Execution Layer architecture — Data Reflection, Knowledge Ontology, Reasoning Flows, AI AppStudio, Reasoning Atlas
  • Full breakdown of the 13-minute financial services collections pipeline — 4 phases, 4 AI types, 5 concurrent agents
  • Governance by Design: four levels of traceability (Data, Model, Decision, Audit) — ISO 42001 + SOC 2 Type 2 aligned
  • AI Maturity assessment: Level 6–7 across McKinsey / Gartner / OECD frameworks with evidence of deployed capabilities
  • Architecture diagrams, worker type breakdowns, and pipeline design methodology
  • Horizontal use cases: Collections, Credit Risk, Pricing, Supply Chain, Fraud, Strategic Planning, Customer Intelligence

Who is this for?

CIOs, CTOs, VPs of Engineering, Operations leaders, and AI/ML teams who are past the pilot stage and need a production-grade AI execution layer. Relevant for any organization dealing with complex multi-step decision processes.

Request Your Copy

Complete the form below and we'll send the white paper to your work email instantly. Our team may follow up to discuss your use case.

By submitting this form, you agree to receive communications from ARPIA Technologies. We respect your privacy and will never share your information. See our Privacy Policy.

FAQ

Common questions about the white paper

What is the ARPIA Intelligence Execution Layer White Paper?

ARPIA's February 2026 technical white paper documents the Intelligence Execution Layer — ARPIA's platform architecture that coordinates ML, GenAI, and Agentic AI in governed pipelines. It includes a production case study of a financial services collections pipeline that goes from raw data to activated ERP strategy in 13 minutes, plus architecture deep dives, governance framework details, AI maturity assessment, and deployment methodology.

Who is this white paper designed for?

This white paper is designed for CIOs, CTOs, VPs of Engineering, AI/ML leaders, and digital transformation executives evaluating enterprise agentic AI platforms across financial services, healthcare, manufacturing, retail, and government sectors.

How do I access the white paper?

Complete the request form above with your work email and role information. The white paper will be sent to your inbox instantly, at no cost.

How long does it take to deploy enterprise AI with ARPIA?

ARPIA's deployment methodology enables organizations to deploy production AI use cases in 30 to 90 days — compared to the industry average of 6 to 12 months.

Is this white paper free?

Yes. The white paper is completely free. We ask for your work email and role so our team can follow up to discuss your specific use case if relevant.