Blog

Whitepapers, articles and examples that explain algorithmation, UAPF and the path to enterprise AI.

Featured Guide

Algorithmation Playbook: Complete Guide for AI Integrators

45 min read · Comprehensive Technical Guide

A step-by-step guide to converting business processes into executable UAPF packages. From process selection through BPMN/DMN modeling to deployment—everything an AI integrator needs.

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Includes AI agent test reports

Whitepaper: The Prerequisite for Enterprise AI

Why algorithmation is required before AI agents can operate safely in regulated and complex organizations.

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Article: From Processes to Executable Intelligence

How BPMN, DMN, CMMN and UAPF work together to create a unified representation of enterprise logic.

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Example: Algorithmating a Credit Approval Process

A concrete example model set and UAPF package for a financial services use case.

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Article: From Algorithmated Permits to AI Agents

How to turn a municipal building permit process into a UAPF-based AI capability, keeping legal rules explicit and under public control.

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Article: From Algorithmated Credit to AI Agents

How to expose a UAPF model set as MCP tools so AI agents can safely run real credit approval decisions, with explainability and governance built in.

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Article: From Algorithmated Processes to Gemini

A practical guide to wiring Algorithmated UAPF processes into Google Gemini using native function calling as the governance layer.

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Article: Algorithmating Enterprise Processes for Claude AI Agents

How to connect Claude AI agents to Algorithmated enterprise processes using UAPF and MCP, keeping rules explicit and governance intact.

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Article: Empowering Grok with Governed Intelligence

A conceptual example of how Grok from xAI could use Algomation's UAPF and MCP to power governed, explainable credit decisions.

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Concept: From Single Processes to the Algorithmated Enterprise

A concept paper on how to scale UAPF from single workflow demos to an enterprise-wide algorithm layer with registries, CI/CD, MCP adapters, and AI agents.

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