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What is Ejentum?

Ejentum acts as a systematic reasoning framework designed specifically for agentic AI, improving the trustworthiness, traceability, and consistency of LLM agents when handling complex or lengthy assignments. This groundbreaking tool can be activated by agents during ongoing tasks, allowing for accurate cognitive processes tailored to their unique challenges, which provides opportunities for immediate adjustments in reasoning rather than relying solely on fixed prompts. Created to prevent AI agents from wandering off track, flattering users, creating falsehoods, or clinging to erroneous assumptions, Ejentum also guarantees that they do not accept shallow answers or lose crucial context through successive interactions. The framework features an impressive 679 capabilities categorized into four cognitive harnesses: reasoning, code, anti-deception, and memory. The reasoning harness specifically focuses analytical capabilities on comprehending causality, temporal aspects, spatial relationships, simulations, abstractions, and metacognition, thereby helping agents avoid superficial pattern recognition. Through the integration of these varied functionalities, Ejentum empowers AI to engage more profoundly with tasks, leading to improvements in the overall quality and depth of their outputs. Moreover, this structured approach not only enhances operational effectiveness but also fosters a more profound understanding of intricate problem-solving scenarios.

What is Causality Engine?

Numerous attribution tools can show which advertisements a customer engaged with before completing a purchase, highlighting correlation. However, Causality Engine goes a step further by revealing how much your revenue would have been affected if those advertisements had not been executed, thereby proving causation. By employing causal inference—akin to the statistical methods used in clinical drug studies—we evaluate the actual additional revenue produced by each marketing channel. This methodology guarantees that the outcomes are purely the result of marketing initiatives without any external influences or alterations. The calculations can be easily understood: for example, if Meta indicates a 4.2x ROAS, yet your total revenue remains the same after a 30% reduction in spending, it suggests those conversions would have occurred anyway. Causality Engine accurately quantifies this phenomenon. By integrating your GA4 and Shopify data, you can obtain your preliminary causal analysis in under five minutes. There’s no requirement for tracking pixels, cookies, or complex integrations—just upload your data to identify which channels are genuinely facilitating growth versus those that only take credit for existing gains. The service provides a one-time analysis for $99, and ongoing insights are offered for $299 each month, enabling businesses to continuously monitor their marketing performance and revenue contributions. This approach fosters a deeper understanding of marketing effectiveness over time, allowing for more informed decision-making.

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Media

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Integrations Supported

Agno
Amazon Bedrock
Anthropic
AutoGen
Claude Code
Codex CLI
DeepSeek
Devin Desktop
LangChain
LangGraph
LlamaIndex
Make
Meta AI
Mistral AI
Model Context Protocol (MCP)
Perplexity
Replicate
Smolagents
Voiceflow

Integrations Supported

Meta Ads

API Availability

Has API

API Availability

Pricing Information

€25 per month
Free Version
Free Trial Offered?

Pricing Information

$99/month

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Ejentum

Company Location

United States

Company Website

ejentum.com

Company Facts

Organization Name

Causality Engine

Date Founded

2023

Company Location

Netherlands

Company Website

causalityengine.ai

Categories and Features

Agentic AI

Not specified

Categories and Features

Marketing Attribution

Not specified

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