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AletheionAGI Review

AletheionAGI provides AI systems with memory and grounding capabilities, ensuring responses are backed by persistent context and verifiable evidence.

shipped Aug 22, 2026paid
AletheionAGI — product screenshot

Why it matters

1Offers a 'Grounding & Memory pipeline' including persistent memory and fail-closed delivery.
2Launched the AletheionAGI Grounding Bridge on August 14, 2026, to prevent unsupported AI claims.
3Provides a 'Test Plan' for R$ 99 (approximately US$ 18) for 1,000 grounding queries.
4Supports up to 10 concurrent queries on its 'Scale Plan' for 2,000,000 grounded memory decisions per month.

About AletheionAGI

Business Model
Per-Job Pricing
Usage Pricing
R$ 99 for 1,000 queries per query
Target Audience
AI developers and companies implementing AI solutions.

Pricing Plans

Test Plan
R$ 99 / one-time
  • • 1,000 grounding queries
  • • 30 days validity
  • • One namespace
  • • Concurrency 1

Cost Examples

  • • Run a bounded proof of concept before choosing a production plan.
API Docs

Specs

API Docs

View Documentation →

API Available

Yes, public API

overview

What is AletheionAGI?

AletheionAGI is a grounded memory infrastructure tool developed by AletheionAGI, an independent Brazilian AI company, that enables AI developers and enterprises to build AI applications requiring grounded memory infrastructure. It ensures that AI responses are backed by persistent context and verifiable evidence, thus building trust with customers by preventing unsupported claims from reaching users. The system sits before the Large Language Model (LLM) reader, controlling memory, retrieval, authorized evidence, and grounding, augmenting existing LLM readers or RAG systems.

features

Key Features of AletheionAGI

AletheionAGI's core offering is its 'Grounding & Memory pipeline,' which provides a suite of functionalities designed to enhance the reliability and trustworthiness of AI systems. These features ensure that AI responses are consistently supported by verifiable data and operate within defined boundaries.

  • Evidence-bound: Ensures AI responses are tied to authorized evidence.
  • Namespace-isolated: Provides secure and distinct memory contexts for different applications or tenants.
  • Reader-independent: Operates as an augmentation layer, compatible with various LLM readers and RAG systems.
  • Fail-closed delivery: Prevents unsupported claims from being delivered to end-users.
  • Semantic retrieval: Utilizes advanced retrieval methods to fetch relevant context.
  • Grounding delivery: Delivers verifiable context to the LLM for informed responses.
  • Explicit authority: Manages and enforces authorization for evidence access.
  • Reversible feedback: Allows for tracking and auditing of AI decisions and memory usage.
  • API available: Provides programmatic access for integration into existing AI workflows.

use cases

Who Should Use AletheionAGI?

AletheionAGI is designed for AI developers, researchers, and enterprises that require robust, verifiable, and auditable memory and grounding infrastructure for their AI applications. Its capabilities are particularly beneficial in scenarios where factual accuracy, data privacy, and trust are paramount.

  • AI developers building applications that require grounded memory infrastructure and verifiable responses.
  • Enterprises implementing retrieval and authorization functionalities in AI services, such as customer support and commerce agents.
  • Organizations monitoring and improving internal coherence and factuality in neural language models for internal copilots.
  • AI researchers conducting experimental research on state-based causal AI models and evaluating AI performance.
  • Teams needing to prevent unsupported claims in AI-driven customer support, ensuring account history is useful without stale or unauthorized memory.

how to use

How to Use AletheionAGI

AletheionAGI integrates into existing AI architectures, typically positioned before the Large Language Model (LLM) reader. Users can begin by evaluating its capabilities through a proof-of-concept plan.

  • 1Sign up for a 'Test Plan' to access 1,000 grounding queries for R$ 99.
  • 2Connect your preferred LLM reader key to the AletheionAGI pipeline.
  • 3Utilize the API (documentation at https://www.aletheionagi.com/docs) to integrate grounded memory into your AI application.
  • 4Configure namespaces and evidence boundaries for specific use cases like customer support or internal copilots.
  • 5Monitor usage tracking and audit history to ensure compliance and improve AI response quality.

pricing

AletheionAGI Pricing & Plans

AletheionAGI offers a tiered pricing structure designed to accommodate various stages of AI application development, from proof-of-concept to large-scale production. All plans include the complete AletheionAGI Grounding & Memory pipeline, with customers responsible for connecting and paying their preferred LLM provider separately.

  • Test Plan: R$ 99 (one-time payment) for 1,000 grounding queries, 30-day validity, 1 concurrency, 1 namespace, and 1 GiB storage. No auto-renewal or overage charges.
  • Launch Plan: Contact sales for pricing. Includes 25,000 grounded memory decisions/month, up to 5 concurrent queries, and 10 GiB for memories, evidence, and audit data. Designed for products entering production with standard support.
  • Scale Plan: Contact sales for pricing. Includes 2,000,000 grounded memory decisions/month, up to 10 concurrent queries, and 1,000 GiB for memories, evidence, and audit data. Features enterprise support, annual billing, and optional dedicated capacity for recurring customer traffic.

Pros

  • +Ensures AI responses are backed by persistent context and verifiable evidence, enhancing trust.
  • +Implements a 'fail-closed' delivery boundary to prevent unsupported claims from reaching users.
  • +Offers a complete 'Grounding & Memory pipeline' including hybrid retrieval and authorized evidence.
  • +Reader-independent design allows integration with existing LLM readers and RAG systems.
  • +Provides detailed usage tracking and audit history for compliance and performance monitoring.
  • +Offers tiered pricing plans suitable for proof-of-concept, launch, and scaling production environments.

Cons

  • −Requires separate payment and connection to an LLM provider, adding an additional cost component.
  • −Specific public user reviews or detailed reception analyses are not readily available.
  • −The 'Launch' and 'Scale' plans require contacting sales for pricing, lacking transparent upfront costs for production use.
  • −Concurrency limits are relatively low for initial plans (1 for 'Test', 5 for 'Launch').
  • −Primarily focused on grounding and memory, not a full-stack AI development platform.

Similar Tools

AletheionAGI vs Competitors

AletheionAGI positions itself as a specialized 'grounded memory infrastructure' provider, focusing on verifiable systems and preventing unsupported claims. It augments, rather than replaces, existing RAG systems and LLM readers, distinguishing its approach from broader AI development frameworks.

1
LangChainOn Stork

A comprehensive framework for developing applications powered by language models, offering modules for memory, agents, and Retrieval Augmented Generation (RAG).

While AletheionAGI is a ready-to-use solution, LangChain provides the building blocks and abstractions to construct a custom AI system with memory and grounding, requiring more development effort but offering greater flexibility and control.

2
LlamaIndexOn Stork

Specializes in data ingestion, indexing, and retrieval for Large Language Models (LLMs), making it ideal for building RAG applications that provide context and grounding.

LlamaIndex focuses heavily on the data aspect of grounding and context, whereas AletheionAGI presents a more integrated solution; using LlamaIndex means you would build out the full application yourself, offering more customization at the cost of immediate plug-and-play functionality.

3
HaystackOn Stork

An open-source NLP framework for building custom search and question-answering systems, including robust RAG capabilities to ground LLM responses with specific data.

Haystack provides a powerful framework for search and RAG, similar to how AletheionAGI grounds responses, but it requires more hands-on implementation to achieve a complete solution for memory and verifiable evidence.

4
PrivateGPT↗

Allows you to interact with your documents using an LLM, ensuring all data remains private and local, providing grounding from your own files without sending data to external services.

PrivateGPT offers a self-contained solution for grounding AI responses with local data, but it is primarily focused on local document interaction rather than a general-purpose memory and grounding service for external AI systems like AletheionAGI.

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