aletheionagi

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1. 10/14/2026

   Updated: features, limitations, summary, use cases, walkthrough

   See recorded changes

   features

   Before: ["Persistent memory, authorized retrieval, and fail-closed grounding for production AI systems.","Transforms raw user language into validated intent state with facts, constraints, preferences, unresolved dimensions and provenance.","Delivers governed experience embedded directly in websites and applications using intent, evidence and policy.","Evidence-driven B2B prospecting that discovers companies, creates individual outreach, follows conversations and learns from real outcomes."]

   After: ["Persistent memory, authorized retrieval, and fail-closed grounding for production AI systems.","Transforms raw user language into validated intent state with facts, constraints, preferences, unresolved dimensions and provenance.","Delivers governed experience embedded directly in websites and applications using intent, evidence and policy.","Evidence-driven B2B prospecting that discovers companies, creates individual outreach, follows conversations and learns from real outcomes.","An RLM-native agent with a persistent Python kernel, explicit tools and an execution-first workflow."]

   limitations

   Before: ["Systems expose uncertainty when evidence is insufficient instead of manufacturing certainty."]

   After: ["Systems should expose uncertainty when evidence is insufficient instead of manufacturing certainty."]

   summary

   Before: "AletheionAGI is an independent Brazilian AI research and engineering organization focused on investigating state, memory, causality, grounding, and intent to build verifiable AI systems. Their products are research-based, modular, and designed to be inspected, tested, and challenged independently."

   After: "AletheionAGI is an independent Brazilian AI research and engineering organization focused on investigating state, memory, causality, grounding, and intent in AI systems. It builds verifiable, inspectable, and testable infrastructure products based on research findings."

   useCases

   Before: ["Building AI systems that preserve useful history without repeatedly exposing the entire context.","Improving temporal continuity in AI mechanisms and understanding conditions under which they fail."]

   After: ["Building AI systems with compact persistent state that preserves useful history without repeatedly exposing the entire context.","Improving temporal continuity in AI mechanisms and understanding conditions under which they fail."]

   walkthrough

   Before: ["Claims identify their protocol, denominator, version and limitation before narrative is formed.","A model may propose; authenticated policy and canonical state decide before autonomy is granted."]

   After: ["IntentParse structures intent, GQueries grounds evidence, Widport delivers the authorized experience, and Tune2all applies evidence to GTM learning, with each credential and call remaining explicit.","Research program builds experiments to learn which AI mechanisms work, which fail, and where their limits begin."]

2. 9/13/2026

   Updated: best for, limitations, walkthrough

   See recorded changes

   bestFor

   Before: ["Organizations seeking AI infrastructure with explicit evidence, authority controls, and abstention mechanisms to prevent unsupported claims."]

   After: []

   limitations

   Before: []

   After: ["Systems expose uncertainty when evidence is insufficient instead of manufacturing certainty."]

   walkthrough

   Before: ["IntentParse structures intent, GQueries grounds evidence, Widport delivers the authorized experience, and Tune2all applies evidence to GTM learning, with each credential and call remaining explicit.","Research-led approach involves building experiments to learn which AI mechanisms work, which fail, and where their limits begin."]

   After: ["Claims identify their protocol, denominator, version and limitation before narrative is formed.","A model may propose; authenticated policy and canonical state decide before autonomy is granted."]