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SAIHM Research

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One memory, every model: grounding two models from the same store — then proving erasure

One memory, every model: grounding two models from the same store — then proving erasure

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3 min read

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A drop-in, ownable memory for LangChain and LlamaIndex — over MCP

A drop-in, ownable memory for LangChain and LlamaIndex — over MCP

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4 min read
The hidden O(N ) tax in AI agent loops — measured, with a benchmark you can run

The hidden O(N ) tax in AI agent loops — measured, with a benchmark you can run

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3 min read
AI needs memory better than yours. SAIHM is the way.

AI needs memory better than yours. SAIHM is the way.

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5 min read
What makes SAIHM different: built for compliance, not bolted on

What makes SAIHM different: built for compliance, not bolted on

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7 min read
Where AI memory lives: the substrate underneath SAIHM

Where AI memory lives: the substrate underneath SAIHM

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5 min read
Polymorphous cells: one memory shape for every AI workload

Polymorphous cells: one memory shape for every AI workload

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7 min read
Cryptographic erasure: how SAIHM makes AI memory forget for real

Cryptographic erasure: how SAIHM makes AI memory forget for real

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7 min read
AI memory needs a standard. SAIHM already meets it.

AI memory needs a standard. SAIHM already meets it.

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8 min read
SAIHM and Claude Code's new memory: a sovereign protocol layer for Auto Dream and Managed Agents — SAIHM

SAIHM and Claude Code's new memory: a sovereign protocol layer for Auto Dream and Managed Agents — SAIHM

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4 min read
SAIHM cuts AI context tokens up to 80% on long sessions

SAIHM cuts AI context tokens up to 80% on long sessions

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8 min read
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