๐Ÿง  The differentiator

Memory that survives the visit.

A native engine, not a CRM lookup.

Most AI chatbots say "memory" โ€” but they really mean a CRM record. Shrinam runs a native 6-layer memory engine built in-house: visitor-level, cross-session, self-healing, and impossible to confuse between speakers.

Conversation BufferCurrent turn contextL1Context MemorySpeaker-tagged utterancesL2Context Frame ฮš5-component episodic frameL3MEEKU recallLong-term + scored retrievalL4RHCM hierarchyRecursive content mapL5CREG causal graph6 typed edges, drift-proofL6

A 6-layer memory architecture

Each layer is a Hindustani AI Logics invention โ€” not a vendor SDK.

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L1 ยท Conversation Buffer

Active-turn context. What was just said, what is on screen, who is speaking.

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L2 ยท Context Memory

Every utterance is tagged with the speaker's voice-ID and name โ€” never confuses who said what.

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L3 ยท Context Frame ฮš

Every episode is captured as a 5-component frame: identity (ฮน), topic (ฯ„), reason (ฯ), affect (ฮฑ), state (ฯƒ).

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L4 ยท MEEKU recall

Long-term store with relevance scoring. Past chunks are blended into the current turn at retrieval time.

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L5 ยท RHCM hierarchy

Recursive Hierarchical Content Map โ€” billions of memory nodes, organized for sub-second retrieval.

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L6 ยท CREG + CDSHA

Causal event graph with self-healing hashes. Drift is detected and repaired before it shows up as a hallucination.

Real moments that memory unlocks

These come from actual conversation logs.

"Welcome back, Raj."

Recognized by voice + name three months after the first visit. No login required.

"Last time you asked about pricing."

Picks up the previous thread at the exact line where it ended.

"Ready for that demo we discussed?"

Natural follow-up from past visit history. Asks at the right moment, never spams.

"Your use case is the sales SDR."

Stored intent + business context, recalled at the start of every new conversation.

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Memory horizon (cross-session)
< 80ms
Recall latency at scale
6
Memory layers, fully native
0
Vendor SDKs in the path

Shrinam vs typical chatbots

A side-by-side on memory depth.

CapabilityShrinamTypical chatbot
Returning visitor recognizedโœ“ Voice-ID + name + historyโœ— Cookie-based, often missed
Cross-session continuityโœ“ Multi-visit threadโœ— Session-only
Identity confusionโœ“ Speaker-tagged turnsโœ— Mixes user inputs together
Memory drift over timeโœ“ CDSHA self-healโœ— Hallucinates after weeks
Cold context recallโœ“ Dormant store, wake on demandโœ— Forgotten โ€” re-asks the same
Data sovereigntyโœ“ Self-host on your infraโœ— Vendor cloud only

Memory is the difference between a chatbot and an employee.

Come back tomorrow. She will remember everything you said today.

Talk to Shrinam โ†’