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SHODH-MEMORY

Cognitive Memory Infrastructure for Autonomous Systems

SLIDE 1: TITLE

SHODH-MEMORY

Cognitive Memory Infrastructure for AI Agents, Robotics & Edge Devices

Memory that learns. Single binary. Runs offline.

Founder: Varun Sharma Website: shodh-rag.com/memory GitHub: github.com/varun29ankuS/shodh-memory

SLIDE 2: THE PROBLEM

AI Agents Have Amnesia

Every AI session starts from zero.

Pain Point Impact
No persistent memory Agents repeat mistakes, ask same questions
Cloud dependency Latency kills autonomous operations (drones, robots)
Privacy concerns Sensitive data leaves device
No learning Agents don't improve from experience

Real Cost:

  • Robotics companies lose $50K-500K per failed autonomous mission
  • AI assistants waste 30% of user time re-explaining context
  • Edge AI devices cannot operate offline reliably

"We need memory that works without internet, learns from mistakes, and fits on our drone's compute module." — Robotics Company (Active Pilot Discussion)

SLIDE 3: OUR SOLUTION

Shodh-Memory: The Brain for Autonomous Systems

What we built: A cognitive memory system that learns like a brain — Hebbian learning, activation decay, semantic consolidation — in a single 8MB binary that runs 100% offline.

Key Differentiators:

Feature Shodh-Memory Competitors
Deployment Single binary (8MB) Cloud API / Multi-service
Offline 100% No / Partial
Latency <60ms Network-bound
Learning Hebbian plasticity Static vectors
Privacy On-device Cloud storage

The Result:

  • Robots that remember which actions worked
  • Drones that don't repeat navigation mistakes
  • AI assistants that learn user preferences

SLIDE 4: HOW IT WORKS

Biologically-Inspired 3-Tier Architecture

┌─────────────────────────────────────────────────────────────┐
│                    SHODH-MEMORY                             │
├─────────────────────────────────────────────────────────────┤
│  WORKING MEMORY (100 items)                                 │
│  ├── Capacity-limited, immediate context                    │
│  └── Overflow triggers importance-based selection           │
├─────────────────────────────────────────────────────────────┤
│  SESSION MEMORY (500MB)                                     │
│  ├── RocksDB persistence                                    │
│  ├── Vamana HNSW vector index (billion-scale)               │
│  └── Entity-relationship graph                              │
├─────────────────────────────────────────────────────────────┤
│  LONG-TERM MEMORY (unlimited)                               │
│  ├── Semantic consolidation (episodic → facts)              │
│  ├── Hebbian-strengthened associations                      │
│  └── Long-term potentiation (permanent connections)         │
└─────────────────────────────────────────────────────────────┘

Based on established neuroscience:

  • Cowan's Working Memory Model (capacity limits)
  • Hebbian Synaptic Plasticity (learning through co-activation)
  • Sleep-like Consolidation (memory compression over time)

SLIDE 5: TECHNOLOGY INNOVATION

What Makes Us Different

1. Hebbian Learning (Not Just Vector Similarity)

Strength(t+1) = Strength(t) + η(1 - Strength) × co_activation
  • Memories used together strengthen their connection
  • After 50+ co-activations → permanent (Long-Term Potentiation)
  • Failed retrievals weaken associations (2:1 decay ratio)

2. Realistic Forgetting (Exponential Decay)

Activation(t) = A₀ × e^(-λt)    where λ = 0.02/day
  • 14-day half-life matches human forgetting curves
  • Recent access recovers activation (recency effect)
  • Important memories decay slower

3. Semantic Consolidation

  • Old episodic memories (7+ days) → compressed semantic facts
  • Preserves meaning, reduces storage
  • Mirrors hippocampal-neocortical transfer in sleep

4. Named Entity Recognition

  • TinyBERT NER extracts Person, Organization, Location
  • Entities create knowledge graph relationships
  • Boost memory importance automatically

Result: Memory that behaves like biological memory, not a database.

SLIDE 6: PERFORMANCE

Production-Grade Speed

Benchmarked on Intel i7-1355U (consumer hardware):

Operation Latency Notes
Store memory 55ms Embedding + NER + storage
Semantic search 45ms Vector + graph retrieval
Tag lookup 1ms Direct index
Entity query 10ms Graph traversal

Scale:

  • 1M+ memories per user
  • O(log n) search via Vamana HNSW
  • 8MB binary + 40MB models

Comparison:

System Latency Deployment
Shodh-Memory 45ms Single binary
Mem0 (Cloud) 200-500ms API + network
Cognee 100-300ms Neo4j + Vector DB
Pinecone 50-100ms Cloud-only

SLIDE 7: MARKET OPPORTUNITY

$47B Edge AI Market by 2030

Target Segments:

Segment TAM Pain Point Our Value
Robotics $12B Mission failures from context loss Persistent learning memory
Drones/UAV $8B Offline autonomy requirements 100% air-gapped operation
Defense/Aerospace $15B Security + no cloud On-device, auditable
Industrial IoT $7B Factory floor latency Sub-60ms response
AI Assistants $5B User context retention Cross-session memory

Why Now:

  1. Edge AI compute is finally viable (Jetson, Apple Silicon)
  2. AI agents are mainstream (GPT, Claude, local LLMs)
  3. Privacy regulations tightening (GDPR, DPDP Act)
  4. India's robotics industry growing 20% CAGR

Beachhead: Robotics & autonomous systems (clear pain, budget authority)

SLIDE 8: TRACTION

Early Validation (4 days since launch)

Downloads:

Platform Downloads
npm (MCP server) ~800
PyPI (Python) ~1,800
crates.io (Rust) ~100
Total ~2,700

Registry Presence:

  • MCP Registry (Model Context Protocol)
  • npm @shodh/memory-mcp
  • PyPI shodh-memory
  • crates.io shodh-memory

Business Development:

  • Active pilot discussion with funded robotics company
  • Targeting defense/aerospace contacts

Technical Validation:

  • 19,000+ LOC production code
  • 600+ test cases
  • 6 benchmark suites

SLIDE 9: BUSINESS MODEL

Open Core + Enterprise

Free (Apache 2.0):

  • Full memory system
  • All cognitive features
  • Single-user deployment
  • Community support

Enterprise (Paid License):

Tier Price Features
Team $500/mo Multi-tenant, priority support, SLA
Enterprise $2,000/mo SSO, audit logs, custom integrations
OEM Custom Per-device licensing for robotics/drones

Revenue Projections:

Year Customers ARR
Y1 5 pilots $100K
Y2 20 enterprise $500K
Y3 50+ enterprise + OEM $2M+

Why This Works:

  • Open source builds trust & adoption
  • Enterprise features are must-haves for production
  • OEM licensing scales with customer success

SLIDE 10: COMPETITION

Landscape

Shodh-Memory Mem0 Cognee Pinecone
Focus Edge/Offline Cloud Memory Knowledge Graphs Vector DB
Deployment Single binary API Multi-service Cloud
Offline 100% No Partial No
Learning Hebbian Static Graph updates None
Latency <60ms Network DB-bound 50-100ms
Pricing Open core Per-API-call Enterprise Usage-based

Our Moat:

  1. Technical: Neuroscience-grounded algorithms (not easily replicated)
  2. Edge-first: Competitors are cloud-first, retrofitting to edge is hard
  3. Performance: 5-10x faster than network-bound alternatives
  4. Open Source: Community adoption creates switching costs

Defensibility:

  • Protocol standardization opportunity (HMCP - Hierarchical Memory Context Protocol)
  • First-mover in offline cognitive memory
  • Patent potential on consolidation algorithms

SLIDE 11: ROADMAP

18-Month Plan

Phase 1: Product-Market Fit (Months 1-6)

  • Close robotics pilot → paying customer
  • ARM64 Linux support (Jetson, RPi)
  • Harden edge cases from production feedback
  • Land 3-5 customers in robotics/defense

Phase 2: Scale (Months 6-12)

  • Multi-agent memory sharing
  • Defense/aerospace certifications
  • Enterprise dashboard & admin console
  • Hire 2 senior engineers

Phase 3: Platform (Months 12-18)

  • HMCP Protocol specification (open standard)
  • Memory federation for agent hierarchies
  • Memory marketplace (pre-trained domain memories)
  • Series A preparation

Technical Milestones:

Milestone Timeline
ARM64 Linux Month 2
First paying customer Month 3
10 enterprise users Month 9
Protocol v1.0 Month 15

VISION

"We're building the memory layer for the autonomous future."

Every drone, robot, and AI agent will need persistent, learning memory. We're building the infrastructure — offline-first, privacy-preserving, biologically-inspired.

India can lead this.

CONTACT

Varun Sharma

  • Email: 29.varun@gmail.com
  • GitHub: github.com/varun29ankuS/shodh-memory
  • Website: shodh-rag.com/memory

Shodh (शोध) = Research, Discovery

APPENDIX: TECHNICAL DEPTH

For Technical Reviewers

Code Metrics:

  • 19000+ LOC core Rust
  • 600+ tests
  • 31 modules
  • 651+ unit tests
  • 6 benchmark suites

Algorithm References:

  1. Cowan, N. (2010). Working Memory Capacity. Current Directions in Psychological Science
  2. Magee & Grienberger (2020). Synaptic Plasticity Forms and Functions. Annual Review of Neuroscience
  3. Subramanya et al. (2019). DiskANN: Billion-point Nearest Neighbor Search. NeurIPS

Dependencies:

  • Rust 2021 edition
  • ONNX Runtime 1.22 (MiniLM-L6, TinyBERT)
  • RocksDB (persistence)
  • Vamana HNSW (vector index)

Platform Support:

  • Linux x86_64 ✓
  • macOS ARM64 ✓
  • Windows x86_64 ✓
  • Linux ARM64 (in progress)