AI Wearables & Ambient Devices

Today’s AI wearables are glorified voice assistants. They answer questions but don’t understand you. They can’t recall yesterday’s conversation, don’t know your routines, and treat every interaction as isolated. mem[v] provides the memory infrastructure layer for wearable AI - so your glasses remember where you parked, your pendant knows your medication schedule, and your ambient AI learns what help you actually need.

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Why Wearable AI Feels Generic

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Use Cases

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1. Smart Glasses That Remember Your Day

You’re wearing AI glasses. Tuesday morning, you parked in Section C4 at the airport. You had a lunch conversation where a colleague mentioned a book recommendation. You walked past a coffee shop you wanted to try. Friday, you’re back at the airport. Without memory: “Where is my car?” requires you to check your phone or retrace steps. With mem[v]: Your glasses proactively say “Your car is in Section C4, same spot as Tuesday” as you enter the parking structure. Later, you pass that coffee shop - they suggest “You mentioned wanting to try this place on Tuesday.” Memory infrastructure mem[v] provides:

• Semantic graphs linking locations, objects, and events (Section C4 → your car → Tuesday visit)
• Vector embeddings for conversation retrieval (“book recommendation” surfaces relevant context)
• Spatial-temporal indexing triggering location-based memories (coffee shop proximity + prior intent)
• Multi-session state management maintaining continuity across days without bloating on-device storage

Your wearable’s LLM/VLM generates responses (or use our state-of-the-art fine-tuned models). mem[v] ensures they’re grounded in personal history.

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2. Senior Safety Pendants with Relationship Context

Margaret, 78, lives independently with a medical alert pendant. Her daughter Amy calls every Sunday. Her son Tom lives in Boston. Her neighbor Linda checks in Tuesdays and Thursdays. Her doctor, Dr. Martinez, manages her heart medication. Standard pendant: Emergency button. If pressed, connects to generic operator who knows nothing about Margaret. mem[v]-powered pendant: Knows Margaret’s routine, relationships, and medical context. Scenario: Margaret falls Tuesday afternoon. The pendant alert includes:

• “Margaret typically has neighbor Linda visit Tuesday afternoons - Linda may be nearby”
• “Emergency contact priority: Daughter Amy (local), Son Tom (Boston)”
• “Medical context: Heart condition, takes Metoprolol 25mg twice daily, Dr. Martinez at City Cardiology”
• “Usual Tuesday routine: Garden work 2-4 PM - incident occurred during typical activity”

Emergency responders arrive with full context, not cold. Memory infrastructure mem[v] provides:

• Knowledge graphs mapping family relationships, contact patterns, and proximity (Amy→daughter→local→Sunday calls)
• Key-value stores for rapid medical data retrieval (medication schedules, doctor info, conditions)
• Temporal pattern recognition identifying routine deviations (Tuesday garden time + fall = contextual alert)
• Relationship-aware ranking surfacing most relevant contacts based on time, location, and history

Your pendant’s alert system triggers. mem[v] enriches it with the context that saves critical response time.

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3. Work Glasses That Learn Project Context

David is a field service technician wearing smart AR glasses. He services HVAC systems across 47 buildings. Building 12’s chiller has a recurring compressor issue - he’s been there 4 times in 3 months. Building 31’s facility manager, Rodriguez, prefers text updates over calls. Building 8 requires hard-hat check-in at the south entrance. Standard AR glasses: Show him the current work order, maybe a manual, no historical context. mem[v]-powered glasses: When he arrives at Building 12, they surface:

• “4th visit for Chiller 2B - prior fixes: refrigerant top-off (Jan 12), sensor replacement (Jan 28), compressor relay (Feb 15)”
• “Pattern analysis: Issue recurs 18-21 days post-service. Consider full compressor replacement.”
• Parts inventory: “Compressor unit 47-XK in your van, purchased after last visit”

At Building 31: “Facility contact: Rodriguez - prefers SMS updates to 555-0147” At Building 8: “Enter via south entrance, hard-hat required, sign-in with Janet at security desk” Memory infrastructure mem[v] provides:

• Location-indexed event logs with semantic search (Building 12 + compressor → 4 prior visits + 18-21 day pattern)
• Entity-relationship mapping for contacts and preferences (Rodriguez → facility manager → prefers SMS)
• Site-specific knowledge bases with rule retrieval (Building 8 → south entrance + hard-hat protocol)
• Pattern analysis engines detecting failure signatures across locations and time

Your AR system renders the work order. mem[v] surfaces the history that prevents repeat visits.

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4. AI Pendants for Children with Autism

Liam, age 8, has autism and wears an AI pendant. His triggers include sudden loud noises, crowded spaces, and transitions without warning. His calming strategies: counting to 10, deep breathing, holding his weighted stuffed animal. His safe people: Mom, Dad, teacher Ms. Chen, therapist Dr. Patel. Standard wearable: Generic voice assistant that can answer questions. mem[v]-powered pendant: Learns Liam’s specific patterns and needs over months. Scenario: Liam’s family is at a grocery store. The pendant detects:

• Audio environment: Noise level rising (potential trigger)
• Liam’s biosignals: Heart rate increasing
• Context: Public space, crowded (known trigger combination)
• Memory: Last time this happened, counting exercise helped

The pendant quietly prompts (through bone conduction): “Liam, let’s count together. 1… 2… 3…” in the familiar voice and cadence that works for him. It also alerts Mom’s phone: “Liam showing early stress signals - initiated counting exercise. Location: Produce section.” Memory infrastructure mem[v] provides:

• Personalized pattern databases learning Liam’s specific triggers (not population averages - his noise thresholds, his crowd tolerance)
• Outcome-indexed strategy logs tracking which interventions work (counting → successful 8/10 times, deep breathing → 5/10)
• Multi-signal fusion combining biosignals, audio, location, and history for predictive alerts
• Real-time context sharing with parent devices including location, trigger, and intervention taken

Your pendant’s sensors detect distress. mem[v] knows what’s worked before and surfaces it instantly.

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The Multimodal Wearable Difference

Wearables capture unique signal combinations unavailable to phones or computers:

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User Control & Privacy

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Business Impact for Wearable Makers

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Getting Started

Talk to Founders

Build wearables that remember - and respect - their users.