Memory that
actually listens.
The first memory layer built for voice AI agents - not a text database with a voice wrapper, engineered for the way conversations actually work.
Preference
Prefers email follow-ups
Commitment
Renewal call due Q2 2025
Context
Budget range: $40-60K
Profile
Team of 12 people
Language
English + Spanish
Memory is the foundation of every great interaction
Whether it's customer support, sales, healthcare, or smart assistants — an AI agent without memory is just an expensive FAQ bot.
Agents forget by default
Without memory, every call starts cold. Customers repeat themselves, agents re-ask the same questions, and relationships never deepen — even after hundreds of interactions.
Context survives every session
Orvin persists every preference, promise, and profile detail across sessions automatically. Your agent picks up exactly where it left off — with full context, zero re-onboarding.
Personalisation at scale
When your agent remembers names, past issues, team structure, and communication style, every interaction feels personal — for every user, across every call.
Commitments never fall through
Orvin extracts and tracks every commitment made in a conversation: follow-ups, deadlines, action items. Agents never drop the ball again.
Temporal awareness
Voice carries time — urgency in tone, past-tense references, real-time corrections. Orvin understands the temporal dimension of voice that flat text pipelines miss entirely.
Multi-agent memory sharing
Sales, support, and onboarding agents share a unified participant memory. No silo switching, no lost context when a call gets transferred.
Memory improves over time
As Orvin builds richer participant profiles, retrieval gets smarter. Your agent gets better at serving users — not just on day one, but on day 100.
Privacy-first architecture
Participant-scoped memory, workspace isolation, consent tooling, and deletion support built in. Enterprise-grade memory without the compliance headaches.
< 10ms
Context retrieval
100%
Voice-native
∞
Participant memory
4+
Adapter frameworks
Others are generalised. Text only.
We're voice.
Every other memory provider was built for text: chat logs, documents, embeddings. They work for LLM apps. They fall apart when audio enters the room.
Generic memory providers
Text-first, voice-retrofitted
- Flattens rich spoken context into plain text strings
- Misses interruptions, corrections, and tone signals
- No concept of turn lifecycle or playback events
- Can't distinguish agent speech from user speech
- Requires heavy glue code for every voice provider
- Ignores temporal cues like 'I said earlier' or 'never mind'
Orvin
Voice-native, built from scratch
- Normalised voice interaction model from day one
- Ingests transcript + acoustic hints + speaker identity natively
- Full turn lifecycle: started → updated → finalised
- Separate playback event model for agent speech
- First-party adapters for LiveKit, Pipecat, VAPI, Retell
- Redis hot-state for sub-10ms context delivery mid-call
| Capability | Generic providers | Orvin |
|---|---|---|
Designed for voice conversations Built from scratch for audio-first interactions, not bolted on. | ||
Acoustic hint ingestion Captures tone signals, interruptions, and real-time corrections. | ||
Turn-level temporal reasoning Understands multi-turn context that only emerges over a conversation. | ||
Playback & commitment tracking Tracks agent speech playback and extracted user commitments natively. | ||
Voice provider adapters LiveKit, Pipecat, VAPI, Retell — first-party adapters with zero glue code. | ||
Hot-state session recovery Redis-backed context warm path for sub-10ms context delivery mid-call. | ||
Text/document storage Document ingestion supported — but not the primary use case. | ||
Generic API Others are built for generic text storage and retrieval. |
Built for every industry that talks to people
Orvin's voice-native memory layer powers AI agents wherever persistent, contextual conversations matter most.
Customer Support
Agents remember every past issue, ticket, and resolution. Customers never repeat themselves. Support quality compounds session over session.
- Persist issue history across all channels
- Track unresolved commitments and follow-ups
- Personalise tone based on customer profile
Sales & Revenue
Never lose track of deal context, stakeholder preferences, or renewal timelines. Your sales agent remembers what the CRM misses.
- Extract deal signals and objections automatically
- Track pricing discussions and commitments
- Warm every follow-up call with full context
Healthcare
Persistent patient context for voice-first healthcare AI. Symptoms, preferences, medication history, and care instructions — always available.
- Retain symptom history across visits
- Surface relevant care context automatically
- HIPAA-aware participant scoping
Smart Home & IoT
Voice assistants that learn your household. Routines, preferences, and instructions persist naturally — no manual configuration required.
- Learn habits without explicit commands
- Multi-user household profile management
- Context-aware device automation
Education & Tutoring
AI tutors that remember learning gaps, progress, and explanations that worked. Every session builds on the last.
- Track knowledge gaps per learner
- Remember preferred explanation styles
- Adaptive curriculum based on history
Enterprise Ops
Internal voice agents for onboarding, HR, and operations — with memory that spans departments and functions.
- Cross-department context sharing
- Role-aware memory scoping
- Audit trail for all interactions
AI Companion
Companions that genuinely know their users. Orvin powers the memory layer that makes AI relationships feel real and lasting.
- Deep profile synthesis over time
- Tone and preference learning
- Relationship continuity across months
Multilingual Agents
Memory that works across languages. Orvin normalises multilingual voice context so your agent remembers in any tongue.
- Multilingual extraction normalisation
- Language-agnostic participant profiles
- Cross-language context consistency
Ready to add memory to your agent?
Start building in minutes
Plug Orvin into LiveKit, Pipecat, or your own websocket pipeline with a single adapter package. No schema design, no vector DB setup.