Memory at the speed of speech.
The operating point where voice agents stop sounding like software.
MemoAIr is the realtime memory and context engineering layer for voice agents. Drop it between your voice stack and your LLM. Ship in hours.
Trusted by teams shipping voice AI in production
Not a vector DB with a wrapper. A memory layer built for voice.
Speed without the accuracy tax
Most stacks force a choice: fast or accurate. We hold 80%+ accuracy at sub-10ms, the point where voice feels human.
Context engineering, not dumping
We retrieve only the facts that matter, compress them, and cache the rest. Fewer tokens per turn, better answers.
Slots into your stack
One SDK. Native plugins for LiveKit, Pipecat, VAPI, and ElevenLabs. No infra to rip out, no vector DB to maintain.
Fluent across Indian languages
Users mix Hindi, Tamil, and English mid-sentence. We keep the thread, so nothing drops between turns.
Voice agents fail at the memory layer
Latency, hallucinations, and bloated prompts are not LLM problems. They're memory problems. Today's stacks weren't built for realtime speech.
Retrieval is too slow
Vector DBs add round-trips that turn natural conversation into walkie-talkie pauses.
Context is dumped, not engineered
Agents ship the entire history into every prompt. Costs scale linearly with conversation length.
Accuracy collapses under speed
When teams tune for latency, they lose recall. Wrong facts get spoken out loud.
Memory resets every call
Customers re-explain themselves. Agents re-ask questions. Trust evaporates.
Drop MemoAIr between your agent and your LLM. That's it.
MemoAIr intercepts every turn, retrieves only the relevant memory, engineers a tight prompt, and serves it back fast enough that the user never notices a pause.
Your bottleneck isn't your LLM. It's memory.
Real workloads. Real voice traffic. Measured end-to-end, including embedding and retrieval.
Benchmark: 100K documents, 10 voice workloads, 50 queries per workload. View methodology
Built for voice agents where every turn matters
The agent remembers prior tickets, account context, and what the customer said two minutes ago. No "can you repeat that?" loops.
Drops into your existing voice stack
- LiveKit
- Pipecat
- VAPI
- ElevenLabs
- Retell
- Vogent
- OpenAI
- Anthropic
- Groq
- Together
- Open-source
- Salesforce
- HubSpot
- Zendesk
- Intercom
- Notion
- Custom webhooks
- LangChain
- LlamaIndex
- Vercel AI SDK
- MCP Server
What changes when memory stops being the bottleneck
Agents that sound human
800ms to 1.5s pauses on retrieval
<10ms, well under the threshold of perception
LLM bill drops
3,000+ tokens per turn (full context dumped)
800 to 1,200 tokens per turn (context engineered)
Accuracy goes up, not down
~60% top-k recall at low latency
80%+, with no latency penalty
Ship in a day, not a quarter
4 to 8 weeks wiring up retrieval, eval, and infra
SDK installed, plugged in, live in production
Common questions
Stop letting memory slow your voice agent down.
SOTA accuracy. <10ms latency. Lower token bills. One SDK away.
Early access · Priority onboarding · Direct line to the team