Voice conversations that pick up where they left off.
Voice is the most natural interface for AI, and the most frustrating when the agent doesn't remember you. Hebbrix gives your voice agents persistent memory so every call feels like a continuation, not a new conversation with a stranger.
What continuity actually feels like
When a voice agent has memory, callers stop explaining themselves. Each call is shorter, more useful, and less frustrating, because the agent already knows who it's talking to.
Without memory
"Hi, I'm Marcus. I have a Pro plan. I'm calling about the API rate limits…"
Call: 14 minutes. 3 minutes just on re-introduction.
"Hi, I'm Marcus again. Still on the Pro plan. Last time we talked about rate limits…"
Call: 11 minutes. Same introduction.
"Hi again, Marcus here, Pro plan…" The caller sounds frustrated before they even start.
With Hebbrix memory
First call, Marcus explained his use case
Rate limit issue on the batch endpoint. High-volume data pipeline on Pro plan. Wants to understand the upgrade path to Scale.
"Hi Marcus, following up on the rate limit issue"
Agent opens with context. Marcus doesn't re-introduce. Call is 6 minutes. Discusses Scale plan details and pricing.
"Ready to upgrade your plan, Marcus?"
Agent proactively asks about the upgrade since Marcus expressed interest. No re-explanation needed. Decision made in 4 minutes.
Memory built for how voice conversations actually work
Cross-session continuity
Every call resumes where the last one left off. The caller never re-introduces themselves or repeats context.
Latency-optimized search
Voice needs fast responses. Hebbrix's 5-layer search runs in under a second, fast enough to load context before the caller finishes their first sentence.
Speech-to-memory pipeline
Transcripts are processed for structured knowledge, including preferences stated out loud, decisions made, and follow-ups promised, then stored automatically.
Caller identification
Collections scope memory per caller. Whether you identify them by phone number, account ID, or voice biometrics, each caller gets their own persistent context.
Sentiment history
The knowledge graph tracks frustration patterns, past escalations, and prior resolutions. High-value callers at risk of leaving surface on their own.
Multi-agent handoffs
Memory persists across IVR, bot, and human agent handoffs. The human agent sees everything the bot learned, so the customer doesn't repeat it.
Voice agents that never make callers repeat themselves
Free tier, no credit card. Build a voice agent that actually remembers your customers.