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Multimodal perception system for real-time conversation

Details

External ID
46965012
Source
HN
Company
—
Product
Multimodal perception system for real-time conversation
Website domain
tavuslabs.org
Launched
Feb. 10, 2026
Cohort
—
Upvotes
54
Upvotes percentile
0.8268194070080862
Tags
—
Fetched at
Sept. 7, 2026, 9:25 p.m.
Updated at
Sept. 7, 2026, 9:25 p.m.

Description

I work on real-time voice/video AI at Tavus and for the past few years, I’ve mostly focused on how machines respond in a conversation.One thing that’s always bothered me is that almost all conversational systems still reduce everything to transcripts, and throw away a ton of signals that need to be used downstream. Some existing emotion understanding models try to analyze and classify into small sets of arbitrary boxes, but they either aren’t fast / rich enough to do this with conviction in real-time.So I built a multimodal perception system which gives us a way to encode visual and audio conversational signals and have them translated into natural language by aligning a small LLM on these signals, such that the agent can "see" and "hear" you, and that you can interface with it via an OpenAI compatible tool schema in a live conversation.It outputs short natural language descriptions of what’s going on in the interaction - things like uncertainty building, sarcasm, disengagement, or even shift in attention of a single turn in a convo.Some quick specs:- Runs in real-time per conversation- Processing at ~15fps video + overlapping audio alongside the conversation- Handles nuanced emotions, whispers vs shouts- Trained on synthetic + internal convo dataHappy to answer questions or go deeper on architecture/tradeoffsMore details here: https://www.tavus.io/post/raven-1-bringing-emotional-intelli...

Enrichment

Theme
voice AI agents and infrastructure
Vertical
Horizontal
Function
Agent / copilot
Audience
Developer
AI stance
AI-native
Project type
Commercial product
Normalized one-liner
multimodal perception for real-time conversation
Manually corrected
False

Could you build this?

No Building sub-second multimodal conversational perception that processes audio-visual streams directly without transcript reduction requires cutting-edge research and heavy GPU inference infrastructure.

What it would actually take: The system requires training custom multimodal neural networks that fuse continuous audio waveforms and video frames to detect sentiment, gaze, and interruptions in real time. The runtime relies on low-latency WebRTC media servers (e.g., LiveKit or custom C++ pipelines) streaming directly to GPU clusters running TensorRT-optimized models. It demands a specialized research team in computer vision, audio processing, and low-latency infrastructure.

Discussion

14 comments analyzed.

Concerns raised: Bias in emotion detection (e.g., loud speech labeled as 'shrill', poor camera angle misinterpreted as lack of eye contact), Computational cost at scale (30-minute interviews, 7-hour depositions), Risk of over-reliance on AI for subjective judgments in hiring/justice systems, Potential misuse in low-quality, insecure surveillance-adjacent products, Poor integration into workflows could create inequitable outcomes

Feature requests: Ability to adapt to individual speech patterns and camera positioning within single conversation, Sub-80ms latency for real-time results

Competitors

Other products that read as similar to this one — 311 launches clear the similarity bar, closest 8 shown.

Attention rank: #57 of 312 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 103 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a agent / copilot tool for Agriculture yet.