Turning continuous speech into stable context, natural interaction, and actionable workflows.
Zhen Zhang — Former Tencent senior engineer
Raising $1M Pre-Seed SAFE
reception.lansonai.com · Call 949-627-8989
Modern speech models can recognize words quickly, but continuous speech still breaks when people or AI systems need to follow it in real time.
Partial output keeps changing and breaks attention.
Names, terminology, intent and references depend on conversation-level context — not isolated tokens.
Pauses, interruptions, corrections and turn-taking are not isolated transcription events.
The missing layer sits between speech / foundation models and the final application. It has four jobs:
Across an evolving conversation.
Before people or software depend on it.
Across turns, pauses and interruptions.
For display, translation, reasoning and action.
Reception · Live · Flow · Podcast · future APIs / SDKs
context · correction · streaming stability · interruption · conversational state · structured output
ASR · TTS · language / reasoning models
Longer context windows and stronger reasoning make semantic repair, terminology inference and conversation-level understanding practical.
Agents, customer conversations, meetings, hands-free computing and multimodal systems increasingly depend on continuous speech — not isolated voice commands.
Inference cost, latency and streaming orchestration now allow contextual processing to happen inside the interaction loop.
These are not four equal GTM bets. They are four product surfaces proving that one context layer travels across the voice lifecycle. Current commercial focus: Reception.
Three core capabilities form the system. Each is implemented and referenced by internal proof.
VAD is a signal, not the endpoint decision. Combines physical silence, semantic completeness and time pressure to distinguish continuation, pause and interruption.
Real-time correction, terminology / homophone refinement, context propagation and state coordination as meaning unfolds.
Controls incremental output stability and reduces unnecessary reflow / re-anchoring for readable, dependable live output.
Typical model-processing latency
P50 multi-client end-to-end latency
Concurrent live on single L4 test config
Internal long-form processing speed
The consumer launch validated the technology. It also revealed where the real urgency and monetization potential live — business voice workflows.
The live speech pipeline, stable streaming and contextual correction work in a real product.
Usage showed monetization and urgency are higher in business voice workflows than general consumer transcription.
Missed or poorly handled calls have direct revenue cost. Scheduling, follow-up and workflow automation create a clear path from voice to action.
Average inbound miss rate (CallRail)
After an unanswered call (CallRail, 2025)
reception.lansonai.com · Call 949-627-8989
Hotels and front desk operations
Businesses where scheduling is core to operations
Environments where language and reliability matter
Start with workflows where a live conversation already carries measurable operational value, then expand through the same Voice Context Layer into adjacent customer-experience and communication surfaces.
Prove repeatable revenue in one narrow vertical
Expand through the same Voice Context Layer into related customer-experience surfaces
Open the context layer to developers and partners
Real-time speech pipeline, contextual correction, streaming stability, translation, interruption handling and conversational state are implemented.
Four product surfaces already run on the same underlying foundation across live, interactive, input and long-form speech workflows.
Reception outbound is underway; the product is moving toward direct paid business deployments. A repeatable revenue engine is not yet proven.
This round is designed to convert unusually high product and technical completion into repeatable demand.
Strength: telephony, agent orchestration, integrations and vertical workflows.
Strength: recognition accuracy, raw latency, language coverage and API reliability.
Strength: recording, transcription, captions and post-meeting intelligence.
Context and state layer, not telephony or agent orchestration
Semantic understanding and interaction, not raw recognition
Real-time interaction, not post-session intelligence
Hosted API / SDK for stable live context, correction, interruption signals and conversational state.
Strategic platform / IP licensing where the same real-time information problems appear outside the initial voice products.
Former Tencent senior engineer · 10 years of full-stack and large-scale systems experience
Product, infrastructure, interaction design and early GTM across multiple voice workflows
Web and native product experiences across live, interactive, input and long-form speech workflows
Built the real-time speech, correction, streaming and state systems underneath the product suite
Reception demonstrates that major product capabilities can be assembled quickly because the underlying systems already exist
Full-stack and large-scale systems experience
Built across the voice lifecycle
Powers all product surfaces
Models can change while selected real-time assembly, state and presentation work executes client-side, reducing round trips and centralized infrastructure pressure.
VAD is a signal, not the endpoint decision. LansonAI combines silence, semantic completeness and interaction state to distinguish continuation, pause and interruption.
The same foundation already travels across the voice lifecycle.
New applications reuse production infrastructure instead of restarting from zero.
Target runway: 18–24 months
Convert outbound into pilots and paid deployments.
Establish a narrow ICP and measurable sales motion.
Improve reliability, observability and deployment for real business usage.
Add only the hires or partnerships that directly accelerate commercial proof.
reception.lansonai.com · 949-627-8989 · zhen@lansonai.com
LansonAI