Sub-second Latency: How We Architected Voice AI for African Networks

In voice AI, latency isn’t just a performance metric; it’s the product.

A delay of even one second can turn a natural conversation into an awkward, robotic experience. For AI voice agents, especially those powered by LLMs, anything above sub-second latency breaks the illusion of intelligence.

Now add African telecom networks into the mix:

• Variable network quality
• Carrier fragmentation
• Long routing paths
• Inconsistent infrastructure across regions

Achieving sub-second latency in this environment isn’t trivial.

This post breaks down how we architected voice AI for African networks, the challenges we faced, and the design decisions that made real-time conversations possible.

Why Latency Is Harder in African Networks

Most voice AI platforms are optimized for stable, centralized telecom infrastructure

African networks operate differently.

Key challenges:

• Calls often traverse multiple carrier hops
• Audio routing paths are longer and less predictable
• Infrastructure quality varies significantly by country

Traditional CPaaS platforms assume:

“Latency is acceptable as long as the call connects.”

That assumption does not work for AI voice agents.

What “Sub-second Latency” Actually Means for Voice AI

For clarity, sub-second latency isn’t about one thing, it’s the sum of multiple delays:

1. Audio capture
2. Network transport
3. Speech-to-text
4. LLM reasoning
5. Text-to-speech
6. Audio playback

If any one of these steps is slow, the entire experience breaks.

Our goal was simple:

The AI should respond as fast as a human would on a phone call.

That meant rethinking the entire voice pipeline.

Architectural Principle #1: Minimize Network Hops

Every extra hop adds latency.

Instead of routing calls through distant regions, we designed the system to:

• Terminate calls as close to the user as possible
• Reduce unnecessary carrier handoffs
• Keep audio paths short and predictable

This alone removed hundreds of milliseconds from typical call flows.

Architectural Principle #2: WebRTC First, SIP Where Necessary

SIP is reliable, but it wasn’t designed for real-time AI conversations.

WebRTC, on the other hand:

• Supports low-latency audio streaming
• Handles jitter and packet loss better
• Is ideal for real-time voice interactions

Our approach:

• Use WebRTC internally for AI voice processing
• Convert to SIP only at the network edge when interacting with traditional phone networks

This keeps AI conversations fast while remaining compatible with local telecom infrastructure.

Architectural Principle #3: Streaming Everything (No Blocking)

Blocking operations kills real-time voice.

We avoided:

• Waiting for full transcriptions
• Waiting for full LLM responses
• Waiting for complete audio buffers

Instead:

• Audio streams in real time
• Transcription happens incrementally
• LLMs respond token-by-token
• Speech synthesis begins before the full response is generated

This pipeline design is what makes sub-second responses possible.

Architectural Principle #4: Event-Driven Call Control

Traditional voice systems rely on rigid call states.

We moved to an event-driven model, where:

• call.started fires instantly
• transcription events stream continuously
• AI decisions happen mid-call, not after
• call.ended triggers cleanup immediately

This gives AI agents the ability to react, not just respond.

Architectural Principle #5: Design for Network Variability

African networks aren’t uniform, and pretending they are is a mistake.

We designed for:

• Packet loss
• Temporary drops in quality
• Bandwidth fluctuations

Instead of breaking, the system adapts:

• Graceful degradation
• Intelligent buffering
• Dynamic audio quality adjustments

The result: conversations that remain usable even on imperfect networks.

Where KrosAI Fits In

This architecture is the foundation of KrosAI.

KrosAI handles:

• WebRTC ↔ SIP conversion
• Low-latency audio routing
• Real-time transcription and call events
• Local phone numbers in African markets
• AI-native voice infrastructure designed for production use

Developers don’t have to think about telecom complexity; they focus on building great AI voice experiences.

It is important to note that voice AI is only as good as its ability to feel human.

In emerging markets where voice remains the most important communication channel getting latency right isn’t optional.

It’s foundational.

By designing for African networks from day one, we proved that real-time, intelligent voice AI is possible anywhere, not just in the world’s most privileged infrastructure environments.