Étude de cas
When we first launched Sadie, speed mattered more than anything. We used the same third-party voice AI orchestration layer that much of the industry relies on. It helped us move fast, validate demand, and get real customers using the product.
In 2025 alone, Sadie handled over 1.5 million real customer calls, spanning nearly 1.8 million minutes of live conversation, across restaurants operating in multiple countries and time zones.
That kind of usage changes the problem entirely.
➜ Latency we could not fully control.
➜ Conversation quality that degraded under load.
➜ Limited visibility into failures and performance.
➜ And too much of the experience living outside our control.
When you are answering hundreds of thousands of calls outside staffed hours and directly driving tens of millions of dollars in restaurant revenue, those tradeoffs stop being acceptable.
If we wanted Sadie to be reliable at scale, we needed to rethink the foundation.
The problem with most voice AI stacks
On the surface, most voice AI products look similar. Under the hood, many are built on the same pattern, third-party orchestration layers stitched together to handle listening, transcription, reasoning, and response.
This approach works for demos and early traction. It does not hold up when usage spikes, when latency matters, or when the system becomes part of core operations.
When something slows down, breaks, or behaves unexpectedly, you are often blind. When you want to improve conversation quality, you are constrained by someone else’s pipeline. And when call volume surges, performance becomes unpredictable.
That is the reality of glued-together systems.
We felt this acutely as Sadie scaled from tens of thousands of monthly calls to over 18× growth within a single year. At that scale, orchestration is not an implementation detail; it is the product.
Introducing the Sadie AI Engine
The Sadie AI Engine is Sadie’s fully in-house voice AI foundation.
Instead of relying on a generic third-party orchestration layer, we now control the entire call pipeline end to end, from the moment a caller starts speaking, to how the AI detects pauses, chooses a language, formats speech, and responds.
This shift is not cosmetic.
It is what allows Sadie to operate reliably while handling over one million real customer conversations in production, including nearly 280,000 After Hours calls where there is no staff safety net.
What this unlocks in the real world
Faster, more natural conversations
The Sadie AI Engine reduces end-to-end latency by a full second.
That difference is immediately noticeable when you consider that the average Sadie call lasts just over one minute. Saving a second per interaction meaningfully changes how conversations feel.
The AI understands when callers have stopped speaking, handles interruptions mid-sentence, and responds with better timing. Speech is formatted more naturally, including numbers, dates, addresses, and emails, across multiple languages.
The result is simple.
Conversations feel less robotic and more human, even at peak demand.
Enterprise-grade reliability
We rebuilt the system with reliability as a first-class requirement.
The Sadie AI Engine uses emergency-grade audio streaming technology trusted for critical use cases. If a primary model slows down or becomes unavailable, the system automatically falls back. If anything goes wrong during a call, the AI recovers or transfers smoothly so conversations do not just drop or dead-end.
This matters when Sadie is responsible for capturing nearly one quarter of total restaurant revenue after hours, and when a missed call is a missed booking.
In 2025, Sadie directly helped drive $34.8M in restaurant revenue, including $8.5M generated entirely outside staffed hours.
That level of responsibility demands a system built for failure recovery, not just happy paths.
Full control to move faster
By bringing the entire stack in-house, we are no longer limited by external vendors.
We can ship improvements faster, diagnose issues immediately, and respond directly to real customer feedback. Features like improved warm transfers, better multilingual support, and major latency reductions are only possible because we own the orchestration layer.
This control has real downstream impact.
Sadie now consistently handles group reservations, with the majority of bookings made for parties of two or more, driving higher-value outcomes for restaurants without increasing staff workload.
Control is not just about power. It is about accountability.
Already live, already proven
The migration required a single click, with the ability to roll back instantly if needed. We rolled it out in phases, starting with early venues and scaling to full adoption across integrations.
Today, the Sadie AI Engine is live, stable, and running in production, handling over a million customer conversations per year, saving restaurants nearly 2 million staff hours annually, and operating across regions including the United States, Australia, Canada, and the UK.
This is not a lab environment.
It is real-world usage, at real scale.
A foundation shift, not a feature release
This is a foundational shift in how Sadie is built and how it will evolve.
If you rely on voice AI for real customer interactions, the difference between stitched-together systems and true orchestration becomes obvious very quickly.
✓ You hear it in the speed.
✓ You feel it in the flow of conversation.
✓ And you trust it when things get busy.
This is the foundation we are building Sadie on.
If you want to see what this feels like in practice, we are always happy to talk.
