AIVA Chat,
Launch voice and chat AI assistants in minutes
AIVA Chat — AI Case Study
A platform to instantly create and deploy voice chatbots and embeddable chat widgets, with full call recordings, detailed logs, and analytics to automate customer communication.
Three numbers that
tell the story upfront.
AI agents deployed
A platform to instantly create and deploy voice chatbots and embeddable chat widgets, with full call recordings, detaile…
Call minutes processed
A platform to instantly create and deploy voice chatbots and embeddable chat widgets, with full call recordings, detaile…
Call connection reliability
A platform to instantly create and deploy voice chatbots and embeddable chat widgets, with full call recordings, detaile…
Before we got here,
here's what wasn't working.
AIVA Chat makes it effortless to deploy AI-powered voice and chat assistants. Create a voice chatbot by entering a number and a prompt, embed a chat widget on any website with minimal setup, and enable voice call assistants complete with recordings and detailed interaction logs. Built-in analytics track performance and user engagement, while full automation handles both voice and chat to streamline customer communication.
We confirmed every challenge with the team before writing a line of production code. The right column lists the pain points we tackled head-on and how we approached each one.
What wasn't working
BEFOREReal-time audio streaming latency between Twilio and OpenAI
Optimised the audio pipeline by streaming MP3 chunks directly to OpenAI's Whisper API in parallel with TTS, reducing perceived latency to under 800ms.
Don't over-engineer.
Solve what matters, ship fast.
The instinct on complex projects is to rebuild everything from scratch. We pushed back. A full rewrite often means months of regressions while engineering rebuilds features that were already working.
We mapped the existing workflow against actual usage. The result was clear: a small set of features accounted for the majority of user time. We rebuilt the core, shipped fast, and extended outward.
On the technical side, we made clear decisions early and held them. The right column shows what we chose, and why.
4 decisions we made early
CHOSEN / REJECTEDInstant Voice Chatbot Creation — how we built it
Custom Instant Voice Chatbot Creation — built for this product's exact needs and data model.
Generic SaaS tool that would require heavy workarounds and compromise the user experience.
Embeddable Chat Widget — how we built it
Custom Embeddable Chat Widget — built for this product's exact needs and data model.
Generic SaaS tool that would require heavy workarounds and compromise the user experience.
Voice Call Assistant with Recordings — how we built it
Custom Voice Call Assistant with Recordings — built for this product's exact needs and data model.
Generic SaaS tool that would require heavy workarounds and compromise the user experience.
Analytics & Engagement Tracking — how we built it
Custom Analytics & Engagement Tracking — built for this product's exact needs and data model.
Generic SaaS tool that would require heavy workarounds and compromise the user experience.
4 features that
define the product experience.
Every feature was scoped and shipped with the same priorities: speed, reliability, and staying out of the user's way.
Instant Voice Chatbot Creation
Create voice chatbots instantly by entering a phone number and providing a prompt — no technical setup required.
Embeddable Chat Widget
Embed a chat widget on any website with minimal setup — just add basic site details and generate the script.
Voice Call Assistant with Recordings
Enable voice call assistants with full call recordings and detailed interaction logs for every conversation.
Analytics & Engagement Tracking
Access analytics to track performance, user interactions, and engagement metrics across all voice and chat channels.
What changed,
measured against the day-one baseline.
We benchmark before we touch anything, logging every metric below against its state before we wrote a line of production code.
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