Voaiz — Our AI Voice Automation Engine, Deployed at Enterprise Scale in the UAE - Richinnovations 11th Anniversary Marketing & Tech Ecosystem

Case study

Voaiz — Our AI Voice Automation Engine, Deployed at Enterprise Scale in the UAE

Building a product is one thing; proving it in a demanding market is another. This case study covers the deployment of Voaiz for an enterprise client in the United Arab Emirates, an environment that tests a voice platform harder than most.

Project overview

About the project.

Voaiz is our own product. We launched it on 6 January 2025, alongside Richinnovations’ tenth anniversary, as a next-generation AI voice automation engine built to handle the conversations businesses have at volume — cold calls, support calls, reminders, grievance handling and follow-ups — with intelligent, natural dialogue rather than the rigid menu trees customers have learned to dread. Building a product is one thing; proving it in a demanding market is another. This case study covers the deployment of Voaiz for an enterprise client in the United Arab Emirates, an environment that tests a voice platform harder than most. The UAE is one of the most linguistically diverse commercial markets in the world, where a single customer call may move between Arabic, English and a South Asian language in the space of a sentence. The client’s existing operation depended on a human call team that could not scale with demand, and the implementation timeline was tight. Because Voaiz already existed as a mature product rather than a bespoke build, we were able to move directly to configuration, language tuning and integration — deploying inside the client’s timeline, at the response speeds a live conversation demands, and with a workforce transition managed carefully enough that the client’s people ended up doing more valuable work rather than less.

Technical specifications

Under the hood.

SPEECH RECOGNITION Real-time streaming speech-to-text with support for multiple languages, regional accents and mid-conversation language switching, tuned for telephony-grade audio and noisy environments

CONVERSATIONAL INTELLIGENCE LLM-driven natural dialogue with context retention across the call, intent detection, entity capture, and configurable business rules governing when the assistant acts, confirms or escalates

VOICE SYNTHESIS Natural text-to-speech output with brand-appropriate voice selection per language, tuned for conversational pacing rather than announcement-style delivery

REAL-TIME ORCHESTRATION Low-latency pipeline coordinating recognition, understanding and synthesis, with barge-in handling so callers can interrupt naturally, plus silence, retry and fallback management

The Challenges

What wasn't working.

1. Shifting from a human call team to an automated one. The client’s operation ran on people. Introducing voice automation raised the question every such project raises, and it is a human question before it is a technical one: what happens to the team? Handled badly, automation projects generate internal resistance that quietly undermines adoption regardless of how well the technology performs.

2. A genuinely multilingual customer base. The UAE is among the most linguistically diverse commercial markets anywhere. Callers speak Arabic, English and a range of South Asian languages, often switching between them mid-sentence, and across a wide spread of accents. A system that handled one language cleanly and stumbled on the rest would have been unusable in practice.

3. Response latency in live conversation. Human conversation runs on very short gaps. A delay that would be unnoticeable in a chat interface makes a voice assistant feel broken, and callers begin talking over it or hang up. Getting recognition, reasoning and speech generation to complete inside a natural conversational pause was the hardest technical constraint in the project.

4. A compressed implementation timeline. The client needed the system live inside a window that would not have accommodated building a voice platform from scratch — while still requiring proper configuration, tuning and testing rather than a rushed deployment.

5. Customer tolerance for automated calls. Audiences have been trained by years of poor IVR systems to disengage the moment they detect automation. Anything robotic, repetitive or unable to handle an unexpected reply would have damaged the client’s customer relationships rather than improving them.

6. Fitting into an existing operational stack. The assistant could not sit in isolation. It needed live access to customer records during calls and had to write outcomes back into the client’s systems, so that automated and human-handled interactions produced one consistent operational record.

The Solution

How we fixed it.

1. We deployed a product, not a project. Because Voaiz was already a mature engine, the engagement began at configuration rather than construction. That single fact is what made the timeline achievable — the client received a tested platform tuned to their operation, instead of waiting through a ground-up build and absorbing its risk.

2. We designed the workforce transition as augmentation, not replacement. Voaiz was introduced to absorb the high-volume, repetitive call load — reminders, routine queries, follow-ups — freeing the client’s team to concentrate on complex, sensitive and high-value conversations where human judgement genuinely matters. Framing the shift around redeployment rather than reduction turned the client’s staff into participants in the rollout instead of obstacles to it.

3. We built for real multilingual conversation, not language menus. Rather than forcing callers to select a language and stay in it, the system was tuned to recognise and respond across the languages and accents the client’s customers actually use, including switching within a call. Voice output was configured per language so the assistant sounds natural in each rather than translated into it.

4. We engineered the pipeline for conversational latency. Streaming recognition, tight orchestration between components and natural interruption handling were used to keep responses inside the rhythm of live conversation. Callers can cut in, change direction or answer early without the assistant losing its place.

5. We made escalation a designed feature rather than a failure state. When confidence drops or a caller asks for a person, the call transfers to a human agent with the full conversation context attached. The customer never repeats themselves, and the client retains a clean safety net beneath every automated interaction.

6. We integrated with the client’s live systems. The assistant reads real customer data during the call and writes outcomes back automatically, so automated calls produce the same operational record as human-handled ones and no manual reconciliation is required.

7. We delivered on time, and kept tuning after launch. The system went live within the client’s original timeline. Post-deployment, transcripts, outcome data and escalation patterns are used to refine recognition, dialogue and business rules — so performance continues improving with volume rather than fixing at launch quality.

More case studies

Belltel Oman A Bilingual E-Commerce Portal Running on Live SAP Data

keralacsc.com A Membership selfcare Platform for Kerala’s Village Level Entrepreneurs

Sukrutha Keralam A Digital Donation for a Million-Malayali Movement

From Wasted Ad Spend to an AED 100,000 Milestone Naturals Salon, Sharjah

Naturals Ladies Beauty Salon, Sharjah crossed an AED 100,000 milestone, with the client formally recognising our contribution to it. Beyond the number, the engagement changed what marketing means to the business: stronger brand visibility, greater customer trust, an owned organic audience rather than reliance on paid reach alone, and a sales team whose time goes to genuine customers instead of filtering spam.

LetMeGoo — Solving a Universal Parking Problem Without Exposing Anyone’s Privacy

The owner is notified in real time and comes to move. What makes the product work is what it deliberately does not do: at no point does either person see the other's phone number or name unless they choose to reveal it. Users can hide their name, hide their contact number, or operate fully anonymously while still using every feature.

Every Lead Captured, Every Target Visible — A Custom Platform for PCCS

The problem was not demand — it was that almost nothing about the operation was being captured in a form management could act on. Leads arrived through several channels and were written into notebooks, sent as messages or simply remembered. Targets were set for staff but tracked informally, so nobody could say with confidence who was ahead and who was behind until the month had already closed.