It mishears
A typical bot
Trained on one accent, and asks the caller to repeat themselves.
With DwaniAI
Speech tuned for regional accents, in the language the caller chose.
Voice and chat · one platform
Answers from your systems, not from a model's memory.
DwaniAI runs voice and chat agents across phone, web and messaging, in the languages your customers use. When it cannot answer, it hands over to a person with the transcript, the intent and the customer record already open.
What is in the box
Voice agentsInbound and outbound calls, IVR replacement, live transfer
Chat agentsWebsite, app and messaging channels from the same flows
Context handoffTranscript, detected intent and customer record passed to the agent
Conversation analyticsWhat was asked, what was answered, and what escalated
- Phone, web and messaging on one platform
- Speech tuned for regional accents
- Connects to your CRM and ticketing
What it does
Most people have already met a bad chatbot
Which is the real obstacle. Your customers arrive expecting a loop and a dead end, so the useful question is not what the agent can do well, but what it does when it cannot.
| The moment | A typical bot | With DwaniAI |
|---|---|---|
| It mishears | Trained on one accent, and asks the caller to repeat themselves. | Speech tuned for regional accents, in the language the caller chose. |
| It does not know | Guesses from the model, or loops back to the same menu. | Escalates, rather than answering something it cannot verify. |
| The handover | The agent picks up cold and the customer starts again. | Transcript, intent and customer record are open before the agent speaks. |
| The answer itself | Comes from training data, and may not match your systems. | Comes from your systems through an API call, at the moment of asking. |
| Six months later | A fixed script that nobody has reviewed since launch. | Intent coverage reviewed against real transcripts and adjusted. |
It does not know
A typical bot
Guesses from the model, or loops back to the same menu.
With DwaniAI
Escalates, rather than answering something it cannot verify.
The handover
A typical bot
The agent picks up cold and the customer starts again.
With DwaniAI
Transcript, intent and customer record are open before the agent speaks.
The answer itself
A typical bot
Comes from training data, and may not match your systems.
With DwaniAI
Comes from your systems through an API call, at the moment of asking.
Six months later
A typical bot
A fixed script that nobody has reviewed since launch.
With DwaniAI
Intent coverage reviewed against real transcripts and adjusted.
How it fits together
Between the customer and the systems that hold the answer
Channels come in, we work out the intent, fetch the answer from your own systems, and send anything unresolved to a person with the context attached.
Scroll the diagram sideways →
Capabilities
Three things it has to get right
Everything else follows from these. If the agent cannot hear the caller, cannot answer in their language, or cannot hand over cleanly, nothing else about it matters.
Every channel
One set of flows, wherever they reach you
Voice and chat agents run from the same intent flows and business rules, so the answer a customer gets on the phone matches the one they get on the website.
- VoiceContact centre, inbound and outbound, with live transfer
- DigitalWebsite, in-app and messaging channels
- ConsistencyFlows and rules configured once, not per channel
Every caller
The language they actually speak
Speech recognition and language understanding are tuned for regional accents, so the agent does not spend the first thirty seconds asking someone to repeat themselves.
- RecognitionSpeech models tuned for regional accents
- UnderstandingIntent detection per language, not translated from one
- ImprovementCoverage reviewed against real transcripts
Every escalation
The handover is the product
Automation is judged on its worst moment, not its best. When the agent hands over, the person taking the call already has the conversation, the detected intent and the customer record in front of them.
- TranscriptThe full conversation, not a summary line
- IntentWhat the agent understood the customer to want
- RecordThe customer pulled from CRM before the agent speaks
Demos
See it in action
Short recordings of DwaniAI voice agents in real scenarios — lead intake, appointment booking, and airline support.
Handling leads
Sales and enquiry intake
Doctor appointment
Healthcare scheduling
Airlines voicebot
Flight and booking support
Guardrails
What it will not do
We state this plainly, because this is where conversational AI projects go wrong and where your risk function will start.
Not inventedIt does not answer from memory
Balances, order status, policy details and account facts are fetched from your systems at the moment of asking. If the call fails, the agent says so rather than approximating.
Not guessedIt escalates instead of improvising
Outside the intents it has been configured for, the agent hands over. Containment rate is a reporting metric, not a target that overrides the customer.
Not hiddenEvery conversation is reviewable
Transcripts, detected intent and the escalation reason are retained for review, so coverage gaps are found by you rather than reported by a customer.
Under the hood
What it is built on
Standard integration surfaces, so connecting it is a normal piece of work rather than a bespoke project.
Speech & language
Gen AISpeech-to-textNLP & intent
Channels
Voice / telephonyWeb chatMessaging
Integration
REST APIsWebhooksCRM & ticketing
Who uses it
Four teams, one platform
It is deployed across insurance, banking, telecom, healthcare and retail, where the volume of routine questions is high and the cost of a wrong answer is higher still.
Customer experience
Consistent answers on every channel, and a handover that does not make people repeat themselves.
Flows & journeysContact centre operations
Routine volume handled without adding headcount, and agents receiving cases with context attached.
AnalyticsProduct & innovation
New journeys tested as configuration, then measured against real transcripts rather than opinion.
Intent designIT & security
Standard integration surfaces, reviewable transcripts, and a clear answer on where data goes.
IntegrationQuestions
Answers before you ask
Anything specific to a customer is fetched from your systems through an API call while the conversation is happening. The language model handles understanding and phrasing, not the facts. That distinction is the whole design, and it is the one worth testing in the demo.
Get started
Request a demo
Tell us where your call and chat volume goes today, and we will schedule a short walkthrough of DwaniAI.
- A live conversation on voice and on chat
- What a handover looks like from the agent's side
- A sequenced plan with effort ranges, written for your team