A conversational AI development company for agents that finish the task
We build chat and voice agents that understand Arabic, English and Indian languages, work out what the caller wants, and then do it: book the room, raise the ticket, read back the balance. Each agent runs on a LangGraph-style state machine with explicit intents, tool calls into your systems, and a clean handover to a person when confidence drops.
Why most enterprise chatbots answer but do not act
The first generation of chatbots could tell a customer the opening hours and little else. The moment a request needed a record changed, a slot booked or an identity checked, the conversation ended with a phone number. Call centers absorbed the overflow on resets, status checks and reschedules that needed no judgement.
Voice makes this harder. Callers in Dubai switch between Arabic and English mid-sentence. Callers in Chennai expect Tamil. Noise, accents and interruptions defeat a bot tuned on studio audio, and a wrong transcript becomes a wrong action.
Our approach to AI voice agent development
We design each agent as a graph of states, not a prompt. Every intent is named, every slot declared, and every action is a typed tool call with its own permission check. The model handles understanding and phrasing; the graph decides what may happen next. That is the LangGraph-style pattern behind our AI Restaurant Ordering Assistant and A24 Hospitality Intelligence, where guests book rooms, order food and check out in one conversation.
For voice we add streaming speech recognition and synthesis in Arabic, English and Indian languages, telephony for inbound and outbound calls, and a confidence model that decides when to confirm, re-ask or hand to a person. We benchmarked intent classification, LLM against retrieval, before choosing per use case, and we will show you that comparison on your own recordings.
What the solution includes
Six parts, assembled to your channels and systems. Text-only deployments skip the speech layer.
Multilingual speech-to-text and text-to-speech
Streaming recognition and synthesis for Gulf and Modern Standard Arabic, English, and Indian languages including Hindi and Tamil, with code-switching handled inside one call.
Intent graph and conversation state
A LangGraph-style state machine with named intents, declared slots and checkpointed state in PostgreSQL, so a caller who drops and redials resumes where they left off.
Knowledge grounding
Policy and product answers are retrieved from your approved content with citations, using the permission-aware retrieval of our enterprise knowledge solution.
Typed tool calls into CRM, PMS and calendars
Each action is a declared function with input validation and a permission check: create booking, update ticket, read balance, schedule callback. Nothing happens by improvisation.
Telephony, routing and escalation
Inbound IVR replacement and outbound calls over SIP, with warm transfer to a human carrying the transcript, detected intent and collected slots.
Consent, disclosure and recording controls
The agent states that it is an AI, records only with consent where the law requires it, and redacts card numbers and identifiers before storage.
High-value use cases for enterprise conversational AI
The best first deployments are high-volume, well-defined and backed by a system of record. These six usually qualify.
Hotel guest services in Arabic and English
Booking, room service, late checkout and feedback in one conversation across phone, WhatsApp and in-room devices, connected to the property management system.
Restaurant ordering by voice and chat
Menu discovery, upsell, payment and order status over web chat, WhatsApp and multilingual voice, posted into the POS.
Bank and insurer customer service
Balance, card block, claim status and branch appointment intents with identity verification, in markets where SAMA and UAE regulators expect disclosure and recording controls.
Internal HR and IT help desk
Leave balance, payslip questions, password resets and access requests resolved against your HRMS and ticketing tools, with the ticket created and the employee told its number.
Outbound reminders and collections
Appointment confirmations, payment reminders and renewal calls placed by a voice agent that records the outcome in the CRM and books a human follow-up on request.
Field and plant voice access to data
Technicians ask for a maintenance record, part number or safety procedure by voice on a noisy site and get a spoken answer grounded in the maintenance system.
How does a voice agent handle a call from start to finish?
Six stages, a few seconds per turn. The graph controls the order; the model supplies the language.
- 01
Answer, disclose and detect language
The agent answers, states it is an AI, and detects Arabic, English or an Indian language from the first utterance.
- 02
Transcribe and classify intent
Streaming speech-to-text feeds the classifier. Low confidence triggers a clarifying question, not a guess.
- 03
Collect and confirm slots
Dates, names and amounts are gathered, validated against the system of record and read back before anything changes.
- 04
Check policy and permissions
The graph verifies that this caller may perform this action on this record. Denied actions are explained and routed.
- 05
Execute the tool call
The booking, ticket or update is made through the system API and the result is spoken back in the caller's language.
- 06
Close, hand over or follow up
The agent summarizes, offers a human if needed, and writes transcript, intent and outcome to the CRM.
Deployment, telephony and integration
Agents run as gRPC microservices with Redis sessions and WebSocket streaming, in your cloud tenancy or on-premises when recordings must not leave the building. Speech models can be hosted regionally or inside your environment. Telephony connects over SIP to your contact-center platform or carrier trunk, so you keep your numbers and reporting.
Where we deliver: in UAE, agents are built for UAE PDPL consent rules and the Arabic-English mix of Dubai and Abu Dhabi callers; in Saudi Arabia, for SDAIA PDPL, NCA ECC-2:2024 and Gulf Arabic; in India, for the DPDP Act 2023 and callers who expect Hindi or Tamil; in United States, for TCPA consent on outbound calls and HIPAA where health data is spoken. Our Abu Dhabi and Chennai offices run language testing with native speakers.
Benefits and success criteria
We measure on your live calls against a baseline from your current IVR or chat tool. The pilot produces the numbers; this page promises none.
Task completion rate
Share of conversations where the agent fulfilled the intent without a human, by intent and language.
Containment and escalation
How many calls needed a person, and whether the handover carried enough context that the caller did not repeat themselves.
Turn latency
Time from the caller finishing a sentence to the agent starting its reply, measured per turn, because long pauses kill trust in voice.
Satisfaction and cost per contact
Post-call rating and fully loaded cost per resolved contact against the human-handled baseline.
Implementation approach
A first agent covers three to five intents on one channel. Each added intent is a new branch on the same graph.
- 01
Mine your call and chat logs
We cluster real transcripts to find the intents that carry volume, the language mix and the phrases callers use.
- 02
Design the intent graph
Intents, slots, confirmations, permissions and handover rules are drawn as a graph and signed off before any code.
- 03
Benchmark speech and intent models
Candidate recognition and classification approaches are tested on your recordings, per language.
- 04
Connect tools and knowledge
Typed tool calls into CRM, PMS or ticketing are built and tested with synthetic and replayed conversations.
- 05
Pilot on a fraction of traffic
A slice of calls or chats is routed to the agent with instant human fallback. Transcripts are reviewed daily.
- 06
Expand intents and channels
Once completion and latency hold, new intents, languages and channels are added, with regression tests on every change.
Why Constient for conversational AI
We have shipped multi-tenant hospitality agents with Indian-language speech and outbound telephony, and we benchmarked intent-handling approaches rather than picking the fashionable one. Graph-first design is why our agents can be audited: every action is a named transition, not a line in a prompt.
The same engineers deliver our generative and agentic AI services, so a help-desk agent can grow into agentic process automation without a rebuild. For hotels and restaurants, our hospitality practice brings PMS and POS integrations already done.
Frequently asked questions
How does a conversational AI engagement with Constient begin?
With your transcripts. Under NDA we take a sample of call recordings or chat logs, cluster them into intents and report where the volume and language mix sit. You get a proposed first agent of three to five intents, a channel recommendation and a pilot plan. If your volume does not justify an agent yet, we say so.
Which languages and dialects do your voice agents support?
Gulf and Modern Standard Arabic, English, and Indian languages including Hindi and Tamil, with code-switching inside a single call. We test recognition on your own recordings per language with native speakers from our Abu Dhabi and Chennai teams, and report each separately. Other languages are assessed on your audio before we commit.
Can the agent work with our existing contact-center platform and phone numbers?
Yes. The agent connects over SIP to your existing platform or carrier trunk, so numbers, queues and reporting stay where they are. Warm transfers carry the transcript and collected details. For chat, we connect to WhatsApp Business, your website widget and your mobile app through their standard APIs.
Where are recordings and transcripts stored?
Wherever you decide before go-live. The agent runs in your cloud tenancy in-region or on-premises, speech models can be hosted inside your environment, and transcripts are redacted of card numbers and identifiers before storage. We document the design against UAE PDPL, SDAIA PDPL or the DPDP Act 2023 and your sector regulator's recording rules.
How long does a first voice agent take and how is it priced?
A first agent of three to five intents on one channel typically reaches a traffic-slice pilot in 10 to 14 weeks. Discovery and pilot are a fixed fee, then a fixed scope per intent group or a dedicated team. Speech and telephony running costs are passed through and estimated from your call volumes.
What happens when the agent is not confident?
It asks a short clarifying question first. If confidence stays low, or the caller asks for a person, it transfers to a human with the transcript, intent and collected slots attached, so nobody repeats themselves. Every low-confidence turn is logged and reviewed in the pilot to decide whether to add data, tighten a slot or add an intent.
Bring us a hundred calls and we will show you the agent
We will cluster your transcripts, show where an Arabic, English or Indian-language agent would complete the task, and propose a pilot with measures you can hold us to.