Conversational AI for business: how voice agents work and where they pay off

A conversational AI system takes a customer's words, works out what they want, decides on the next step and responds in language the customer understands. Older rule-based bots followed fixed menus and keyword triggers. Current systems use large language models, so they can handle a question phrased in many different ways and keep track of what was said earlier in the conversation.

For a business, the useful test is whether the system can complete a task. Answering a question about opening hours helps a little. Confirming a booking, updating the CRM and sending the customer a reminder takes real work off your team.

Most deployments connect the AI to the tools your staff already use, such as the CRM, the calendar, the order system and the phone line. The AI reads from those systems to personalise the conversation and writes back the outcome so a person can pick up where it left off.

Chat assistants live on websites, in apps and in messaging channels. Customers type at their own pace, can scroll back and can paste order numbers, which makes chat a good fit for account questions and self-service.

Voice AI handles phone calls. Phone is still the channel many customers trust for purchases, appointments and payments, and many leads respond to a call faster than to a message. A voice agent has to listen, think and speak in real time, which makes it the harder engineering problem of the two.

Many teams start with voice for outbound lead calls and inbound overflow, because those are the calls that go unanswered when the team is busy.

Every voice agent does four jobs on each turn of a call. It hears the caller, understands the request, decides what to say and speaks the reply. Platforms differ in how those jobs are wired together.

In a cascaded design, three separate models run one after another. Speech-to-text (STT) turns the caller's audio into a transcript. A large language model (LLM) reads the transcript and writes a text reply. Text-to-speech (TTS) converts that reply into audio. Each step adds processing time and often a network hop, and tone of voice is lost when speech is reduced to text.

In a single audio-to-audio design, one model takes the caller's speech as input and produces spoken output directly. Removing the hand-offs between models shortens the gap before the agent starts talking, and the model can respond to how something was said as well as what was said.

• ▸Turn detection. The agent has to judge when the caller has finished speaking, so it neither cuts them off nor leaves a long silence.
• ▸Interruptions. Callers talk over the agent, and a good agent stops, listens and adjusts its answer.
• ▸Tool calls. During the call the agent checks calendars, looks up dues or creates CRM records, and those lookups also count against response time.

Latency is the time between the caller finishing a sentence and the agent starting its reply. On a phone line, a long pause sounds like a dropped call. Callers repeat themselves, talk over the agent or hang up, and each of those makes the conversation harder to recover.

Measure latency on real phone calls, over the carrier network, with tool calls switched on. Demo recordings made on a laptop with a fast connection often show numbers that a live phone line will not match.

GRX10 Voice AI reaches a first-word response under 700 ms on real phone calls, using a single audio-to-audio model (patent-pending architecture). The Voice AI page covers the architecture in more detail.

The strongest use cases share three traits. The calls are frequent, they follow a recognisable script, and a fast response changes the outcome. The examples below are anonymised results published by GRX10.

• ▸Lead qualification (education). An EdTech team runs outbound tele-sales at 10,000 calls a day with 32% conversion. The agent calls each lead, asks the qualifying questions and passes interested prospects on. See education.
• ▸Site visits (real estate). A Mumbai residential developer cut callback time to 90 seconds from more than 14 hours. Lead-to-site-visit conversion rose 3.1x and cost per booked visit fell 78%. See real estate.
• ▸Appointment booking (healthcare). A healthcare provider brought its no-show rate down from 40% to 12%. See healthcare.
• ▸Collections (BFSI). Voice agents secured promise-to-pay on 41% of overdue accounts for a BFSI collections team. See AI collections voice agents.
• ▸Customer support (FinTech). A FinTech service desk reports 85% first-call resolution and 92% CSAT.
• ▸COD verification (D2C ecommerce). A D2C brand calls cash-on-delivery buyers to confirm orders and recovered 18% of carts. See ecommerce.
• ▸Solar consultations. A rooftop solar company achieved 4.2x site-visit conversion. See solar.

Run a pilot on one call type with your own scripts and your own customers before you sign a long contract. Score each vendor on the criteria below, using recorded calls as evidence.

• ▸Languages. Check every language your customers speak, including code-switching such as Hinglish. Ask whether the agent detects the caller's language and switches mid-call, and whether other languages cost extra.
• ▸Latency on live calls. Time the first-word response on real phone calls with CRM lookups running.
• ▸Human handoff. The agent should transfer to a person with the conversation summary attached, so the customer does not repeat themselves.
• ▸Logging and CRM write-back. Every call should be recorded, transcribed, summarised and written to your CRM with a disposition your team can report on.
• ▸Compliance. In India, check DPDP Act 2023 readiness and that outbound calls follow TRAI commercial-calling hours (9 am to 9 pm) and DND rules. In the US, review TCPA requirements for each outbound use case.
• ▸Pricing clarity. Ask for the per-minute platform rate and telephony as separate lines, and model the cost of a typical call length.

GRX10 Voice AI runs inbound and outbound voice agents in 30+ languages, including 22+ Indian languages with Hinglish code-switching. The agent detects the caller's language and switches mid-call. Every call is recorded, transcribed, summarised and written to the CRM with a disposition, and the agent can warm-transfer to a human with full context.

Your team builds an agent in the agent studio by describing its job in one sentence. GRX10 generates the prompt, greeting, guardrails and voice, which your team then reviews and adjusts.

Platform pricing is $0.119 per minute (₹7.99 per minute in India), with telephony billed separately at carrier cost and no language surcharge. A typical two-minute call costs about ₹16 or $0.24 in platform fees. Details are on the pricing page.

Teams that want the calling handled for them can use VoiceOps, where GRX10 runs AI and human calling operations from its own call floor in Bengaluru. To see an agent on your own call type, book a demo through the contact page.