High volumes of repetitive enquiries
Support teams answer the same questions many times a day.
04 / Hybrid Intelligence
Conversational AI and Virtual Assistants
Customers and employees increasingly expect to get answers and complete simple tasks through conversation, on the web, in messaging apps or by voice. A good assistant resolves what it can, knows what it cannot and hands over to a person gracefully. We design assistants that work that way.
At a glance
- Service
- Conversational AI and Virtual Assistants
- Pillar
- Hybrid Intelligence
- Typical starting point
- Conversation and channel analysis
- Typical first phase
- 8 to 12 weeks for a first production assistant
- Key technologies
- Natural Language Processing, Large Language Models, Generative AI
Durations depend on scope, data readiness and integration needs.
What is Conversational AI?
Short answer
Conversational AI is technology that allows people to interact with software through natural language, in text or speech. Virtual assistants built with conversational AI understand requests, answer questions from approved knowledge, carry out tasks through connected systems and transfer conversations to human agents when needed.
Earlier chatbots relied on rigid decision trees and frustrated users who phrased things unexpectedly. Language models have changed what assistants can understand and how naturally they respond. They have also introduced new risks: confident wrong answers, off-topic conversations and manipulation attempts.
Our assistants combine language model understanding with structure where it matters. Questions are answered from approved knowledge with guardrails. Transactions such as checking an order, booking an appointment or raising a request follow defined flows through secure integrations. Conversations that need a person are handed over with full context, so customers do not have to repeat themselves.
Business problems
What problems does Conversational AI solve?
These are the situations where organisations most often find this service worthwhile.
Limited service hours
Customers need help outside business hours or across time zones.
Frustrating legacy chatbots
Existing bots misunderstand users and cannot complete tasks.
Internal helpdesk overload
IT and HR teams spend time on routine employee queries.
Multilingual audiences
Customers prefer to communicate in regional languages.
Inconsistent answers across channels
Web, phone and messaging give different information.
Use cases
Where can Conversational AI be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| Customer service | Order status, account queries, returns and troubleshooting with agent handover. |
| WhatsApp and messaging | Enquiries, appointment booking and notifications through business messaging. |
| Employee helpdesks | IT support, HR policy questions and internal service requests. |
| Education | Admissions enquiries, student services and course information assistants. |
| Healthcare administration | Appointment scheduling, preparation instructions and general information, not diagnosis. |
| Voice | Speech-enabled assistants for call centres and phone-based self-service. |
Who should consider it
- Organisations with high enquiry volumes
- Businesses serving customers on WhatsApp and web chat
- Enterprises with busy internal helpdesks
- Institutions serving multilingual audiences
How AARHIT approaches it
How we deliver Conversational AI
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Conversation analysis
We analyse existing enquiries, tickets and call transcripts to find common intents, tasks and failure points.
Output: intent and task inventory with volumes.
Stage 2 of 6
Assistant scope and design
We define what the assistant will handle, its tone, escalation rules and conversation flows for transactions.
Output: assistant design and conversation guidelines.
Stage 3 of 6
Knowledge and integration
Approved knowledge is prepared, and integrations with CRM, ticketing, booking or order systems are built.
Output: knowledge base and system connections.
Stage 4 of 6
Build and guardrails
The assistant is built with language understanding, retrieval, transaction flows, safety rules and handover.
Output: working assistant in test channels.
Stage 5 of 6
Testing
We test with real enquiry samples, adversarial inputs, multilingual cases and handover scenarios.
Output: test results and refinements.
Stage 6 of 6
Launch and optimisation
Staged launch with conversation analytics, containment and satisfaction tracking, and regular improvements.
Output: live assistant and improvement reports.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| Language | Large language models, intent classification, entity extraction |
| Platforms | Custom assistants, Rasa, Microsoft Copilot Studio, Dialogflow, Amazon Lex |
| Channels | Web chat, WhatsApp Business Platform, Microsoft Teams, mobile apps, telephony |
| Speech | Speech-to-text and text-to-speech including Indian language support |
| Integration | CRM, helpdesk, booking and order management APIs; live agent platforms |
| Analytics | Conversation analytics, containment, escalation and satisfaction metrics |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- Routine enquiries resolved without waiting
- Support teams focused on complex cases
- Consistent answers across channels
- Service available around the clock
- Insight into what customers and employees ask for
How long does implementation generally take?
Typical timeline
A first production assistant covering priority intents and one or two channels typically takes 8 to 12 weeks. Adding languages, channels or transactions is then planned in further releases.
What determines complexity?
- Number of intents and transactions
- Systems to integrate
- Channels and languages
- Voice versus text
- Regulatory constraints on advice
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Make human help easy to reach
Users who cannot reach a person when they need one lose trust quickly.
Define what the assistant must not do
Clear boundaries, such as not giving medical or financial advice, protect users and the organisation.
Test in real language
Real users write with typos, mixed languages and incomplete sentences. Test with real samples.
Follow messaging platform rules
WhatsApp and similar platforms have policies on templates, consent and message types.
| Risk to consider | How we manage it |
|---|---|
| Incorrect or harmful answers | Grounding in approved content, topic limits, guardrails and conversation review. |
| User frustration | Clear capability statements, fast escalation and handover with context. |
| Privacy breaches | Authentication for account data, data minimisation and secure integrations. |
| Manipulation attempts | Input filtering, restricted actions and adversarial testing. |
FAQ
Questions about Conversational AI
What is the difference between a chatbot and a conversational AI assistant?
Traditional chatbots follow scripted menus and keywords. Conversational AI assistants understand natural language, handle varied phrasing, answer from knowledge sources and can complete tasks through integrations, while still following defined rules.
Can the assistant work on WhatsApp?
Yes. We build assistants on the WhatsApp Business Platform, following its policies on consent, templates and messaging, alongside web chat and other channels.
Does the assistant support Indian languages?
Yes, with appropriate model and speech technology selection. Hindi and many regional languages can be supported, and we test quality with real user phrasing in each language.
What happens when the assistant cannot help?
It transfers the conversation to a human agent or raises a ticket, passing the conversation history and collected details so the user does not need to repeat anything.
How do you measure assistant performance?
Key measures include resolution without handover, correct handover rate, answer accuracy on sampled conversations, user satisfaction and time to resolution.
Next step
Considering Conversational AI for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.