AI-led Services
AI chatbots that work in production, not just demos.
Most AI chatbots look impressive in a demo and disappoint in production. They hallucinate product details. They give confident wrong answers about pricing. They fail when the user asks something outside the scripted flow and say "I did not understand your query" to a customer who is about to churn. The chatbots we build are trained on your actual product documentation, tested against real customer queries, deployed with a fallback to a human agent, and monitored for accuracy after launch.
The chatbot problems we are built to avoid.
Generic chatbot builders produce generic results. Here is what goes wrong when the chatbot is not built correctly for production.
The chatbot hallucinates product details and customers notice.
An AI chatbot trained on general web knowledge will confidently answer product questions with incorrect information. A customer asks about a specific pricing plan and the bot describes a plan that does not exist. The customer either complains or, worse, makes a purchase decision based on wrong information. Production chatbots need to be trained exclusively on your own documentation, with hallucination prevention built in.
The bot has no fallback when it cannot answer.
The customer types a question the bot does not recognise. The bot says "I am sorry, I could not understand your query" and loops. The customer is stuck. There is no human to escalate to, no way to leave a message, and no path forward. The customer closes the chat and contacts a competitor. A production chatbot needs a clear, tested fallback to a human agent for every failure mode.
WhatsApp bots are built on unofficial tools and get banned.
Several third-party tools offer WhatsApp automation by accessing WhatsApp through unofficial means. Accounts built on these tools are banned by WhatsApp regularly. The WhatsApp Business API is the only compliant route for production chatbots. Any bot not built on the official API is a liability waiting to shut down your primary customer communication channel.
The chatbot is not connected to any business system.
The chatbot can answer FAQs but cannot check an order status, book an appointment, or create a support ticket. Every useful action requires the customer to go somewhere else. A chatbot that only answers FAQs is a search engine with a conversational UI. A useful chatbot connects to your CRM, your booking system, or your support platform and takes actions on the customer's behalf.
Nobody is monitoring the chatbot for accuracy after launch.
The chatbot was launched. It gets 200 conversations a day. Somebody changed the pricing last month. The chatbot is still quoting the old pricing to every customer who asks. Nobody set up a monitoring process, nobody is reviewing conversation logs, and nobody knows the bot is giving wrong information until a customer complains.
How we build production-ready AI chatbots.
Every chatbot we build goes through a defined QA process, is tested against real customer queries, and includes monitoring after launch. We do not ship chatbots that are impressive in demos and fail in production.
Use case definition and knowledge base build
- Query audit, analysis of existing customer support queries to identify the top 50 questions the chatbot must answer correctly
- Knowledge base compilation, product documentation, FAQs, pricing, policies, and process guides collected and formatted
- Escalation mapping, every query type that must escalate to a human agent documented with trigger condition
- Channel selection, whether the chatbot deploys on WhatsApp, website, or internal Slack, with platform requirements documented
- Integration requirements, whether the chatbot needs to connect to CRM, booking system, or support platform
Chatbot build
- Knowledge base embedding, documentation processed and stored in a vector database for retrieval-augmented generation
- Prompt engineering, system prompt designed to constrain the chatbot to your knowledge base and prevent hallucination
- Conversation flow design, welcome message, main menu, FAQ routing, and escalation paths designed and documented
- Integration build, CRM lead capture, appointment booking, or support ticket creation connected to the chatbot
- Fallback handler, unrecognised or complex queries route to human agent with full conversation context passed across
Testing and QA
- Accuracy testing, the top 50 customer queries tested against the chatbot with expected vs. actual response documented
- Edge case testing, ambiguous queries, typos, and off-topic questions tested against fallback behaviour
- Hallucination stress test, questions designed to elicit hallucinations tested and prompt refined until failure rate is under 2%
- Escalation path testing, every escalation trigger tested to confirm human agent receives full conversation context
- Load testing, chatbot response time verified under concurrent user load before launch
Deployment and monitoring
- Production deployment, chatbot deployed on WhatsApp Business API, website widget, or internal tool
- Monitoring dashboard, conversation volume, resolution rate, escalation rate, and accuracy score tracked daily
- Fortnightly accuracy review, conversation logs reviewed every two weeks to identify new failure patterns
- Knowledge base update process, documented process for updating the knowledge base when product information changes
- Team training, support team trained on how to handle escalated conversations and how to update the bot
What is included in an AI chatbot engagement.
Build
- Query audit and KB compilation
- Vector database setup
- Prompt engineering
- Conversation flow design
- Fallback handler
Integrations
- CRM lead capture
- Appointment booking
- Support ticket creation
- WhatsApp Business API setup
- Website widget deployment
QA
- 50-query accuracy test
- Edge case test suite
- Hallucination stress test
- Escalation path verification
- Load testing
Post-Launch
- Monitoring dashboard
- Fortnightly accuracy review
- KB update process
- Team training
- 30-day post-launch support
This is right for you if:
- Indian B2B and D2C companies receiving more than 100 customer queries per month through chat, WhatsApp, or email
- Businesses where first response time is more than 1 hour and customer satisfaction is suffering
- Support teams spending significant time answering the same 20 questions repeatedly
- Companies that want a WhatsApp Business API chatbot built on the official, compliant infrastructure
- Teams that have tried generic chatbot builders and found them too limited or inaccurate for production use
Not the right fit if:
- Businesses with fewer than 50 customer queries per month: a chatbot is not justified at this volume
- Companies wanting a chatbot trained on general AI knowledge rather than their own documentation: generic chatbots are not useful for customer-facing deployment
Frequently asked questions.
What is an AI chatbot for business?
An AI chatbot for business is a conversational interface trained on your specific product documentation, policies, and FAQs that can answer customer queries, qualify leads, book appointments, or create support tickets without human involvement. Unlike generic chatbot builders, a production AI business chatbot is trained exclusively on your knowledge base, tested against real queries, and deployed with a fallback to a human agent for anything it cannot handle accurately.
How does a WhatsApp Business API chatbot work?
A WhatsApp Business API chatbot uses Meta's official WhatsApp Business API, accessed through an approved partner like Wati, Interakt, or 360dialog, to send and receive messages on a WhatsApp number. Incoming messages are processed by an AI system that matches the query against a knowledge base and generates a response. When the query is outside the chatbot's scope, the conversation is handed to a human agent inside the same WhatsApp interface. All conversations are logged in a shared team inbox.
How do you prevent the chatbot from giving wrong answers?
Hallucination prevention in AI chatbots uses a technique called retrieval-augmented generation: instead of the AI generating answers from general training data, it retrieves specific passages from your documentation and generates answers grounded in those passages. The system prompt instructs the AI to decline to answer when the answer is not in the knowledge base rather than guessing. We then test the system against 50+ real customer queries before launch and measure the accuracy rate, targeting under 2% hallucination on in-scope queries.
Can the chatbot connect to our CRM to capture leads?
Yes. Chatbot conversations can trigger CRM lead creation via API when a qualifying event occurs, such as a prospect providing their email and expressing interest. The lead is created in HubSpot, Zoho CRM, or Salesforce with the full conversation context attached. The assigned rep receives a notification with a summary of what was discussed, so the first human call starts with full context rather than from scratch.
How long does it take to build and deploy an AI chatbot?
A standard AI chatbot, trained on your documentation, deployed on WhatsApp or website, with CRM integration and a tested fallback, is typically live within three weeks of the engagement starting. The timeline depends on the size of the knowledge base, the number of integrations required, and how quickly we can complete the QA testing phase. Chatbots with complex conditional flows or multiple language support take longer.
Ready to build a chatbot that actually works in production?
Book a 30-minute call. We will review your current customer query volume, identify the right channel and use case, and give you a clear scope and timeline for a chatbot your customers will actually find useful.
Book a call