AI Chatbots.
Your Data. Smart Answers.
Generic chatbots give generic answers. Our AI chatbots are trained on your specific business data: your products, pricing, processes, FAQs, and case studies. They answer questions the way your best employee would, available 24/7.
What's Included
Knowledge Base Creation
Ingest your docs, FAQs, product data, pricing, and processes into a searchable AI knowledge base
Conversational Flow Design
Multi-turn conversation design for lead qualification, support, recommendations, and escalation
RAG Architecture
Retrieval-Augmented Generation: chatbot pulls answers from your data, not general internet knowledge
Multi-Channel Deployment
Website widget, WhatsApp, Slack, or embedded in your existing app. Same brain, multiple interfaces
Human Handoff System
Seamless transfer to human agents when the chatbot cannot answer, with full conversation context preserved
Trusted by businesses across 12+ industries
The Problem
The Problem
Traditional chatbots follow rigid decision trees. User clicks option A, gets response A. Anything outside the tree? "I do not understand your question. Please contact support." Customers hate them because they are glorified FAQ pages with a chat interface.
AI chatbots powered by large language models are the opposite: they understand natural language and generate intelligent responses. But without training on your specific data, they hallucinate, give wrong pricing, and make promises your business cannot keep.
We build AI chatbots that combine the intelligence of LLMs with the accuracy of your business data. Using RAG (Retrieval-Augmented Generation), every answer is grounded in your actual documents, pricing, and processes. The bot is smart AND accurate.
How custom-trained chatbots transform customer interaction.
Modern AI chatbots use large language models (LLMs) to understand and respond to natural language. Unlike rule-based chatbots that follow decision trees, AI chatbots can handle unexpected questions, maintain conversation context across multiple messages, and provide nuanced answers.
The key technology behind effective business chatbots is RAG (Retrieval-Augmented Generation). Instead of relying on the AI general knowledge (which may be outdated or wrong for your business), RAG retrieves relevant information from your specific documents, product data, and knowledge base before generating a response. This grounds every answer in your actual business data.
Common deployment scenarios include customer support (handling 60-80% of inquiries without human intervention), lead qualification (asking the right questions and scoring responses), internal knowledge management (employees getting instant answers from company documentation), and sales assistance (helping prospects find the right product or service based on their needs).
Why We Are Different
Wonkrew vs the typicalai chatbots & assistants experience.
“Most AI projects fail because they start with the technology, not the problem. We build AI that solves real business problems, not science experiments.”
Satish Rajendran
Founder, Wonkrew
22+ years in tech and marketing. Former Cognizant. 500+ projects delivered.
How We Work
How we build AI chatbots.
From data ingestion to deployed chatbot in 3-4 weeks.
Data Collection & Ingestion
We gather your documents, FAQs, product data, pricing, and process guides. Ingest everything into a vector database that the AI can search intelligently.
Conversation Design
Design conversation flows for your top use cases: support, sales, lead qualification. Define guardrails, tone of voice, escalation rules, and out-of-scope handling.
Build & Test
Build the RAG pipeline, create the chat interface, integrate with your website/WhatsApp/Slack. Test with 100+ real scenarios to validate accuracy and edge case handling.
Deploy & Monitor
Launch on your chosen channels. Monitor conversations, accuracy rates, and user satisfaction. Tune the knowledge base and prompts based on real conversation data.
Transparency
How we report ai chatbots & assistants results.
No black box. No jargon. Every month you get a clear picture of what we did, what moved, and what we are doing next.
Conversation Analytics
Total conversations, resolution rate, handoff rate, common topics, unanswered questions. See exactly what customers ask and how well the bot handles it.
Accuracy Dashboard
Answer accuracy rates, hallucination incidents, source attribution quality. Every answer traceable to a source document.
Business Impact
Support tickets deflected, leads qualified, response time improvement, customer satisfaction scores. Chatbot ROI quantified.
Industries We Serve
Across every vertical.
Manufacturing
Kassa, Simta
F&B
Cake Square, Chocomans
EdTech
Stride, Everwin
Healthcare
Kinesis, Cansaa
Sports
Football Plus
Hospitality
Sparsa Resorts
Legal
Lincoln, Pravda
E-Commerce
Velonae, Beanies
AI Chatbots & Assistants results thatmoved the needle.
Wonkrew AI Concierge (Planned)
AI chat trained on 71 case studies, 48 services, and complete pricing data. Helps visitors find the right service and see relevant case studies without browsing.
Case Studies Trained
Services Mapped
Kassa ABS Doors
WhatsApp-based automated responses for dealer inquiries. Instant pricing, product specs, and nearest dealer information for leads across 180+ cities.
Cities Covered
Dealer Info
Healthcare Clinic
WhatsApp chatbot handling appointment booking, FAQ responses, and doctor availability queries. Reduced admin workload by 15+ hours per week.
Hours Saved/Week
Availability
Stride Edutech
Lead qualification chatbot for prospective students. Asks about background, goals, and budget before routing to the appropriate counselor with full context.
Qualification
Routing
Tools & Technology
The tools we work with.
AI & NLP
Chat Interfaces
Data & Infrastructure
FAQ
Common questions about ai chatbot development.
With proper RAG setup and training data, AI chatbots achieve 85-95% accuracy on questions within their knowledge base. For questions outside the knowledge base, the bot escalates to humans rather than guessing. The key is comprehensive data ingestion and well-defined guardrails.
A functional chatbot with RAG can be built in 3-4 weeks. Week 1: data ingestion and knowledge base creation. Week 2: conversation design and guardrails. Week 3: build and integration. Week 4: testing and deployment. Complex multi-channel deployments may take 5-6 weeks.
Yes. LLMs natively support multiple languages. We can configure the chatbot to detect user language and respond accordingly. For business data in one language, the AI can translate responses on the fly while maintaining accuracy.
We implement multiple safeguards: RAG ensures answers come from your data (not AI imagination), confidence thresholds trigger human handoff for uncertain answers, and guardrails prevent the bot from making commitments or quoting prices not in the knowledge base.
Custom AI chatbot development starts at Rs.2,00,000 ($2,500 USD). Ongoing AI API costs are typically Rs.3,000-15,000/month depending on conversation volume. Compare this to hiring a 24/7 support team.
Yes. The knowledge base is a living database. When you update products, pricing, or processes, we update the knowledge base and the chatbot immediately reflects the changes. No code changes needed for data updates.
We recommend transparency. The initial greeting identifies it as an AI assistant. Most customers prefer instant, accurate AI responses over waiting in a queue for a human. The quality of answers matters more than who delivers them.
Yes. We design qualification flows that ask the right questions (budget, timeline, requirements), score responses, and either book a meeting directly via calendar integration or route the qualified lead to your sales team with full context.
From Our Blog
AI Chatbot Development insights.
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