AI CHATBOT DEVELOPMENT SERVICES
AI Chatbot Development Services for Websites, WhatsApp & CRM
Scallar builds AI conversational systems that connect customer conversations to approved business knowledge—and to real actions such as qualification, CRM updates, bookings, resolution, or human handoff.
Start with one useful workflow. Keep the human path clear.
We need a chatbot for website leads and WhatsApp follow-up.
I can help scope that. Which action should happen after a qualified enquiry?
Chatbot capabilities
One connected conversational system
- 01Website
- 02WhatsApp
- 03CRM
- 04RAG
- 05Automation
- 06Human handoff
FROM CONVERSATION TO OUTCOME
A chatbot should do more than reply
AI chatbot development connects a website or WhatsApp conversation to approved knowledge, a defined decision, and an owned next step. The useful unit is not a message—it is a completed customer or team workflow.
Answer questions
Use approved service, policy, product, and support information.
Qualify leads
Collect intent, fit, location, budget range, or urgency before routing.
Book meetings
Move a qualified conversation into the existing booking workflow.
Update CRM
Create or enrich records, notes, stages, tasks, and owner context.
Automate support
Resolve routine requests and escalate exceptions with conversation context.
Assist sales
Guide buyers to the right offer, resource, or human next step.
08 SOLUTION VARIANTS
Choose the assistant by job, not by hype
Start with the channel and business task that can be defined, tested, and owned. Expand only after the first workflow is reliable.
Website AI Chatbot
Guide visitors, answer approved questions, and capture useful enquiry context.
WhatsApp AI Chatbot
Continue lead, booking, reminder, or support journeys on WhatsApp.
Lead Qualification
Ask a focused set of questions, score fit, and route the conversation.
Customer Support
Handle repeat questions while keeping complaints and exceptions human-led.
CRM-Integrated
Turn conversation details into owned contacts, stages, notes, and tasks.
Ecommerce
Support product discovery, order context, delivery questions, and escalation.
Enterprise Knowledge
Retrieve from governed sources with permissions, evaluation, and monitoring.
Multilingual
Plan language coverage, source quality, fallbacks, and handoff by audience.
THE SYSTEM, END TO END
Every answer should know where it came from—and what happens next
The assistant retrieves approved information, identifies the visitor’s intent, then performs only permitted actions. When confidence, policy, or context falls outside the defined boundary, the conversation moves to a person.
CONCRETE USE CASES
Built around the work your team already does
Suitable businesses usually have repeated questions, structured qualification, meaningful after-hours demand, or a clear operational action after the conversation.
Appointment intent, service information, staff handoff
Clinical or sensitive questions stay with qualified staff.
Programme questions, counselling qualification, CRM capture
Admissions exceptions route to a counsellor.
Product discovery, order context, returns triage
Disputes and unusual requests escalate with context.
Property preferences, site-visit intent, lead routing
Commercial advice and negotiation remain human-led.
Guest information, service requests, front-desk routing
Urgent or non-standard requests bypass automation.
Tracking lookup, delay context, support-ticket creation
Missing, damaged, or disputed shipments escalate.
INTEGRATION ECOSYSTEM
Meet customers in the channel they use. Continue work in the system your team owns.
Website and WhatsApp chatbot conversations can connect to supported CRM, knowledge, booking, helpdesk, spreadsheet, or API workflows. The exact stack is selected during discovery based on access, permissions, reliability, and ownership.
Technology names describe integration options represented in Scallar’s existing service and case-study material; they do not imply vendor partnership or endorsement.
Messaging
- Website chat
- WhatsApp Business API
CRM
- HubSpot
- Zoho CRM
- Salesforce
- EspoCRM
Knowledge
- Approved documents
- Website content
- Product data
- Google Sheets
Automation
- n8n
- Zapier
- Make.com
- Webhooks & APIs
AI infrastructure
- Language model
- RAG retrieval
- Evaluation set
- Monitoring
RELIABILITY IS A PRODUCT FEATURE
AI that knows when to answer — and when not to.
RAG can help a chatbot answer from approved business knowledge, but retrieval alone is not a reliability strategy. Good systems pair governed sources with permissions, refusal rules, evaluation, privacy choices, monitoring, and a fast path to a person.
Read the integration guideGrounded knowledge
Answers retrieve from approved sources with ownership for freshness and corrections.
Permissioned actions
The assistant can only read or change what the defined tool and role boundaries allow.
Uncertainty & refusal
Low-confidence, restricted, or unsupported questions receive a safe fallback—not a guess.
Evaluation & monitoring
Realistic test questions, fallback reasons, tool errors, and handoffs are reviewed over time.
Privacy by design
Capture only the information needed for the task, with clear access and retention decisions.
Human escalation
Sensitive, frustrated, urgent, or complex conversations transfer with useful context.
DEVELOPMENT PROCESS
A controlled path from first use case to live assistant
A focused website chatbot can often launch in 2–4 weeks when content and decisions are ready. CRM, WhatsApp, multi-language, permissioned actions, or complex RAG increase the scope and test effort.
- 01
Discover
Choose the first user, business outcome, channel, boundaries, and success criteria.
- 02
Design
Map intents, answers, qualification fields, fallbacks, tone, and escalation paths.
- 03
Connect data
Prepare approved knowledge and define CRM, calendar, helpdesk, or API access.
- 04
Build
Implement the conversation, retrieval, integrations, actions, and handoff experience.
- 05
Evaluate
Test expected, ambiguous, restricted, multilingual, and failure scenarios.
- 06
Launch & optimise
Release in a controlled scope, review real conversations, and improve safely.
DOCUMENTED WORKFLOWS
See how bounded chatbot systems work in practice
These existing case studies illustrate workflow design and operational handoff. They are context for implementation—not a promise that a new project will reproduce the same conditions or outcomes.
Hotel guest-support chatbot
A WhatsApp-based concierge for common hotel information, service requests, department routing, and front-desk escalation.
- Approved guest information
- Operational request routing
- Human front-desk path
Logistics WhatsApp tracking chatbot
A tracking-data workflow for shipment status, delay context, delivery proof, and escalation of damaged, missing, or disputed cases.
- Bounded data lookup
- Ticket creation with context
- Exception reporting
ENGAGEMENT SCOPES
Scope follows the workflow—not a fixed feature checklist
Cost is driven by channels, conversation depth, source readiness, integrations, languages, permissions, evaluation, monitoring, and the support model. Scallar confirms the requirements before quoting.
Starter Assistant
One channel and one bounded use case
- Conversation and fallback design
- Approved knowledge setup
- Lead capture or support routing
- Launch testing and handover
Business Automation
A chatbot that must create a real next action
- Website or WhatsApp journey
- CRM, booking, helpdesk, or API connection
- Qualification and owner routing
- Monitoring and improvement plan
Custom / Enterprise
Multiple teams, sources, roles, channels, or languages
- RAG and source governance
- Permissions and tool boundaries
- Evaluation and operational controls
- Phased rollout and support model
- Channels
- Knowledge & data readiness
- Integrations
- Languages
- Security & permissions
- Testing & support
AI CHATBOT FAQ
Questions to answer before you build
Short answers to the decisions most teams face when comparing an AI chatbot development company, platform, or first use case in India.
01How much does AI chatbot development cost?
AI chatbot cost depends on channels, knowledge sources, conversation depth, integrations, languages, permissions, evaluation, and ongoing support. A focused website assistant costs less than a multi-channel system connected to CRM, helpdesk, ecommerce, or internal data. Scallar scopes the workflow first and provides a transparent proposal; the pricing guide explains the main cost drivers.
02Can you build a chatbot for WhatsApp?
Yes. Scallar can build WhatsApp chatbot flows for lead capture, booking, reminders, and customer support, then connect supported CRM, Google Sheets, booking systems, and human handoff rules.
03Can the chatbot connect to CRM?
Yes. A CRM-integrated chatbot can create or enrich contacts, update lead stages, add conversation notes, assign owners, and trigger follow-up tasks when the target CRM and permissions support those actions.
04Can the chatbot answer from our business data using RAG?
Yes. Scallar can prepare approved services, FAQs, policies, documents, and product information as governed knowledge for retrieval-augmented generation (RAG). Source ownership, permissions, freshness, citations where useful, refusal behaviour, and evaluation are planned together.
05What is the first step in creating an AI chatbot?
Start by defining one useful job: the user, priority questions, approved knowledge, next action, required fields, escalation rules, and connected systems. Platform selection comes after the workflow and boundaries are clear.
06Can Scallar build an AI chatbot for local businesses?
Yes. Local businesses can use a website or WhatsApp chatbot to answer service questions, capture location and appointment intent, qualify enquiries, route support, and log useful context in CRM or Google Sheets.
07Is Scallar an AI chatbot development agency?
Yes. Scallar provides AI chatbot strategy, conversation design, knowledge preparation, development, integration, evaluation, launch, and handover for website, WhatsApp, CRM, lead-qualification, and support workflows.
08What is a good first use case for an AI chatbot?
A good first use case has common questions, stable approved answers, and a clear next action—such as service discovery, lead qualification, appointment intent, order context, or routing to the right team.
09Can a chatbot hand a conversation to a person?
Yes. A responsible chatbot includes visible escalation choices, clear triggers, owner notifications, and the relevant conversation context so a person can continue without asking the customer to start again.
10What information is required before chatbot development starts?
Useful inputs include priority customer questions, approved answers, products or services, operating hours, lead fields, escalation rules, privacy constraints, connected systems, language needs, and the team responsible for future updates.
11How long does an AI chatbot take to launch?
A focused chatbot for FAQs and lead capture can often launch in 2–4 weeks when content and decisions are ready. A multi-channel assistant with CRM, WhatsApp, helpdesk, permissions, or complex RAG may take 4–8 weeks or more depending on access, data readiness, and testing.
12Which businesses are a good fit for AI chatbot development?
AI chatbots suit businesses with repeated questions, meaningful after-hours demand, structured qualification, high support volume, or a clear action after the conversation. They are less suitable when every enquiry requires sensitive judgement or frequently changing, unapproved information.
BUILD YOUR STARTING BRIEF
Plan the first useful version
Choose a channel, outcome, and connection. Your selections stay on this page; the call is where Scallar validates feasibility, boundaries, and scope.CONTINUE YOUR RESEARCH