Consumer electronics D2C
- Challenge
- 2,000+ daily support touches; IVR failed Hindi-English users.
- Simplileap solution
- WhatsApp bot with RAG, order lookup, and Freshdesk handoff.
- Outcome
- 34% tier-1 deflection; human wait time 18min → 6min.
// Automate
AI Chatbot Development Company in Bangalore
LLM-powered chatbots for customer support, WhatsApp messaging, and voice interfaces. Built with your knowledge base, designed for graceful human handoff, and evaluated against real conversation quality metrics, not just deployment counts.
// Services
Chatbot development services
- RAG integration with your knowledge base
- Multi-channel deployment from one codebase
- Human handoff with conversation history
- Conversation analytics and CSAT
- Continuous improvement from missed intents
AI Chatbot Development
LLM-powered chatbots with your knowledge base.
Customer Support Automation
Auto-resolve tickets with AI and human handoff.
WhatsApp & Messaging Bots
WhatsApp Business API bots for notifications and support.
Voice Assistants
Speech-to-text and TTS pipelines for voice interfaces.
Conversational AI Integrations
Dialogflow, Rasa, and custom NLU integrations.
// Standards
Chatbot engineering standards
Graceful human handoff
Every AI chatbot has defined escalation triggers that smoothly transfer conversations to human agents with full conversation context preserved.
Off-topic deflection
Red-teamed against jailbreak attempts and off-topic queries. Bots stay within defined scope and decline out-of-scope questions with appropriate responses.
Multi-channel deployment
Unified conversation logic deployed across web widget, WhatsApp, Slack, and other channels, not separate bots with duplicated logic per channel.
Conversation analytics
Every conversation logged with intent classification, resolution status, and CSAT collection. Weekly analysis of unresolved conversations drives content gaps.
Response latency < 2s
Streaming responses for LLM-backed bots, optimised retrieval pipelines, and response caching for frequently asked questions to achieve sub-2s typical response times.
Knowledge base maintenance
Clear process for updating the bot knowledge base when product or policy changes, not a deploy-and-forget implementation that becomes stale within months.
// Technology
Chatbot technology stack
LLM & NLU
OpenAI GPT-4oClaude 3.5GeminiDialogflow CXRasaBotpress
Channels
WhatsApp Business APITwilioSlackTelegramWeb WidgetTeams
Voice
DeepgramAssemblyAIWhisperElevenLabsTwilio VoiceVonage Voice
Orchestration
LangChainLangGraphFlowiseRasa ProAmazon LexAzure Bot Service
Backend
Node.jsPythonFastAPINestJSWebSocketServer-Sent Events
Analytics
BotanalyticsDashbotLangfuseLangSmithPostHogMixpanel
// Process
From conversation design to deployed, measured bot
01
Use Case Scoping
1–2 daysDefine the bot's scope, conversation flows, and escalation triggers. Map the knowledge sources the bot needs access to and the channels it will operate on.
// Stack & frameworks
Stack we use for this
AI & LLM
- OpenAI / Anthropic APIs
- LangChain pipelines
- RAG architectures
- Confidence thresholds
Integration
- Salesforce / HubSpot
- Zapier / Make
- Custom webhooks
- ERP connectors
Governance
- LangSmith observability
- PII handling
- Audit logs
- Rollback procedures
// Delivery
Simplileap execution framework
01
Architecture mapping
Dependencies, API contracts, compliance constraints, and performance budgets documented before sprint one.
02
Secure sprints
Two-week increments with GitHub access, demo recordings, and QA checkpoints, client visibility at every stage.
03
QA & handover
Automated tests on critical paths, security review, runbooks, and knowledge transfer to your team.
// Proof
Real deployments from Bangalore
Gnani.ai (voice AI)
- Challenge
- Product surfaces needed performance-stable integrations with analytics and API-heavy workflows.
- Simplileap solution
- Custom API layer, observability hooks, and performance budgets on critical user paths.
- Outcome
- Reliable production delivery with maintainable codebase for ongoing feature work.
// Engagement models
How teams engage us
Currency
| Package | Ideal for | Investment | Includes |
|---|---|---|---|
| Workflow automation | Ops teams | ₹3L – ₹10L |
|
| AI / LLM integration | Product teams | ₹4L – ₹12L |
|
| RPA implementation | Back-office | Scoped per process |
|
// Company and service positioning
Company and Service positioning is reviewed for production delivery standards by Harsha Parthasarathy (Co-Founder, Strategy & Operations 24+ years IT veteran, IBM, Global Delivery, Program Management) and Keshav Sharma (Co-Founder, Engineering and Lead Architect, Full-stack engineering, product delivery and technical standards).
CIN
AAU-8582
Startup India
DIPP83124
Founded
November 2020
Office
Residency Rd, Bengaluru, India
// FAQ
Common questions about chatbot development
What is the difference between a rule-based bot and an AI chatbot?+
Rule-based bots follow scripted decision trees, fast, predictable, but limited. AI chatbots use LLMs to understand natural language and handle variation, but require guardrails and evaluation. We recommend AI for support bots, rule-based for structured flows like booking and checkout.
Can the chatbot learn from our existing customer support tickets?+
Yes, we index your historical support tickets, knowledge base articles, and product documentation into a vector store. The bot retrieves relevant context for each query rather than relying on static scripted responses.
How does human handoff work?+
When the bot detects a query beyond its scope, user frustration signals, or an explicit handoff request, it transfers the conversation to a human agent in your CRM or support platform with full conversation history. Popular integrations include Intercom, Freshdesk, and Zendesk.
Can you build a WhatsApp bot for customer support?+
Yes, using the WhatsApp Business API (Cloud API or via BSPs like Twilio, Gupshup, or Infobip). The bot handles incoming messages, routes complex queries to human agents, and sends proactive notifications. WhatsApp Business API requires a Meta-approved number.
What languages can the chatbot support?+
LLM-backed bots natively support 50+ languages, no separate models or training required for most languages. We configure language detection and can route different languages to different knowledge bases or agents if needed.