Consumer electronics D2C
- Challenge
- Support and sales needed shared conversation context.
- Simplileap solution
- WhatsApp bot with CRM handoff on low-confidence intents.
- Outcome
- CSAT 4.1/5 on bot-resolved threads.
// Automate
AI Chatbot Development
Modern AI chatbots powered by LLMs are qualitatively different from rule-based predecessors. We build chatbots that understand natural language, access your knowledge base, handle complex queries, and escalate gracefully when they cannot help.
// Key benefits
What makes this service valuable
LLM-powered understanding
Natural language understanding that handles synonyms, typos, context shifts, and complex multi-turn conversations, far beyond keyword matching.
Knowledge base integration
RAG pipelines ground chatbot responses in your specific product documentation, FAQs, and policies, eliminating hallucination and ensuring accurate, on-brand responses.
Seamless human handoff
Clear confidence thresholds, escalation triggers, and conversation summary handoff to human agents, so the chatbot enhances, not frustrates, your customer experience.
// Details
Chatbots that actually help customers
Most deployed chatbots frustrate users, they cannot understand intent, answer incorrectly, and trap users in dead-end flows. We build chatbots that handle the majority of queries accurately and escalate the rest transparently.
We use a RAG architecture to ground the chatbot in your knowledge base, implement intent classification for routing, and design fallback flows that never leave users stuck.
// What this includes
- LLM selection and prompt engineering
- Knowledge base RAG integration
- Intent classification and routing
- Conversation history management
- Human handoff integration
- Analytics and conversation quality monitoring
- Continuous improvement from unresolved queries
// Deliverables
What you receive
Every engagement produces clear, documented deliverables. Here is exactly what is included in our ai chatbot development service.
- 01Custom AI chatbot implementation
- 02Knowledge base and RAG pipeline setup
- 03Human handoff integration
- 04Chatbot analytics dashboard
- 05Quality monitoring and improvement process
- 06Deployment and integration documentation
// In practice
How ai chatbot development engagements run
Production chatbots start with intent taxonomies and a curated FAQ/knowledge corpus — not open-ended prompting on day one. We wire RAG with vector stores (Pinecone or pgvector), Langfuse traces on retrieval quality, and human handoff when confidence scores drop below your threshold. Deflection rate and average handle time are baselined against Zendesk or Freshdesk exports before go-live.
// 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
// 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 ai chatbot development
How do you prevent the chatbot from giving wrong answers?+
RAG grounds responses in your verified documentation. We also implement confidence scoring, responses below a threshold trigger human escalation rather than a potentially wrong answer. All responses include source citations where applicable.
Can the chatbot handle multiple languages?+
Yes, modern LLMs are multilingual. We can configure language detection and language-specific knowledge bases. Accuracy varies by language; English and major Indian languages have the best support.
Ready to get started with ai chatbot development?
Share your requirements with our team. We respond within one business day with a clear plan from discovery to delivery.