SolutionUpdated March 2026

AI chatbots that understand context and solve problems

Build LLM-powered conversational AI that goes beyond decision trees. Grounded in your data, deployed on any channel (web, WhatsApp, Slack, Teams), and capable of handling complex queries with intelligent escalation.

See Our Approach

Your current chatbot frustrates more customers than it helps

Legacy chatbots follow rigid decision trees. They cannot handle anything off-script, they make customers repeat themselves, and they force users through menus when they just want to ask a question. Meanwhile, support ticket volume grows, hiring more agents is expensive, and response times keep slipping. You need chatbots that actually understand what customers are asking.

73%

Of customers say chatbots are the most frustrating service channel

60%

Of routine support queries could be resolved by AI

3-5x

Cost difference between human agent and AI resolution

24/7

Availability that human-only support cannot provide

How Optivus builds intelligent conversational AI

We build LLM-powered chatbots grounded in your specific data (product docs, knowledge base, past tickets, SOPs). They understand natural language, maintain conversation context, handle complex multi-turn queries, and escalate to humans intelligently when needed.

01

Define scope and data

Map the conversations your chatbot should handle. Identify knowledge sources (docs, FAQs, ticket history). Define escalation rules.

02

Build knowledge layer

Build the RAG pipeline connecting the chatbot to your data. Ensure accurate, cited responses grounded in your actual documentation.

03

Deploy and integrate

Deploy on your chosen channels (web widget, WhatsApp, Slack, Teams). Integrate with your CRM and ticketing system for context.

04

Monitor and improve

Track resolution rates, escalation patterns, and user satisfaction. Improve responses based on conversation analytics.

Key capabilities

LLM-powered understanding

Natural language understanding that grasps intent, not just keywords. Handles typos, slang, and complex phrasing.

Knowledge-grounded responses

RAG-backed answers from your docs, FAQs, and knowledge base. No hallucinated information. Cited sources.

Multi-turn conversations

Maintains context across a conversation. Follow-up questions work naturally. Users never have to repeat themselves.

Intelligent escalation

Routes to human agents when needed with full conversation context. The agent picks up where the bot left off.

Multi-channel deployment

Deploy on web, WhatsApp, Slack, Teams, or any messaging platform. Consistent experience across channels.

Multi-language support

Support conversations in English, Hindi, and regional languages. Important for serving customers across India.

Results you can expect

60%

Of routine queries resolved without human intervention

24/7

Availability across all connected channels

3-5x

Cost reduction vs human-only support

2-4 weeks

From scoping to production deployment

Our AI implementation process

Every engagement follows the same four-phase structure.

01

Scope

Map the workflow, define success criteria, lock deliverables.

02

Build

Weekly working demos. Direct channel with the build team.

03

Ship

Production deployment on your cloud with monitoring.

04

Scale

Optimize on real usage. Expand to adjacent workflows.

Frequently asked questions

Traditional chatbots follow pre-built decision trees with fixed responses. AI chatbots use large language models to understand natural language, handle unexpected questions, maintain conversation context, and generate relevant responses. They adapt to how users actually communicate instead of forcing users into rigid flows.
Yes. Modern LLMs have good support for Hindi and major Indian languages. For specialized terminology or regional languages, we can optimize the model for your specific language requirements.
Accuracy depends on the knowledge base quality and RAG pipeline tuning. Our chatbots are grounded in your specific data (not general LLM knowledge), which significantly reduces hallucination. We target 90%+ accuracy for knowledge-grounded responses.
Costs depend on complexity (number of channels, languages, integrations) and knowledge base scope. A focused deployment covering one channel and one knowledge domain starts in the low lakhs range. Multi-channel, multi-language deployments cost more.
A focused chatbot for one channel (e.g., website widget) with one knowledge domain takes 2-4 weeks. Multi-channel deployments with complex integrations take 4-6 weeks.
Yes. We integrate with popular CRMs (Salesforce, HubSpot, Zoho CRM) so the chatbot has context on the customer it is speaking with and can log interactions back to the CRM.

Ready to get started?

Book a 25-minute call. Bring your workflow and we will show you exactly how we would approach it.

See What We Have Built