AI Chatbot
Development Services

AI chatbot development services cover the design, engineering, integration and deployment of conversational applications that interact with users through natural language.

They can support customer service, lead qualification, employee assistance, knowledge access and other digital workflows.

A custom AI chatbot is designed around a specific organization's users, knowledge, workflows, systems and business requirements rather than using a generic conversation flow.

We can build customer-support chatbots, enterprise assistants, lead-qualification chatbots, website chatbots, multilingual chatbots and messaging-based conversational applications.

Yes.

A knowledge-grounded chatbot can retrieve information from approved sources such as websites, knowledge bases, documentation or internal content before generating a response.

For deeper retrieval architecture, see our RAG Development Services.

Yes.

Chatbots can connect with supported APIs and enterprise systems to retrieve information, create records or initiate defined workflows.

Complex integration work can be handled through our AI Integration & Implementation Services.

Yes.

Human handoff can be triggered by user request, confidence thresholds, sensitive topics, workflow exceptions or other defined rules.

Yes, depending on the selected language models, translation approach and quality requirements.

Multilingual performance should be evaluated using representative conversations from each target language.

Yes.

Depending on the model and architecture, chatbot components can run in managed cloud, private cloud, customer-controlled or hybrid environments.

A chatbot is the conversational application users interact with.

RAG is one way of retrieving relevant information so the chatbot can answer from approved knowledge.

A chatbot can use RAG, but not every chatbot requires it.

A chatbot primarily manages conversational interaction.

An AI agent may plan steps, use tools and perform actions across systems.

Some applications combine both, but the primary purpose is different.

A chatbot typically interacts through text-based channels.

A voice AI agent must also handle speech recognition, speech generation, telephony, turn-taking and real-time call behavior.

For voice applications, explore Pulastya AI.

Evaluation can include answer accuracy, task completion, handoff behavior, response latency, unresolved questions and application-specific business outcomes.

The evaluation set should reflect the real conversations users are expected to have.

The timeline depends on conversation complexity, integrations, knowledge sources, deployment requirements and testing scope.

A simple chatbot may require substantially less work than a chatbot connected to enterprise systems and controlled business workflows.

Yes.

We can review conversation quality, unresolved intents, knowledge retrieval, prompts, integration failures, handoff behavior, latency and production analytics to identify where the chatbot can improve.