How AI Works

Conversational AI

Beyond chatbots — context-aware, memory-persistent AI assistants that understand intent, maintain state, and take action.

Timeline

Evolution of Conversation

From rigid decision trees to autonomous agents — four eras of conversational AI, each unlocking new capabilities.

2010s

Rule-Based Chatbots

Decision trees and keyword matching. Brittle scripts that broke on any unexpected input. Zero adaptability.

Keyword matchDecision treesNo memory

2018

Intent Classification

NLU models learned to classify intents and extract entities. Better coverage, but still limited to predefined categories.

Intent modelsEntity extractionSlot filling

2023

LLM-Powered Assistants

Foundation models brought open-ended reasoning, multi-turn context, and natural language generation to every conversation.

LLM reasoningMulti-turnRAG grounding

2025+

Agentic Assistants

Autonomous agents that plan multi-step workflows, call tools, maintain persistent memory, and take real-world actions.

Tool usePlanningPersistent memory

Architecture

The Conversation Pipeline

Seven stages from user input to delivered response. Click a stage or watch it auto-advance every 4 seconds.

Input Processing

Intent Classification

Context Window

Memory Retrieval

Response Generation

Action Execution

Output Delivery

Normalize text, detect language, resolve coreferences, and clean the user message for downstream processing.

PIPELINE ACTIVE

Stage 01/07 — Input Processing

Memory

Memory Systems

Four layers of memory give conversational AI the ability to remember, learn, and personalize across sessions.

Short-Term Memory

Conversation Buffer

~16K tokens

Holds the current conversation turns in a sliding window. Provides immediate context for the LLM without retrieval latency.

When to use: Every turn — always active as the primary context source.

Long-Term Memory

Vector Store

Unlimited

Embeds and indexes past conversations in a vector database. Retrieves semantically similar exchanges when the conversation needs historical context.

When to use: When the user references past interactions or the topic spans multiple sessions.

Episodic Memory

Past Interactions

Per-user index

Stores structured summaries of completed conversations — outcomes, decisions, and user feedback. Enables the assistant to learn from experience.

When to use: Recurring workflows, follow-up conversations, and personalization.

Semantic Memory

User Knowledge Graph

Graph DB

A structured graph of entities, relationships, and preferences extracted from interactions. Powers deep personalization and proactive suggestions.

When to use: Complex domains with user-specific knowledge — healthcare, finance, enterprise.

Channels

Channel Integration

One conversation engine, many surfaces. Deploy the same AI assistant across every channel your users already use.

Web Chat

Embedded widget
Rich media cards
Typing indicators
File uploads

Mobile SDK

iOS & Android native
Push notifications
Offline queue
Biometric auth

Business API
Template messages
Media support
End-to-end encryption

Slack

Slash commands
Thread replies
App Home tab
Interactive blocks

Microsoft Teams

Bot framework
Adaptive cards
Meeting extensions
SSO integration

Voice

Telephony (SIP/PSTN)
IVR replacement
Real-time STT/TTS
Barge-in support

Measurement

Metrics That Matter

The KPIs that separate a toy chatbot from a production-grade conversational AI system.

Task Completion Rate

87%

Percentage of user requests resolved without human handoff.

CSAT Score

4.6/5

Average customer satisfaction rating from post-conversation surveys.

Containment Rate

92%

Conversations fully handled by the AI without escalation.

Avg Handle Time

45s

Mean time from first message to resolution — down from 8 min with human agents.

Escalation Rate

Conversations transferred to a human agent due to complexity or sentiment.

Explore capabilities Voice AI deep-dive

System Blueprint