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Applied AI

INTELLIGENT
CHAT
BOT
SYSTEMS

Conversational AI that goes beyond scripted flows — context-aware, domain-trained systems that handle complexity, remember history, and escalate intelligently.

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Overview

BEYOND THE CHATBOT WIDGET

Most chatbot implementations fail because they're treated as conversation scripts with a UI on top. They break on unexpected input, can't handle nuance, and erode trust when they fail.

We build conversational AI systems rooted in modern LLM infrastructure — retrieval-augmented, context-aware, and trained on your domain. They understand intent, not just keywords.

The result handles the long tail of real customer interactions — questions no FAQ anticipated, multi-turn conversations, and edge cases that scripted bots send straight to human agents.

KEY DIFFERENTIATOR

RAG-grounded responses from your actual knowledge base. Not hallucinated answers from a generic model.

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Capabilities

WHAT WE DELIVER

01
LLM-POWERED CORE

Built on Claude, GPT-4, or open-source models — with RAG grounding to your actual knowledge base. Responses are accurate, not hallucinated.

LLMRAGClaudeGPT-4
02
DOMAIN TRAINING

Your products, policies, tone, and terminology embedded into the system's knowledge. The chatbot speaks like it actually works at your company.

Fine-tuningPrompt Eng.Domain Data
03
MULTI-CHANNEL

Single intelligence layer deployed across web, WhatsApp, Instagram DM, and LINE. Consistent behavior, configured per channel.

WhatsAppInstagramWeb Widget
04
MEMORY & CONTEXT

Long-term user memory, session continuity, and conversation history. Systems that recognize returning users and build on previous interactions.

Session MemoryUser Profiles
05
INTELLIGENT HANDOFF

Clean escalation to human agents when needed — with full context passed, not just a transcript. Agents pick up exactly where the AI left off.

Human HandoffContext Transfer
06
ANALYTICS & IMPROVEMENT

Conversation analytics, resolution rates, fallback frequency, and user satisfaction tracking. Data to continuously improve the system.

AnalyticsFallback Tracking
How We Work

THE PROCESS

01
KNOWLEDGE MAPPING

Audit all documentation, FAQs, policies, and institutional knowledge. Define what the system needs to know and how it should respond.

02
ARCHITECTURE DESIGN

Choose the right model, RAG strategy, memory approach, and channel configuration. Design before building.

03
BUILD & TRAIN

RAG pipeline construction, prompt engineering, integration with your data sources and business systems.

04
TESTING & CALIBRATION

Adversarial testing, edge case coverage, tone calibration, and accuracy evaluation before any user sees it.

05
DEPLOY & MONITOR

Phased rollout with analytics from day one. Continuous monitoring of resolution rates and conversation quality.

READY TO BUILD AN AI ASSISTANT THAT ACTUALLY WORKS?

We'll scope the right system for your use case — no overselling, no underdelivering.

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