Intelligent conversational AI assistants.
We build custom AI chatbots and virtual assistants powered by large language models, retrieval-augmented generation, and multi-turn conversation memory for customer support, sales, and internal operations.
import OpenAI from "openai";
const openai = new OpenAI();
const response = await openai.chat.completions.create({
model: "gpt-4o",
messages: [
{
role: "system",
content: "You are a helpful support agent. Use provided context to answer."
},
...messageHistory,
{ role: "user", content: userMessage }
],
temperature: 0.1,
max_tokens: 1024,
});
return response.choices[0].message.content;Conversational AI Engine
We build multi-turn conversation systems with context retention and intent classification, enabling natural dialogue that understands user intent across complex interactions.
Tools & Frameworks We Engineer With
LLM Stack
- GPT-4o: OpenAI flagship reasoning model
- Claude 3.5: Anthropic long-context model
- Gemini 1.5: Google multimodal model
- Llama 3: Open-source self-hosted option
Retrieval Pipeline
- Pinecone: Managed vector database
- LangChain: LLM orchestration framework
- pgvector: PostgreSQL vector extension
- Embeddings: text-embedding-3-small
Deployment Channels
- Web Widget: Embeddable chat interface
- WhatsApp: Business API integration
- Slack: Workspace bot deployment
- REST API: Custom integration endpoint
Monitoring
- LangSmith: LLM observability platform
- Conversation Analytics: Intent and sentiment tracking
- Feedback Loops: Human-in-the-loop training
- A/B Testing: Prompt variant testing
Who We Work With
E-Commerce
Product recommendations, order tracking, and shopping assistants.
SaaS
Onboarding assistants, feature guides, and technical support bots.
Healthcare
Patient intake, appointment booking, and symptom triage bots.
Banking
Account inquiries, fraud alerts, and transaction assistance.
Education
Tutoring bots, course recommendations, and student support.
HR & Recruitment
Candidate screening, onboarding guides, and policy Q&A bots.
Our Delivery Lifecycle
We operate under a structured, predictable lifecycle. Click any step to inspect deliverables.
Use Case & Knowledge Mapping
We identify high-impact automation opportunities and map your knowledge sources.
{
"useCase": "Customer Support",
"model": "GPT-4o",
"knowledgeSources": 12,
"expectedDeflection": "75-85%"
}Frequently Asked
We use RAG to ground responses in your actual data, apply low temperature settings, implement citation requirements, and add guardrail checks that flag uncertain responses for human review.
Yes. Modern LLMs natively support 40+ languages. We configure language detection and can route conversations to language-specific knowledge bases for optimal accuracy.
A basic support chatbot takes 3-4 weeks. Complex multi-channel assistants with RAG, integrations, and custom training take 6-8 weeks.
We implement intelligent escalation — the bot recognises its limitations and seamlessly hands off to a human agent with full conversation context preserved.
Ready to build your AI assistant?
Get a chatbot strategy proposal with use case analysis and ROI projection within one week.