Conversational AI & Chatbot Systems
Not generic chatbots — AI systems trained on your documentation, products, and processes. Deployed on your website, WhatsApp, Slack, or internal portals, they handle support, qualify leads, and surface knowledge instantly. Context-aware, multi-turn, with human handoff and full analytics.
Tech Stack
Key Features
- Custom training on your knowledge base, FAQs, and product docs
- Multi-channel deployment — web, WhatsApp Business, Slack, Teams
- Context-aware multi-turn conversations with memory
- Lead capture, qualification, and CRM sync
- Intelligent escalation to human agents with full context handoff
- Analytics dashboard: resolution rate, CSAT, unanswered queries
Service Level
Frequently Asked Questions
How do you train the chatbot on our specific business data?
We use Retrieval Augmented Generation (RAG): your documents — product manuals, FAQs, support tickets, internal wikis — are chunked, embedded into vectors, and stored in a vector database (Pinecone or pgvector). At query time, the system retrieves the most relevant chunks and injects them into the language model's context, so it answers only from your approved content. No fine-tuning is required, and the knowledge base can be updated by adding new documents without rebuilding the model.
What accuracy can we expect from a custom AI chatbot?
For in-scope questions (covered by your knowledge base), a well-built RAG chatbot typically resolves 65–80% without human intervention within the first 60 days of launch. Accuracy improves as you monitor unanswered queries and expand the knowledge base. For out-of-scope questions, a properly configured chatbot will say 'I don't know' rather than hallucinating — which is the correct behaviour. We track resolution rate, CSAT, and unanswered query volume from day one.
Which channels can the chatbot be deployed on?
We deploy to: website (embedded chat widget via WebSocket), WhatsApp Business (requires Meta Business verification — we handle the setup), Slack (via Slack app with slash commands or @mentions), and Microsoft Teams (via Azure Bot Service). Each channel has different message format constraints — WhatsApp limits are 1,600 characters with no markdown; Slack and Teams support rich formatting. We build a single intelligence layer and channel-specific adapters so the chatbot behaves consistently across platforms.
How does human escalation work?
Human escalation is triggered by: (1) a low-confidence retrieval (the system didn't find a relevant document), (2) a detected sentiment shift (the user expresses frustration), (3) a query type flagged as requiring human judgment (billing disputes, legal questions, complex complaints), or (4) an explicit user request ('speak to a person'). On escalation, the full conversation context is forwarded to your support team via email, Slack notification, or CRM ticket — the human picks up where the bot left off.
Is customer data processed by third-party AI APIs? How is privacy handled?
By default, conversation data passes through OpenAI or Anthropic APIs for inference. Both have enterprise data processing agreements available: OpenAI's zero data retention option means queries are not used for training; Anthropic has similar provisions under their API terms. For clients with strict data residency requirements (GDPR, HIPAA), we offer self-hosted model options (Llama 3, Mistral) running on your own cloud infrastructure where no data leaves your environment.
Ready to Get Started?
Let's discuss your project requirements and how we can help you achieve your goals with our conversational ai & chatbot systems expertise.