AI chatbots and agents for customer service

The problem is rarely “we don’t have a chatbot”. It’s this:

• Support teams answering the same question hundreds of times a day
• Customers waiting until Monday for something answerable in ten seconds
• Decision-tree bots that only teach customers to type “talk to an agent”
• WhatsApp conversations lost, with no traceability or CRM record
• Generic AI chatbots inventing policies and prices that don’t exist
• Internal staff losing hours searching through manuals and procedures

A bot that doesn’t resolve doesn’t save work: it postpones it and annoys the customer on the way.

The fear of hallucination is justified. Here’s how we prevent it:

• RAG (retrieval-augmented generation): the agent answers only from your documents, catalog and policies — not from what the model “believes it knows”
• Bounded scope: we explicitly define what it may and may not assert; outside that perimeter, it defers
• Human escalation: on doubt, high value or an upset customer, the conversation goes to a person with full context
• Traceability: every answer is logged with the source backing it, auditable

The difference between a useful assistant and a dangerous one isn’t the model: it’s the design around the model.

• Customer service chatbots on the WhatsApp Business API
• Website assistants connected to your catalog and inventory
• Internal agents answering from manuals, procedures and knowledge bases
• Transactional bots: scheduling, order tracking, quoting
• CRM and help desk integration so nothing gets lost
• Rescue of existing chatbots that don’t resolve or that hallucinate

If the project grows toward deeper automation, the same team covers it from applied artificial intelligence.

1. Conversational discovery

• We analyze your real conversations to see what actually gets asked
• We identify what share can be safely automated
• We define the bot’s limits and the escalation rules

2. Knowledge base

• We consolidate catalog, policies and procedures as a single source
• We build the RAG architecture with vector search
• We define the brand tone and handoff messages

3. Development and testing

• Integration with WhatsApp, web and CRM
• Adversarial testing: we actively try to make it hallucinate before a customer does
• Pilot on limited real traffic with human supervision

4. Production and improvement

• Conversation monitoring and resolution rate
• Knowledge base tuned against what the bot couldn’t answer
• Scope widened gradually based on results