# AI Customer Support

## Support that knows your business

AI assistants powered by your data, deployed across every channel your customers use. Accurate answers grounded in real context, not generic chatbot scripts.

[Book a Strategy Call](/content/contact/index.html) [See PingMe Case Study](/content/case-studies/pingme/index.html)

## Multi-channel deployment

### One AI brain, every customer touchpoint

Your customers reach out on WhatsApp, email, SMS, and web chat. Your AI assistant gives consistent, accurate answers on all of them.

#### Web Chat

Embedded widget that matches your brand, with seamless handoff to human agents when needed.

#### WhatsApp Business

Reach customers on the platform they already use. Full WhatsApp Business API integration with rich media support.

#### SMS via Twilio

Two-way SMS conversations for customers who prefer text. Automated responses with intelligent escalation.

#### Email

AI-drafted replies to support emails with citation links, reducing first-response time from hours to seconds.

## How it works

### RAG: retrieval-augmented generation

Your AI assistant does not guess. It retrieves real information from your knowledge base, then generates a grounded response. Here is the pipeline.

1. **Ingest**

### Your data, structured for AI

Documents, help articles, product manuals, and CRM records are chunked, embedded, and stored in a vector database. We support PDF, HTML, Notion, Confluence, and custom API sources.

2. **Retrieve**

### Semantic search, not keyword matching

When a customer asks a question, the system finds the most relevant passages using vector similarity search. This handles misspellings, synonyms, and natural language phrasing that traditional search misses.

3. **Generate**

### Grounded answers with citations

The LLM generates a response using only the retrieved context, with source citations. No hallucinated answers. If the system does not have enough context, it escalates to a human agent rather than guessing.

4. **Connect**

### Actions through MCP

Using Model Context Protocol (MCP), the assistant can check order status, update account details, create tickets, and trigger workflows in your existing systems. It is not just answering questions, it is resolving issues.

## The difference

### Traditional support vs AI-assisted support

| Metric                             | Traditional  | AI-Assisted    |
|------------------------------------|--------------|----------------|
| First response time                | 4 - 24 hours | Under 3 seconds|
| Resolution without escalation       | 30 - 40%     | 65 - 80%       |
| Availability                        | Business hours| 24/7, every channel |
| Consistency                        | Varies by agent | Same quality every time|
| Cost per interaction               | Higher with volume | Decreases with volume|

## Beyond answers

### AI that takes action, not just responds

Most chatbots can only answer questions. Using Model Context Protocol (MCP), your AI assistant connects directly to your business systems and takes action on behalf of the customer.

Check order status in your OMS. Update a billing address in your CRM. Create a support ticket in Jira. Cancel a subscription through Stripe. The assistant resolves issues end-to-end, without human intervention for routine requests.

MCP provides a standardised protocol for tool use, meaning new integrations can be added in hours, not weeks. As your systems evolve, your AI assistant evolves with them.

### CRM and Helpdesk

Salesforce, HubSpot, Zendesk, Freshdesk

Create tickets, update contacts, log interactions

### E-commerce and Orders

Shopify, WooCommerce, custom OMS

Track orders, process returns, check inventory

### Billing and Payments

Stripe, GoCardless, Xero

Check invoices, update payment methods, process refunds

### Knowledge Base

Notion, Confluence, custom docs

Search articles, surface procedures, cite sources

### Internal Tools

Custom APIs, databases, ERPs

Query data, trigger workflows, update records

## Case Study

### PingMe: AI that became a trusted team member

Off-the-shelf AI chat tools were too generic for PingMe. Customers got irrelevant answers, and the support team spent more time correcting the bot than it saved. We built a RAG-powered assistant grounded in real business data, connected to internal systems through MCP, and deployed across WhatsApp, SMS, email, and web chat.

The result: faster, more relevant support with reduced costs and improved customer satisfaction. AI that understands context and delivers answers people actually trust.

[Read the Full Case Study](/content/case-studies/pingme/index.html)

## Give your customers the support they deserve

Let us show you how a RAG-powered AI assistant can reduce response times and support costs while improving accuracy.

[Book a Strategy Call](/content/contact/index.html)