# AI Memory for Customer Support Agents

Support agents that remember every customer interaction. No more asking customers to repeat themselves.

## The challenge

### Customers repeat themselves

Every new session starts from zero. Customers explain their issue again and again across channels and agents.

### No context between sessions

When a customer returns, the AI has no idea about previous interactions, resolutions, or preferences.

### Generic responses

Without history, the AI gives cookie-cutter answers instead of personalized solutions based on the customer's product usage.

### Slow resolution times

Agents spend time gathering context instead of solving problems. Each ticket starts from scratch.

## How Mengram solves it

### Full customer history

Semantic memory stores customer preferences, plan details, and product usage. Episodic memory recalls past issues and resolutions.

### Cross-session continuity

Every interaction enriches the customer's memory. Next time they reach out, the AI already knows their history.

### Personalized resolution

Cognitive Profile generates a system prompt with everything known about the customer — preferences, history, and escalation patterns.

### Workflow learning

Procedural memory captures resolution workflows that improve from failures. The AI learns the best process for each issue type.

## Quick implementation

```python
from mengram import Mengram
from openai import OpenAI

m = Mengram(api_key="mg-...")
openai = OpenAI()

def handle_ticket(customer_id: str, message: str):
    # Get full customer context in one call
    profile = m.profile(user_id=customer_id)
    past_issues = m.search(message, user_id=customer_id, top_k=3)

context = "\n".join([r.memory for r in past_issues])

response = openai.chat.completions.create(
        model="gpt-4o",
        messages=[\
            {"role": "system", "content": profile},\
            {"role": "user", "content": f"Past issues:\n{context}\n\nNew message: {message}"}\
        ]
    )

# Store this interaction for future context
    m.add(f"Customer: {message}\nAgent: {response.choices[0].message.content}",
          user_id=customer_id)
    return response.choices[0].message.content
```

## Results

40%

Faster resolution

3x

Customer satisfaction

Zero

Context switching

## Build customer support agents with memory

Free API key. No credit card required. Start in 60 seconds.