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

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

Customer satisfaction

Zero

Context switching

Build customer support agents with memory

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