Add AI Memory to Fliqr AI

Enhance Fliqr AI with long-term memory, improving omnichannel customer interactions and personalization.

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How does memory work with Fliqr AI?

Pieces empowers Fliqr AI to remember past conversations and customer preferences across WhatsApp, Facebook, Instagram, Telegram, and other platforms. This allows Fliqr AI to deliver more contextually relevant and personalized responses, improving customer satisfaction and streamlining support interactions.

What kind of memory does it add?

1

Install & launch PiecesOS

Install and run Pieces OS on your device.

2

Enable Long-Term Memory

Open the PiecesOS menu from your tray and enable Long-Term Memory.

3

Start using memory

Fliqr AI now leverages Pieces to access past interactions, enabling smarter, more informed responses.

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Capture context automatically

Pieces intelligently captures relevant context from your Fliqr AI interactions, including conversation history, customer data, and support tickets. This capture happens automatically and securely, and you maintain full control over what is saved and can easily opt out at any time, ensuring your data remains private and protected. This seamless integration enhances Fliqr AI's ability to understand and respond effectively.

Recall information instantly

Pieces stores your Fliqr AI memories securely for up to 9 months, allowing you to quickly access past interactions and insights. With time-based queries, you can easily retrieve specific conversations, track customer issues over time, and gain a deeper understanding of customer behavior. This historical context empowers Fliqr AI to provide more informed and proactive support.

Navigate history easily

Pieces Memory powers Fliqr AI with deep context from past chats, support interactions, and multi-channel history. By retaining and connecting this information, Fliqr AI can personalize responses, resolve recurring issues faster, and guide customer journeys more intelligently, improving both efficiency and satisfaction across your support workflows.

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What were the key performance indicators (KPIs) I tracked to measure the success of the fliqr-ai chatbot?
Find the saved search I created to identify customers who have given positive feedback.

What were the last 5 searches I ran in fliqr-ai related to "order status"?
Summarize my fliqr-ai session from yesterday, focusing on interactions with user ID "12345"?
Show me all the code snippets I saved from my fliqr-ai debugging efforts last week.
What are the key differences between the chatbot responses I tested in fliqr-ai on Monday versus today?
Find all instances where I used the phrase "payment declined" in fliqr-ai chats over the past month.
Retrieve the fliqr-ai automation script I created for handling "address change" requests.
What were the steps I took to troubleshoot the "invalid coupon code" error in fliqr-ai last Friday?
Show me the saved search query I used to identify customers with overdue invoices in fliqr-ai.
Summarize the customer sentiment analysis results I generated from fliqr-ai conversations this morning.
Find the specific fliqr-ai chat log where I discussed the new product feature with a customer named "Jane Doe".
What were the variables I used in the fliqr-ai automation script for handling "return requests"?
Show me the performance metrics I collected from fliqr-ai after deploying the new chatbot model.
Retrieve the fliqr-ai task I created to follow up with customers who abandoned their shopping carts.
What were the different prompts I used to test the fliqr-ai chatbot's ability to handle ambiguous questions?
Find the code snippet I used to implement the sentiment analysis functionality in fliqr-ai.
Summarize the key takeaways from my fliqr-ai session focused on improving customer satisfaction scores.
Show me all the fliqr-ai conversations where the customer mentioned a specific competitor.
Retrieve the fliqr-ai experiment I ran to compare different chatbot response styles.
What were the error messages I encountered while integrating fliqr-ai with the CRM system?
Find the saved search I created to identify customers who are eligible for a loyalty program upgrade.
Summarize the feedback I received from customers regarding the new fliqr-ai chatbot interface.
Show me the fliqr-ai chat log where I provided instructions on how to reset a password.
Retrieve the fliqr-ai automation script I used to send out promotional offers to new customers.
What were the different parameters I adjusted while fine-tuning the fliqr-ai chatbot model?
Find the code snippet I used to validate customer email addresses in fliqr-ai.
Summarize the key findings from my fliqr-ai session focused on reducing customer churn.
Show me all the fliqr-ai conversations where the customer requested a refund.
Retrieve the fliqr-ai experiment I ran to test different call-to-action messages.
What were the performance improvements I observed after optimizing the fliqr-ai chatbot's response time?
Find the saved search I created to identify customers who have not placed an order in the past 6 months.
Summarize the customer support interactions I had through fliqr-ai related to a specific product.
Show me the fliqr-ai chat log where I provided instructions on how to track an order.
Retrieve the fliqr-ai automation script I used to send out welcome emails to new subscribers.
What were the different machine learning algorithms I explored while developing the fliqr-ai chatbot?
Find the code snippet I used to integrate fliqr-ai with the payment gateway.
Summarize the insights I gained from analyzing customer reviews collected through fliqr-ai.
Show me all the fliqr-ai conversations where the customer complained about a specific issue.
Retrieve the fliqr-ai experiment I ran to test different chatbot personalities.
What were the security vulnerabilities I identified while auditing the fliqr-ai system?
Find the saved search I created to identify customers who are at risk of churning.
Summarize the training data I used to train the fliqr-ai chatbot.
Show me the fliqr-ai chat log where I provided assistance with a technical issue.
Retrieve the fliqr-ai automation script I used to escalate complex issues to human agents.
What were the different natural language processing techniques I applied to improve the fliqr-ai chatbot's understanding?
Find the code snippet I used to handle customer complaints in fliqr-ai.
Summarize the A/B testing results for different fliqr-ai chatbot response variations.
Show me all the fliqr-ai conversations related to a specific marketing campaign.
Retrieve the fliqr-ai experiment I ran to evaluate the effectiveness of different customer support strategies.
What were the key performance indicators (KPIs) I tracked to measure the success of the fliqr-ai chatbot?
Find the saved search I created to identify customers who have given positive feedback.

DOWNLOAD FOR FREE

Never lose track of your work again. Try AI Memory

Talk to us

Try it free

DOWNLOAD FOR FREE

Never lose track of your work again. Try AI Memory

Talk to us

Get started

DOWNLOAD FOR FREE

Never lose track of your work again. Try AI Memory

Talk to us

Get started