How does the AI decide what needs a response?
Our AI uses natural language processing (NLP) to understand the user's intent to receive a response, or in other words, it finds out if the user is looking for a reply from your brand. The AI is trained to detect various types of conversational intent.
It walks through how messages are flagged for response, how prioritization works within the Smart Inbox, and how your team can take action based on AI recommendations.
Which AI models do you use?
We use a combination of our own proprietary AI model, trained over 11+ years on billions of social media conversations, and trusted third-party tools for specific use cases.
Our in-house model is built specifically for social—capable of understanding slang, sentiment, emojis, and fast-moving trends with high accuracy.
We also integrate third-party models for specialized tasks:
OpenAI & Gemini: Power one-click AI-generated replies, captions, and chat-based analysis through features like Astra the Analyst
Translation models: Enable real-time multilingual moderation
What is sent to Astra?
The data Astra receives depends on which tab or workflow you're using it from within the BrandBastion platform.
In Analyze > Listen:
Astra receives:
All data from the dashboard:
Overview
Sentiment Details
Topics
A sample of 200 recent comments
What’s excluded:
Private messages
Comments with only user tags
Username-level data
Messages tagged as PII or spam (filtered using BrandBastion’s advanced tagging)
YouTube data
This process ensures performance and relevance at scale:
It leverages pre-analyzed dashboard insights and the curated sample.
For instance, sentiment distribution, platform breakdowns (e.g. Facebook vs. Instagram), and top tags still reflect the full dataset, not just the sample.
This approach avoids inefficient raw analysis and relies on BrandBastion's structured tagging system to guide Astra’s topic identification.
Why only 200 comments?
Astra works best when the input is meaningful and condensed. A structured input with summaries, tag distributions, and curated highlights is far more effective than a massive, noisy dataset.
In Posts tab:
Astra receives data from the most recent 1,000 posts, including fields like:
Platform
Post copy
Date
Format (Image, Video, etc.)
Engagement metrics:
Net Sentiment Score (NSS)
ER by Impressions and Reach
Reactions, Shares, Comments
Comment breakdown (positive, negative, neutral, hidden, brand, tagged)
Post type and Tags
This structured post-level input helps Astra:
Compare engagement across platforms and formats
Understand comment sentiment trends at post level
Surface insights tied to specific post tags (e.g. campaign or topic-level analysis)