Introduce a new "Template" tab within the RingSense conversation overview screen alongside the existing tabs (Transcript, Trackers, AI Coach, Next Steps, Q&As).
This Template tab would allow admins to pre-configure question templates (such as a pre-screen questionnaire or sales scripts) and have RingSense AI automatically extract answers from the conversation based on the transcript/audio data.

Link: https://docs.google.com/document/d/1Y3rHbD1GPO_qq98R4Q10bqypIzUwXWFzG5XImaUHQQg/edit?tab=t.0

How This Differs From Existing Trackers, Q&As, and Other RingSense Features
While RingSense already provides powerful tools like Trackers and Q&As, the Template Tab introduces a fundamentally different workflow and user experience:
Trackers are keyword or phrase-based detections that flag when specific terms appear in conversations. They show where something was mentioned but don't systematically organize responses into structured answers.

Q&As currently allow AI to infer general questions and answers from a conversation, but these are generated post-call based on what the AI finds important — they are not based on a pre-defined, user-driven questionnaire.

Scorecards provide a useful way to see whether specific questions have been asked during the conversation, but they do not capture or display the actual answers to those questions. Instead, scorecards track the presence of certain topics or questions, but they lack the ability to extract concise, one-line responses to each question.

How it Works:
Admin Configuration:
In the Admin Portal, admins can create and manage "Templates" predefined sets of questions relevant to a specific workflow (e.g., pre-screen interviews, discovery calls, qualification calls).

During/After Conversation Analysis:
Once the conversation is processed, the AI will:

Analyse the conversation (audio + transcript).

Match user-configured questions against the conversation.

Extract concise, one-line answers if information is available.

Leave the answer field blank if a question was not answered during the conversation.

User Interface:
A new "Template" tab will be added to the overview screen where users can:

Select a relevant template (if multiple exist).

See each question with the extracted answer.

Download/export the completed Q&A if needed.

Example Use Case:
Recruitment companies conducting pre-screen interviews:

Create a template with standard questions like:

"What is your current salary?"

"What is your notice period?"

"Describe your previous experience."

"Are you open to relocation?"

After an interview, the recruiter simply checks the Template tab to view the summarized answers without manually scanning the transcript.

If a candidate didn't mention salary, that field remains blank.

Why the Template Tab Is Different and Valuable:
Pre-Configured Workflow: Admins can upload a consistent set of questions before the conversation, aligning the AI’s attention to exactly what matters for their specific business process (e.g., recruitment, discovery calls, sales qualification).

Structured Data Extraction: Instead of unstructured highlights, users get a clean, one-question-one-line-answer format, making it much easier to review, export, and take action on the gathered information.

Gaps Are Immediately Visible: If a question is not answered during a call, the corresponding field remains blank, providing clear visibility into missing information without reading the full transcript.

Saves Manual Work: Users don't have to dig through the transcript or rely on ad hoc AI prompts; the important information is automatically captured in an organized format.

Consistency Across Conversations: Ensures critical questions are always tracked and answered consistently, regardless of which rep or interviewer is running the conversation.

Why This Is Valuable:
Speeds up information retrieval:
Users no longer need to manually read transcripts or ask AI externally (e.g., copy/pasting into ChatGPT) to find answers.

Consistency in data collection:
Ensures important business-critical questions are tracked across all conversations.

Saves time and reduces error:
Reduces human error in data gathering from calls and accelerates internal workflows (recruitment, sales, support).

Enhances AI usage within RingSense platform itself:
Keeps users inside the platform rather than needing to export transcripts to third-party tools for Q&A extraction.