Quera – AI-Powered Survey and Feedback Platform

Overview

Quera is an AI-driven platform specializing in the creation, distribution, and analysis of surveys and feedback mechanisms. Established in 2017, the platform targets organizations seeking to gather structured and unstructured feedback across various domains, including customer experience (CX), employee engagement, market research, and product development Quera homepage. Its core functionality is built upon an AI engine designed to process and interpret open-ended text responses, providing capabilities such as sentiment analysis and thematic categorization.

The platform is engineered to automate aspects of qualitative data analysis, which traditionally requires manual review. By applying natural language processing (NLP) techniques, Quera aims to identify patterns, recurring themes, and sentiment within large volumes of textual feedback, thereby reducing the time and resources typically associated with manual data coding Quera documentation. This approach is intended to provide actionable insights from verbatim comments, which can be critical for understanding nuanced opinions and identifying underlying issues or opportunities.

For technical users, Quera provides an API for programmatic integration, enabling developers to connect survey data with existing business intelligence (BI) systems, customer relationship management (CRM) platforms, or internal analytics dashboards Quera API reference. The availability of SDKs for multiple programming languages (Python, Node.js, Java, Ruby) supports diverse development environments. A sandbox environment is also provided for testing integrations prior to deployment, which can assist in development and debugging workflows.

Quera positions itself as a tool for enhancing decision-making processes by offering real-time feedback collection and analysis. This can be particularly beneficial for agile product development cycles or ongoing customer experience monitoring, where timely insights are paramount. The platform's compliance with data protection regulations such as GDPR and CCPA is intended to address concerns regarding data privacy and security when handling sensitive feedback information.

The utility of AI in survey analysis, particularly for open-ended questions, is a developing area. While traditional quantitative surveys provide structured data, qualitative feedback from open-ended questions can yield deeper insights into user motivations and perceptions. Platforms like Quera aim to bridge the gap between the richness of qualitative data and the scalability of quantitative analysis by automating parts of the interpretative process CXL on qualitative vs quantitative research. This allows organizations to move beyond simple numerical ratings to understand the 'why' behind customer or employee responses.

Key features

• AI-powered Survey Platform: Tools for designing and deploying surveys, with an emphasis on incorporating AI for data processing.
• Sentiment Analysis: Automated identification of emotional tone (positive, negative, neutral) within open-ended text responses.
• Open-ended Question Analysis: Capabilities to extract themes, keywords, and insights from free-form text answers using natural language processing.
• Real-time Feedback: Mechanisms for collecting and analyzing survey responses as they are submitted, enabling immediate decision-making.
• Customizable Survey Logic: Features for creating dynamic survey paths based on respondent answers, including skip logic and branching.
• Reporting and Dashboards: Visualization tools for presenting survey results, trends, and AI-driven insights in a digestible format.
• API and Webhooks: Programmatic access to survey data and events for integration with external systems, supporting real-time data flow Quera API documentation.

Pricing

Quera offers a tiered pricing model, including a free plan for limited usage and various paid subscriptions. As of May 2026, the details are as follows:

For the most current pricing information and detailed feature breakdowns, refer to the official Quera pricing page.

Common integrations

• CRM Systems: Connect with platforms like Salesforce or HubSpot to link survey responses to customer records Quera integration guides.
• Analytics Platforms: Integrate with Google Analytics or other BI tools for deeper data exploration and correlation Google Analytics documentation.
• Marketing Automation: Feed survey insights into marketing automation platforms to personalize campaigns or segment audiences.
• Collaboration Tools: Push feedback alerts or reports to Slack, Microsoft Teams, or other communication platforms.
• Data Warehouses: Export survey data to data warehouses for long-term storage and complex queries.

Alternatives

• Qualtrics: An experience management platform offering advanced survey capabilities, research tools, and analytics for CX, EX, and product feedback.
• SurveyMonkey: A widely used online survey tool providing a range of question types, templates, and basic analysis features.
• Typeform: Known for its conversational and aesthetically pleasing survey interface, focusing on user engagement and completion rates.

Getting started

To begin using Quera's API, you typically need to obtain an API key from your Quera account dashboard. The following Python example demonstrates how to create a new survey programmatically using the Quera API. This example assumes you have the requests library installed.

import requests
import json

API_KEY = 'YOUR_QUERA_API_KEY_HERE'
BASE_URL = 'https://api.quera.ai/v1'

headers = {
    'Authorization': f'Bearer {API_KEY}',
    'Content-Type': 'application/json'
}

def create_survey(title, description, questions):
    endpoint = f'{BASE_URL}/surveys'
    payload = {
        'title': title,
        'description': description,
        'questions': questions
    }
    try:
        response = requests.post(endpoint, headers=headers, data=json.dumps(payload))
        response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
        return response.json()
    except requests.exceptions.HTTPError as http_err:
        print(f"HTTP error occurred: {http_err} - {response.text}")
    except Exception as err:
        print(f"An error occurred: {err}")
    return None

# Define survey questions
survey_questions = [
    {
        'type': 'text',
        'text': 'What are your initial thoughts on our new product feature?'
    },
    {
        'type': 'rating',
        'text': 'How would you rate your overall experience (1-5)?',
        'options': ['1', '2', '3', '4', '5']
    }
]

# Create the survey
new_survey_data = create_survey(
    'Product Feature Feedback',
    'Gathering feedback on the recently launched product feature.',
    survey_questions
)

if new_survey_data:
    print(f"Survey created successfully with ID: {new_survey_data.get('id')}")
    print(f"Survey URL: {new_survey_data.get('survey_url')}")
else:
    print("Failed to create survey.")

This script initializes a request to the Quera API's /surveys endpoint, passing the necessary authentication header and a payload containing the survey's title, description, and an array of question objects. The create_survey function then sends a POST request and handles potential HTTP errors, returning the JSON response from the API if successful. For detailed API specifications and error handling, refer to the official Quera API reference documentation.