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What is SightX?

SightX is an AI-driven market research platform that helps teams design surveys, analyse results, and create reports without requiring specialist research skills. The platform includes Ada, an AI assistant that guides users through survey creation, data interpretation, and insight generation. It supports multiple research approaches including concept testing, brand tracking, and customer experience studies. The tool handles segmentation, heat mapping, and pricing analysis, then presents findings through visual dashboards. SightX is built for product development and marketing teams who need to gather consumer feedback quickly and make data-informed decisions about campaigns and brand positioning.

Key Features

Survey builder

Create surveys with AI guidance, reducing the need for research expertise

Ada AI assistant

Automated help with survey design, data analysis, and insight generation

Data analysis tools

Segmentation, heat mapping, and statistical analysis with visual outputs

Pricing tools

Built-in features for testing and optimising pricing strategies

Global respondent access

Tap into a network of consumers for research participation

Report generation

Automated creation of reports from survey findings

Pros & Cons

Advantages

  • Reduces time and cost compared to traditional market research methods
  • Makes professional research accessible to teams without research training
  • Combines automation with manual control, so you can adjust analyses as needed
  • Covers multiple research types in one platform rather than using separate tools

Limitations

  • Quality of insights depends on survey design; AI assistance helps but cannot guarantee good research methodology
  • Global respondent pool may have limitations in niche markets or specific geographic regions

Use Cases

Testing product concepts before launch to validate market demand

Tracking how brand perception changes over time among target audiences

Analysing customer experience to identify friction points in user journeys

Optimising pricing by testing different price points with actual consumers

Running campaign post-mortems to measure effectiveness and audience response