Pricing is tiered by capability and usage, so you can start small and scale as your VoC program grows. What it means in practice: Choose the plan that matches your data volume, stakeholder needs, and whether you need advanced analytics and reporting.

We use enterprise-grade controls and align with security best practices, including ISO 27001 where applicable to your environment and agreements. What it means in practice: You can provide procurement with clear documentation on access controls, data handling, retention, and audit support.

Implementation is lightweight. You need a CX owner, a data contact, and an internal stakeholder group for actioning insights. What it means in practice: We align goals, connect data, agree taxonomy, then set a regular cadence to review insights and assign actions.

Most teams can start seeing useful themes and insights within days, then improve precision over the first few weeks. What it means in practice: Week 1 focuses on data ingestion and baseline themes. Weeks 2 to 4 focuses on refining taxonomy, dashboards, and action workflows.

Accuracy comes from good taxonomy design, QA workflows, and continuous refinement using your domain language. What it means in practice: You can review theme labels, merge or split categories, and validate examples so outputs stay trustworthy for exec reporting.

Yes. We surface themes and root causes, not just positive or negative sentiment. What it means in practice: You can see which issues are driving detractors, what is breaking key journeys, and what teams should own each fix.

Survey dashboards show scores and basic charts. Ipiphany AI explains the “why” in the text, at scale, and connects it to action. What it means in practice: Instead of reading thousands of comments manually, you get consistent themes, evidence, and a prioritised list of what to fix first.

We analyse feedback from surveys, reviews, complaints, support tickets, and other text-based customer comments. What it means in practice: You can combine multiple sources into one view, compare themes by product, region, or segment, and track change over time.

Ipiphany AI turns customer feedback text into clear themes, sentiment, and root causes so you can prioritise improvements and prove impact. What it means in practice: You upload or connect feedback sources, the platform clusters comments into themes, flags what is driving NPS and complaints, and gives evidence you can share with stakeholders.

Yes. Ipiphany can be configured to match your CX workflows, data sources and goals. We can align models, filters and outputs to your use cases, so your teams get clear answers without extra complexity.

Ipiphany connects through APIs, secure data connectors and simple upload options. You can bring in feedback, reviews, complaints, surveys, chat logs or tickets from your current systems and use AI Search and Overview without changing your tech stack. The setup is designed to be fast, secure and stable.

Most teams can start using core Ipiphany features in an hour with an initial data load and standard setup. More advanced use such as automated data pipelines or tailored models can take a few weeks, depending on your environment. Our team guides you from first dataset through to full rollout so you see value early and keep risk low.