AI grounded in statistics, built for real analysis

Not in the risky sense. AddMaple's AI is grounded in AddMaple's statistics engine and chart engine. Instead of inventing conclusions, it interprets real pivots, real calculations, and real statistical tests that are run programmatically across your dataset. This keeps results evidence-based and traceable.

Generic AI tools can summarize text, but they do not understand your dataset as a structured analytical system. AddMaple AI works inside an analytics engine. It can interpret quantitative results, explain significance-tested pivots, guide analysis, and create charts and tables you can click into and edit. It is AI designed for analysis, not just writing.

Maple does more than answer in text. When you ask a question, Maple can run deep analysis such as pivots, breakdowns, key driver exploration, segmentation logic, clustering, and statistical testing. It then links you directly to pivot charts and tables you can explore and edit.

Maple is guided by AddMaple's statistics engine. That means it can highlight things like under-represented groups, weak sample sizes, or relationships that are not statistically meaningful. It helps prevent false insight and keeps exploration focused on what is real.

Yes. AddMaple's AI does not produce locked outputs. It uses AddMaple's chart engine to create real pivots and charts, and you can edit them like any other analysis view. You stay in control of the workflow.

Yes. AddMaple supports full thematic coding for open-ended responses. AI generates a suggested codebook grounded in your verbatims, including definitions and real examples from the dataset. You can edit the codebook, then apply it across thousands of responses. Themes can then be filtered, pivoted, and statistically tested like any other variable.

Completely. Every coded response is traceable back to the original text. AddMaple highlights the exact part of each verbatim that triggered a code, so you can validate results and avoid black-box assumptions.

Yes. AddMaple supports multi-tagging, meaning a single response can be assigned multiple themes when appropriate. This is essential for real-world qualitative analysis.

Yes. AddMaple can attach sentiment per theme, helping you understand not only what topics are being mentioned, but whether they are being mentioned positively or negatively.

No. If you do not want to build a codebook, you can explore a verbatim column using AI Q&A. This lets you ask follow-up questions and explore what people are saying, including within filtered segments.

Yes. AddMaple AI can explain pivots, charts, and significance-tested comparisons in clear language, helping you quickly interpret results and communicate findings to others.

Yes. AddMaple automatically detects question types and structures your dataset for analysis. It also supports AI-assisted cleaning and configuration, including relabeling questions, merging categories, and turning messy category lists into useful buckets for segmentation and reporting. Everything remains editable.

No. This is one of the biggest differences between AddMaple and other AI analytics tools. AddMaple AI is built on top of a statistics engine and chart engine, so every output is inspectable, editable, and grounded in real calculations.

Yes. AddMaple is designed for real survey analysis across market research, customer experience, product research, UX, and employee experience datasets, especially when you need to combine quantitative and qualitative insight in one workflow.

Just connect your dataset and start exploring. Once your data is loaded, AddMaple automatically summarizes every question into charts and tables instantly. From there you can filter, pivot, compare segments, analyze text with AI, and build dashboards as you go. Book a demo.