AI Research Assistant

Meet AddMaple's Gen AI Research Assistant

AddMaple is an exploration-first analytics platform: AI accelerates your work where it genuinely helps—not to replace your thinking, but to move faster with evidence-backed outputs. For the full picture of how AddMaple AI differs from generic tools, see AI data analysis.

Why AddMaple AI is different

Most AI tools generate answers from language patterns alone. AddMaple's AI interprets actual statistical relationships, significance tests, and pivot outputs from your dataset—so you get faster analysis without guesswork or hallucinated conclusions. AI sits on top of a statistics engine, chart engine, and pivot engine: real analytical objects you can click into, edit, and keep exploring.

What you can do

• Analyze text (reviews, open-ends, social, tickets, and more): Analyze sentiment, re-analyze for user intent, and more. Prompt the assistant to generate codes or use your own, then apply them across hundreds or thousands of records.

• Summarize large quantities of text: Get key points with examples and highlights—including nuanced, unusual, or interesting ideas from complex text.

• Write interpretations for charts and tables: Ask it to explain why a table or chart is interesting and what is noteworthy, to speed up report writing.

• Enrich your data: Let Gen AI suggest categories, then classify your data—for example group countries into continents, companies into industries, or artists into genres.

• Protect your data privacy: Opt in at the column level, not for the entire dataset. Your data is not stored by our AI and is not used for training.

Thematic coding (open ends → structured variables)

Turn unstructured text into structured insight. AddMaple can code thousands of open-ended responses into themes and sub-themes from your research goal and the verbatims in your dataset.

1. Tell AddMaple what you want to understand
2. AddMaple generates a suggested codebook
3. Edit and confirm it
4. Apply it across the full dataset instantly

You get a full codebook with descriptions and verbatim examples, transparent coding with highlighted traceability, multi-tagging, sentiment per theme, and sub-theme deep dives via multiple coding rounds. Once coded, themes behave like any other column for filters, pivots, and statistical tests.

Verbatim Q&A (without a codebook)

Not every project needs a codebook. Interrogate a verbatim column with follow-up questions—even on filtered subsets. Examples: what a segment says about a topic, what complaints increased, what promoters or detractors mention most. It is qualitative exploration with context, not generic summarization.

Maple agent: questions → analysis, not just text

Maple does not only reply in text—it runs analysis and returns evidence-backed answers with pivots, charts, and breakdowns you can open and edit. It is grounded in AddMaple's statistics engine, can call out under-represented groups so you do not overinterpret noise, and links directly to pivots and charts. Coming soon: Maple can build dashboards for you automatically.

Assisted cleaning

Use AI to clean question wording into readable labels, bucket long category lists, merge categories for clearer charts, reorder scales, and standardize labels—everything stays editable and transparent.

Coming soon: An AI copilot to answer your data questions directly in the product.