do more quant

an AI native platform built to accelerate quantiative analysis and make data driven insights accessible to anyone running UX research.

turn data into insights

access a growing library of 80+ tools to test, describe, visualise and transform data.

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enhanced by ai

Work alongside AI who is there to support you with selecting the right approach and interpreting the results.

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an easy learning curve

Whether talking to the AI assistant or using the no-code graph editor, you can access advanced tools from day one.

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built for speed

dtype's purpose built statistical processing engine is optimised for speed so that you get results fast, even with large datasets.

run analysis on a canvas

an intuitive visual workflow for quickly building quantitative analyses.

deterministic computation

engine generated results

Statistical tests, data transformations and visualisations are generated by our comprehensive library of tools. The agent supports with test selection, explaing results, and coordinating research workflows.

Test selection
Pearson correlation · parametric
Two continuous variables — measuring the strength of their linear association.
Assumptions checked — no methodological concerns.
Alternative considered: spearmansRank
Pre-test checks
Missing data — No missing rows found
Sample size — n = 50000
Normality — One or both variables are strongly skewed/heavy-tailed (skew x=1.61, y=-0.68). Pearson's significance test is CLT-robust at this sample size, but Spearman's rank correlation is less sensitive to heavy-tailed marginals.
Linearity / monotonicity — Relationship well-behaved
automated validation

test selection auditing

For every test the agent requests, a range of checks are preformed to guide the agent and to give you clarity on whether test assumptions and requirements have been met.

Creating research plan
formulate a plan

exploratory research planning

You can instruct the agent to formulate a plan for analysing your data. The agent will conduct explorative analsysis to generate a well informed plan and then implement the research steps for you.

dtype gives you
access to

cap extreme values
scale to a common range
recode labels
group into ranges
compare two groups
compare before and after
compare multiple groups
compare repeated measurements
compare category counts

frequently asked questions

Dtype is an AI native platform built to accelerate quantitative analysis. It is designed for anyone who wants to make data driven decisions, from UX researchers to product managers to business leaders. You can test, describe, visualise and transform data either with an AI assistant or using a no-code graph editor.

Dtype is built for generative and evaluative UX research, with a focus on speed and ease of use. The workflow is centered on analysis, with the creation of reports as the final step in the process. The backend engine does a lot of automated heavy lifitng to convert, encode and transform your data, so that you can stay focused on answering the questions that matter.

Any structured data, such as survey responses, can be analysed. Dtype imports Excel and CSV files.

No, the agentic AI system is purpose built to make running quantitative analysis accessible. Some familiarity with statistics is helpful when working in the graph editor.

Yes, all results can be exported to a Word or PowerPoint file. We have a dedicated agent for taking the results of your work and creating a report in PowerPoint.

Dtype uses a columnar vector database and a purpose built statistical processing engine optimised for speed so that you get results fast, even with large datasets.

No, the graph editor provides a direct way to build analyses without the need for AI.