Turn a decision into research you can defend.
PolitiMind AI is not a question generator. It interrogates your research objective the way a senior methodologist would — validity, feasibility, targeting, cost, interpretation — and resolves it into a structured study specification a human reviews and approves.
The problem
Generic AI writes questions. That was never the hard part.
Drafting survey wording is the cheapest step in research. The expensive failures happen earlier and later: measuring the wrong construct, choosing a population you cannot actually reach, and producing a number nobody can interpret once it lands in front of a decision-maker.
AI tools that skip straight to writing questions accelerate the part that was already easy, and quietly remove the review step that caught the rest. Faster output, same defect rate, less scrutiny.
What is structurally different
The assistant's job is to make the study harder to fault
PolitiMind AI is built to add friction where friction is valuable. It asks fewer questions than a generic chat tool, and each one exists because the answer changes whether the study is valid, feasible, targetable, affordable, or interpretable.
What it emits is a specification, not a paragraph: population, instrument, and reporting plan, versioned and editable, with every field labeled by where it came from. Design review then argues against the draft before the field does.
From a decision to an approved study specification
- 01
State the decision
You start with the decision you have to make, not with a list of questions.
The objective is captured as structured intent — what will be decided, for whom, and what evidence would change the call.
- 02Beta
Get interrogated, not autocompleted
PolitiMind AI asks fewer, higher-value questions about validity, feasibility, targeting, and cost.
Questions are the ones a senior methodologist would ask: is this measurable, is the population reachable, will the result be interpretable, and what will it cost to find out.
- 03Beta
Produce a study blueprint
The session resolves into a structured specification: population, instrument, and reporting plan.
The blueprint is a specification, not prose. It is versioned, editable, and reviewable before anything is fielded.
- 04Beta
Challenge the premise
Design review surfaces leading wording, loaded framing, and assumptions the objective smuggled in.
Counter-bias and pre-mortem review run against the draft so weaknesses are found before fielding rather than argued about after publication.
- 05
Human review and approval
Nothing fields itself. A person reviews the draft, edits it, and approves it.
Every field in the blueprint carries provenance — user supplied, team default, AI recommended, or manually edited — so authorship is never ambiguous.
Provenance
You can always tell who wrote what
Every element of a blueprint is attributed. When a result is challenged months later, the question of who chose the wording has a recorded answer rather than a reconstruction. That attribution is also the boundary of the AI's authority: it recommends and it critiques. It does not field studies, weight results, or publish numbers.
Study blueprint — attribution view
- User supplied
- Team default
- AI recommended
- Manually edited
- Objective
- The decision the study has to support
- User supplied
- Population
- Who must be measured for the result to be interpretable
- AI recommended
- Instrument
- Question set, order, and response scales
- AI recommended
- Screeners
- Qualification into the study
- Team default
- Wording
- Revised after design review flagged leading framing
- Manually edited
- Reporting plan
- How the result will be cut and reported
- Team default
Illustrative attribution view. Field labels show the shape of a blueprint, not its contents, schema, or the review logic behind it.
What the AI layer is allowed to do
- BetaAI-assisted research design turns a decision into a defensible study: structured objectives, question design review, and bias/feasibility checks before fielding.
- BetaThe platform supports advanced research designs including trackers, recontact studies, experiments, MaxDiff, conjoint, and pre/post measurement.
- Methodology is stated in plain language — sampling frame, weighting approach, margin of error, and known limitations travel with every result.
- Published results carry provenance: the specification, method version, and computation run behind a number are recorded and inspectable.
AI assistance improves structure and surfaces weaknesses; it does not establish truth. No model output is treated as evidence, no study fields without human approval, and design review reduces the chance of a flawed instrument rather than guaranteeing a correct answer. Poll Architect and design review are in beta and available to selected research accounts.
What backs this up
AI-assisted research design turns a decision into a defensible study: structured objectives, question design review, and bias/feasibility checks before fielding.
See where design sits in the lifecycleThe platform supports advanced research designs including trackers, recontact studies, experiments, MaxDiff, conjoint, and pre/post measurement.
Methodology is stated in plain language — sampling frame, weighting approach, margin of error, and known limitations travel with every result.
Open a published resultVerified audiences and automated fielding compress the time between a research question and a defensible answer.
Economic value
What you stop paying for
Senior methodologist time is the scarcest resource in research, and most of it is spent on the same recurring review: is this the right construct, is the population reachable, is this wording leading, will anyone be able to act on the output.
Moving that review earlier and making it repeatable means fewer studies re-fielded, fewer results that collapse under scrutiny, and senior judgment spent on interpretation instead of cleanup.