Flashpoint.AIFlashpoint.AIdocs

Surveys

Flashpoint.AI runs end-to-end survey research from a natural language objective — design, recruit, field, analyze, deliver. The same surface serves human researchers and autonomous agents.

What we do

Hand us a research objective in natural language — any language. We plan a multi-step workflow, execute the steps, and return the artifacts: the questionnaire, the recruited panel, the response data, the analysis, the deck. No survey software to learn.

How it works

A single objective decomposes into a typed plan. Steps execute in dependency order — some immediately (questionnaire design), others queued (synthetic fielding, deck generation). Outputs from one step flow into later steps by reference, so a panel created in step one is consumed by step two with its real ID. Artifacts stream back as steps complete. Destructive actions — publishing, sending an email blast, excluding responses — pause for explicit approval before they run.

The two ways in

PathWhen to use
Ask the agentOpen a chat, describe what you want. The agent plans, executes, narrates, and asks for confirmation on anything destructive. Read Ask the agent.
Call the APISame model, same data, every action behind a REST endpoint. Pair it with the agent or use it standalone. Read the API Reference.

Capabilities

AreaWhat it coversRead
Build20+ question types, blocks, skip logic, piping, randomization, translations, validationBuild · Question types · Skip logic
QuotasTotal and conditional quotas with real-time enforcement, monitoring, reconciliationQuotas
LifecycleDraft, publish, pause, complete, clone — plus versioning that lets you edit a live survey without disturbing in-flight respondentsLifecycle
TemplatesPre-built survey designs by category; save any survey as a reusable templateTemplates
SampleAI-generated synthetic panels, Prolific, Dynata — with per-panel segmentation across the pipelineSample · Synthetic · Prolific · Dynata
DistributePublic link, email lists with tracking and resend, panel provider recruitmentDistribute · Email
AnalyzeFrequencies, crosstabs, NPS, chi-square, segment filtering, AI-powered insightsAnalyze · AI insights · Data quality
Import & exportDOCX import (AI programs the survey), CSV, XLSX, PDF, Confirmit XML, Qualtrics QSF, Forsta XMLImport & export
Ask the agentChat-driven everything with approval gates for destructive workAsk the agent

Model

The core objects you'll see across the API and the chat artifacts:

  • survey — the long-lived container. Has a name, a status (draft / active / paused / completed), and a current_version pointer at the live questionnaire.
  • survey_version — an immutable snapshot of the questionnaire document at a point in time. Editing a published survey creates a new version; the live one keeps running until you push.
  • response — a single respondent's answers. Always references the survey_version they took, so renaming Q4 next month doesn't rewrite history.
  • panel — a source of respondents. Open links, email lists, synthetic personas, or external panel providers (Prolific, Dynata) each get a panel_id and you can segment analysis by it.
  • workflow — a typed multi-step plan the agent generates from an objective. Each step has a tool, inputs, dependencies, and produces artifacts (the questionnaire, the deck, etc.).

Conventions

  • Every resource ID is a UUID. Routes are stable: /api/v1/surveys/:id.
  • Destructive actions (publish, send, exclude, complete) emit approval-gated tool calls when invoked through the agent.
  • All artifacts stream back over the chat connection while they're being produced, and persist to the chat history when they're done.

Next steps