Articosv2.0.0.0
Overview
Articos is an AI-driven platform for user research and interviews. It uses AI to turn user ideas, landing page designs, and messaging into structured audience conversations, providing valuable insights with high speed and low overhead. It does this without the need for user sourcing or the long wait times typically associated with traditional research methods.The platform focuses on delivering clear insights within minutes, using features like simulated interviews and landing page testing. Users describe their research goals, choose a path of investigation through interviews or landing page tests, and receive insights quickly. Articos generates insights on user motivations, objections, confusion points, and language patterns, shaped into actionable guidance.
Articos is built for teams such as agencies looking for stronger pitches and evidence-backed creative work, SaaS teams validating product ideas, messaging, and onboarding flows, and growth and marketing teams testing landing pages, campaign angles, and positioning. The insights help users improve client acquisition, streamline product development, and optimize marketing strategies.
Articos also lets users set research goals and define target audiences quickly, simulate audience conversations based on briefs, test and analyze landing pages, and generate summarized, actionable insights. These features significantly reduce the time and cost of traditional research. Articos offers subscription plans that support different workflows and needs, providing flexibility for a wide range of users.
Releases
v2 is a major platform rebuild introducing A/B testing research, and AI-powered persona interviews via Cloud Tasks, a structured report pipeline with charts and citations, deep persona generation, and a fully redesigned UI. The backend was migrated to Python across all primary Cloud Functions.
New Research Type: A/B Testing
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- New ab-test research type for comparing two variants (e.g., landing page A vs. B)
- Variant preview section with live browsing animation during interview setup
- A/B comparison sections in the report with side-by-side analysis
- Screenshots of each variant proxied through Firebase Storage
- Goal selection limited to a single goal per study
- Auto-naming support for A/B test research
AI Persona Interviews (Step 4)
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- Persona interviews now triggered via Cloud Tasks (fully async)
- Full interview status lifecycle: not-started > setting-up > in-progress > completed
- Interview questions chained via previous_response_id for coherent multi-turn conversations
- Research context and roleplay prompt injected into follow-up answers
- Medium reasoning effort enabled for interview AI
- View Transcript and Follow Up Question buttons on completed interview cards
- Preparing the state shown on the interview card during the setup phase
- Live browsing animation while AI interviews are running
Structured Report Pipeline (Step 5)
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- Full structured report UI with charts, citations, and evidence drawers
- Report sections written in parallel for faster generation
- Pipeline resumes from the last completed stage on re-entry (no restart)
- Progressive loading states with skeleton UI and auto-scroll
- TOC sidebar with section navigation and ellipsis truncation on long titles
- Expert critique sections with correct prompts per research type
- A/B variant screenshots labelled as "Variant A" / "Variant B" in the report
- Report download and share buttons with PDF export hook
- Public report endpoint updated to serve structured pipeline reports
- Section IDs sanitised for Firestore paths
- Comparison matrix columns aligned when LLM includes label headers
Deep Persona Generation (Step 2)
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- Progressive persona loading -- each profile updates independently as it completes
- Geocoding and dynamic persona attributes (country flags, world map pins)
- Archetype field rendered in persona card header
- OCEAN personality scores normalised to a 1-100 scale-
- Location and origin country rules added to the deep persona prompt
- Global location diversity enforced (no US default)
- Radar chart with secondary attribute dimensions from generate_relevancy_dimensions
- Persona card bio tooltip showing full text
- Edit persona modal hides AI refinement section when step is completed
- Edit/delete buttons disabled on persona cards after step 2 is completed
- Generation state persisted across step navigation
Auto Study Type Detection (Step 1)
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- Research type (idea-validation, landing-page-feedback, ab-test) now auto-detected from conversation -- no upfront selector
- inferredStage returned from backend and consumed by frontend
- Conversation saving shifted from backend to frontend
- Profile selection is disabled after step completion to prevent race conditions
- Research names no longer include _study{n} suffix
- Row-based roles UI replaces card grid on ProfileCard
UI / Design System
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- Full platform UI revamp (Step 1, Step 2, Step 3 redesigns)
- Design tokens replace hardcoded colors and arbitrary spacing values
- ActionTooltip component added and applied across the platform
- Follow-up chat panel restyled to Articos warm gold/cream design system
- Follow-up sidebar collapses on open
- Accessibility: aria-label added to all icon-only buttons
- Scatter chart uses dynamic domain; persona bio filter fixed
[v0.2.8] and earlier
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Legacy releases prior to the v2 platform rebuild. See git history for details.
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Automated user research interviews that run and analyze themselvesPaul🙏 28 karmaMar 19, 2026@Askiva AIWhat you’re describing is interesting, but also risky. If users stay for insights but you position around ops efficiency, you’re likely attracting the wrong icp and underpricing the real value. In similar cases, that creates a significant revenue ceiling. Have you tested leading with insight speed / decision advantage instead of scheduling pain? -
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