AI is powerful because it's flexible. Flexible output is illegible and hard to trust.
Hercules turns a raw research prompt into a fully realized survey. Almost every problem below is the same tension: build structure around the AI's flexibility without flattening it into rigid text walls. As Hercules grew into an enterprise product, that instinct reached targeting, billing, and navigation too.
A researcher describes what they want to learn, in plain language.
Step 1 of 5
Active, ongoing work on a live product, not a completed, measured case study. Nothing below claims a validated before/after number.
Six problems, one instinct: give the AI structure without taking away its flexibility.
Converting raw AI research prompts into structured surveys usually results in rigid, unstructured text walls that lack clarity and flexibility.
The callA suite of question artifact cards, built for AI generation now and manual editing later.
Standard and multi-select inputs, rating scales and matrices, heatmaps and signatures, and attention-sustaining “break screens,” architected in a structured, disabled state for real-time generation, with the design system already laid for direct editing.
Setting up multi-tiered survey routing (skip logic, branch filtering, terminations) traditionally requires complex, error-prone visual flowcharts or code.
The callLogic set up in chat, inspected question by question.
A dual-view workspace pairs the chat with a live “Logics” artifact tab. The same prompt-and-response structure (action selectors, condition builders, summary confirmations) doubles as training ground truth for the AI agent.
Researchers need to narrow down demographic cohorts with high granularity without losing survey context or restarting generation.
The callAn Edit Audience modal with layered filters, and one click to deploy to all.
Instant sample size, geo-targeting, and gender and age splits, layered with NCSS classes and custom behavioral tags, plus a “remove filters & deploy to all” toggle for when precision isn't the point.
Long inference and agent brainstorming latencies leave researchers wondering if the system has stalled or failed.
The callThinking streams that show the reasoning as it happens.
Brainstorming and thinking-stream states map Hercules' step-by-step reasoning and benchmark evaluations in real time: dead air becomes something a researcher can audit, which also softens how long the wait feels.
Monetizing a self-serve enterprise research tool means handling multi-variable credit systems, team limits, and subscription transitions without interrupting campaign setup.
The callA credit shortfall interrupts the checkout, not the research.
The full paywall and checkout across Free, Starter, and Pro and multiple billing cadences, plus credit-state modals for insufficient credits, large-deploy surcharges, top-ups, and prorated upgrades with itemized breakdowns.
As Hercules expanded to handle multiple campaigns, templates, and credit states, the interface needed a scalable navigation framework.
The callA clear sidebar hierarchy, and one dashboard layer for credits and upgrades.
New campaign, chat history, templates, starred and recent work, and direct switching into SuperJ, with credit balance, upgrade entry points, and notifications in one place.
Where Things Stand
Hercules is in active use by internal and enterprise teams at Jupiter Meta Labs. New artifact types, logic conditions, and monetization edge cases are still shipping, so this page reflects where the design stands today, not a final measured outcome.