How It Works

Research you can
actually trust.

While you sleep, Spaces reads 2,400 sources per day, validates problems with data, and generates solutions before you even know you need them.

How a Space gets built.

Four stages. AI does the research, discovery, and generation. A human approves at every gate.

01Define the Space

Every Space starts with a specific person with a specific problem. The Avatar defines exactly who we serve and the daily friction that makes their working life harder than it should be.

Human approves Avatar + frustration profile before research begins.
02Find & Validate Problems

The research engine scans 2,400+ sources daily — forums, X, job boards, changelogs — and scores problems on a 0–100 pain index. Members can also submit problems directly, bypassing the filter.

Human selects which validated problem to solve next.
03Build the Knowledge Base

A solution blueprint (Recipe) is designed. Deep Research runs wide on the answer space. All findings become the Space's private Knowledge Base — the grounding source that ensures AI responses are accurate and tool-specific, not drawn from generic internet data.

Human reviews the Recipe blueprint and spot-checks the KB before generation.
04Create the Course

The Course Generator writes every lesson, challenge, and AI mentor prompt from the grounded KB only. Every statement is source-traceable. Hallucination is contained at architecture level.

Human reviews and approves all content before it goes live.
Pulse

The Space keeps learning after you join.

Once a Space is live, the pipeline doesn't stop. Pulseautomatically fires research workflows — based on the Space Avatar and settings — scanning for what's new, what's changed, and what members need to know right now.

When a tool ships an update, when an API changes, when a new capability drops in your stack — the Space surfaces it before you even know to ask. Your AI mentor knows the latest. Always.

Automated workflows fire from Space Avatar settings
Scans 2,400+ sources daily — relevant ones only
Curators can add their own intel at any time
Delivered as a briefing — not a notification flood
Pulse flow
Avatar + Space settings define research scope
Automated workflows scan sources continuously
AI synthesises findings into a briefing
Human reviews, enriches, or adds intel
Delivered to members — tool-specific, role-relevant

Three steps. Zero guesswork.

Enterprise consulting firms charge $15,000 for this. Spaces delivers it automatically. Updated daily.

1

Scan

2,400 sources scanned daily

Forums, X, Reddit, job boards, industry reports, SEC filings, patent databases. Monitored 24/7.

2

Validate

7-point evidence scoring

Pain score 0–100 based on frequency, intensity, urgency, and 4 other criteria. Not opinions. Data.

3

Solve

Solutions in 47 seconds

Courses, blueprints, automations. AI-generated in under a minute, human-reviewed within 24 hours.

The difference is time.

Staying UninformedUsing Spaces
Time to spot threat6–12 months24 hours
Sources monitored0–52,400+ daily
Solution pathsGuessCurated
CostCareer riskFree to start
From Real Professionals

What they're saying

Finally, someone is talking about the REAL problems with AI & automation, not the hype. This is exactly what I needed.

Sarah K. · Marketing Director

I was nodding along in AI meetings, hoping nobody asked me a question. Now I actually understand what's happening with AI & automation in my industry.

David M. · Project Manager

Used a problem we found here to pitch a new product line. Got the green light. Oracle paid for itself 100x.

James T. · Product Strategist
From the team behind 30,000+ trained professionals

Where do you want to go next?

We've built this for three kinds of people. Pick the path that fits.

I'm an individual professional

I want an AI mentor for my tools

Pick the Space that matches your role. Your AI mentor is ready immediately.

Browse Spaces
I manage a team

I want to keep my whole team current

See how teams roll this out — and what it replaces in your current L&D stack.

Team options
I'm technical or skeptical

I want to see the engineering before I commit

Read the full story — the architecture, the AI patterns, and why we built it this way.

How we built it

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