Documentation Index
Fetch the complete documentation index at: https://cernio.gadulabs.com/llms.txt
Use this file to discover all available pages before exploring further.
Pricing Philosophy
The pricing model must balance three goals:
-
Low barrier to entry — Small exporters must be able to try the platform without financial risk.
-
Alignment with AI cost — AI discovery queries generate real cost through:
- LLM calls
- search APIs
- enrichment processing
Pricing must reflect usage.
-
Simple purchasing decision — Exporters should understand pricing within seconds.
Therefore the model chosen is:
Hybrid SaaS + Credit Model
Why Not Pure Subscription?
Many SaaS platforms offer unlimited usage subscriptions.
This model does not work well for AI products.
Example problem:
heavy users generate massive AI costs
For example:
| user | searches |
|---|
| light user | 3 per month |
| heavy user | 200 per month |
Why Not Pure Credits?
Another option is a pure credit system.
However this creates friction:
Users constantly think about cost.
Example:
“Should I spend this credit?”
This reduces product usage.
A hybrid model solves this.
Hybrid Pricing Model
The chosen structure:
Base subscription
+
Usage credits
- predictable revenue
- controlled AI cost
- low psychological friction
Plan Structure
Three plans are sufficient.
| plan | target user |
|---|
| Free | trial users |
| Pro | individual exporters |
| Team | export teams |
Free Plan
The purpose of the free plan is activation, not revenue.
Limits (updated April 2026 — R0-8 decision):
| feature | limit |
|---|
| searches | 5 per month |
| companies per search | 10 |
| contact reveals | 0 (completely gated — Pro required) |
| saved leads | 10 |
| card scans | 5 |
Pro Plan
This is the core commercial plan.
Example limits:
| feature | limit |
|---|
| searches | 50 |
| companies per search | 25 |
| contact reveals | 100 |
| saved leads | unlimited |
| card scans | unlimited |
- AI company analysis
- follow-up reminders
Team Plan
For small export teams.
Additional features:
| feature | description |
|---|
| multi user | team accounts |
| shared leads | common workspace |
| activity history | interaction tracking |
| higher limits | more credits |
Credit Usage Model
Credits represent AI compute consumption.
Example actions:
| action | credits |
|---|
| buyer discovery | 1 |
| contact reveal | 1 |
| deep company analysis | 2 |
| market intelligence search | 3 |
Credit Packs
Users can purchase additional credits.
Example packs:
| pack | credits |
|---|
| small | 50 |
| medium | 200 |
| large | 1000 |
Pricing Psychology
Exporters think in terms of:
“How many leads can I generate?”
Pricing should therefore feel like:
“lead generation capacity”
not AI usage.
SaaS Unit Economics
Example cost breakdown per discovery search:
| component | cost |
|---|
| LLM queries | $0.02 |
| web search APIs | $0.03 |
| data processing | $0.01 |
Expected Usage Patterns
Typical exporter usage:
| user type | searches/month |
|---|
| light user | 10 |
| medium user | 30 |
| heavy user | 80 |
Lifetime Value Potential
Revenue per plan (April 2026 confirmed pricing):
| plan | monthly |
|---|
| Pro | 49(39/mo annual) |
| Team | 149(119/mo annual) |
Revenue Expansion
Additional revenue sources include:
- extra credits
- team seats
- intelligence modules
- enterprise features
These expansions increase ARPU.
Monetizing Export Intelligence
Future premium features include:
| feature | price potential |
|---|
| market discovery | premium |
| tariff intelligence | premium |
| competitor analysis | premium |
Pricing Summary
The final pricing architecture:
Free plan → activation
Pro plan → core revenue
Team plan → expansion
Credits → usage scaling
