The product

An engine that reads, scores and predicts.

CURNT ingests creator content in real time, scores every post across four signal layers, and turns raw social output into structured decision-support for brands.

Pipeline

Ingest. Analyze. Decide.

Three stages. Every post from every verified creator flows through them within seconds of going live.

stage_01

Ingest

We continuously sync content across Instagram, TikTok and YouTube. Public data for discovery, verified data via authenticated pulls when a creator connects.

/api/ingest
real-time · webhook + polling
stage_02

Analyze

Each post runs through the CURNT engine. Performance scoring, theme extraction (Claude), sentiment analysis, brand-fit prediction against active briefs.

/engine/score
4 signal layers · 100pt scale
stage_03

Decide

Brands query the engine with a brief. Get ranked creators, ranked posts, and structured reasoning behind each match — explainable, not black-box.

/query
brief → ranked results + reasoning
Signal layers

Four dimensions. One score.

The CURNT Score is not follower count. It's a composite of four independent signal layers, each trained on what actually moves campaign outcomes.

01 · Performance

What's landing

Raw performance normalized by category, platform and creator baseline. Not vanity metrics — the signals that predict campaign lift.

view_efficiency save_rate share_velocity comment_depth
02 · Themes

Content DNA

AI-driven theme extraction across every post. We map what a creator actually makes — routines, reviews, city guides, workouts — not what a hashtag says.

theme_clusters topic_density content_format aesthetic_tags
03 · Sentiment

Audience response

Deep comment analysis. Emoji patterns. Response depth. Whether the audience is buying — or just scrolling past with a heart tap.

sentiment_split emoji_signal response_depth conversion_intent
04 · Brand-fit

Real match

Content-level alignment between what a creator makes and what a brand wants. Trained on live campaigns — sharpens every week.

tone_alignment category_match audience_overlap past_performance
Data model

Three sources. One truth.

Every score is built from a stack of data with different provenance. Brands see which signal came from where.

public_data

Discovery layer Public

Scraped and enriched from Instagram, TikTok and YouTube. Follower counts, public engagement, category signals, growth. Enough to discover — never enough to decide.

verified_data

Truth layer Verified

Real analytics from creators who connect their accounts. Audience demographics, story reach, save rates, saves-to-reach ratios. Ground truth public data can't give you.

campaign_data

Moat layer Campaign

Post-campaign performance across our network. Which creator delivered on brief. At what price. For which brand. This is how the model gets sharper every week.

Query interface

Brief in. Ranked creators out.

Brands describe the campaign in natural language. The engine responds with matches, scores and reasoning — usable in minutes, not weeks.

You describe your campaign

Natural language brief. Audience, category, budget, tone. Two minutes. No structured forms — the engine parses what matters.

Engine returns ranked matches

Each creator scored across all four signal layers. Full reasoning per match. Filtered to your budget range. Ready to reach out — you keep the relationship.

// POST /query { "brief": "Skincare launch, Nordic audience 25-40, wants real-routine content over polished, budget 100k SEK" } // → 200 OK { "matches": [ { "creator": "nova.lindberg", "score": 92, "reasoning": { "performance": 89, "themes": ["skincare", "routine"], "sentiment": "+62% positive", "brand_fit": 94 } } ] }
Get access

Query the engine.

We're onboarding brands into the pilot. Get access to the query interface and start running briefs through CURNT.

Request Demo