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The Engine

Purpose-trained.
Not general purpose.

Generic AI cannot understand brand vibe. influengine-v3.1 is trained exclusively on influencer marketing data — content patterns, audience behaviours, and campaign performance history.

Model Architecture

Built for one job.
Done exceptionally.

Most AI tools are general-purpose language models given a marketing prompt. Influengine is different — our model was fine-tuned exclusively on influencer marketing datasets: creator content archives, audience demographic matrices, brand–collab outcome data, and engagement authenticity patterns.

The result is a model with genuine domain intelligence — one that understands what "minimal editorial fashion" means in content, knows which creator cohorts drive purchase intent, and can detect bot traffic from organic engagement patterns at scale.

Meta Graph
Creator nodes queried live across the Meta graph — no stale databases.
14 signals
Extracted from every brand brief — tone, audience, format, budget, sector, seasonality and more.
98%
Brand match rate reported by early access partners across their first campaigns.
v3.1
Current model version. Continuously retrained on new campaign performance and creator data.
Core Capabilities

Six matching layers.
One precise output.

01 / 06
Brand Vibe Analysis
Reads tone, aesthetics, and cultural signals from your brief. Maps them to creators who naturally embody your brand without feeling forced or transactional.
02 / 06
Audience Overlap Intelligence
Analyses real demographics, interest clusters, and overlap matrices. Surfaces creators whose audiences actually match your target consumer — not just follower count.
03 / 06
Authenticity Verification
Detects fake followers, bot engagement, and artificially inflated metrics. Every creator in your list has passed automated fraud analysis before you see them.
04 / 06
Campaign Context Matching
Product launch, Diwali campaign, brand story — the model surfaces creators proven to perform in your specific campaign context, from historical outcome data.
05 / 06
Cross-Platform Intelligence
Queried in real-time across Meta's native ecosystem. Platform-specific performance data factored into every vibe score.
06 / 06
Weighted Ranking Model
Combines all signal layers — vibe, audience, authenticity, context, and platform — into a single weighted rank. The top creator is always the best total fit.
Under The Hood

What runs when
you submit a brief.

Every brief triggers a deterministic pipeline. Here's what the model processes — in real time.

influengine-v3.1 — brief processing pipeline
1// Brief received — Zara India SS'25
2brief = parse_input("Zara India, minimal editorial, SS'25, fashion, ₹8L–₹20L")
3signals = extract_signals(brief) // 14 signals extracted in 0.3s
4 
5// Phase 1: Build brand DNA vector
6brand_dna = build_dna_vector(signals)
7// → tone: minimal · sector: fashion · audience: 22–32 urban · vibe: editorial
8 
9// Phase 2: Audience overlap scan across 50K+ profiles
10candidates = scan_profiles(50482, brand_dna.audience_map)
11// → 2,841 candidates pass audience threshold
12 
13// Phase 3: Vibe + authenticity scoring
14vibe_scored = vibe_score(candidates, brand_dna)
15auth_verified = fraud_check(vibe_scored) // removed 134 flagged profiles
16 
17// Final: Rank and return
18result = rank_and_output(auth_verified, top_n=24)
19→ returning 24 matched creators · total runtime: 3h 41m
Training Foundation

The data behind
the intelligence.

Our model is trained and continuously updated on real influencer marketing data — not generic internet text. This is what domain expertise actually looks like.

Meta Graph
Direct Query Connectivity
2M+
Campaign data points processed
10K+
Campaign outcomes trained on
98%
Match accuracy rate
Live
Zero Stale Data
Daily
Model retraining cadence
 Invite Only

Put the model to work
on your next campaign.

Submit your brand brief and let influengine-v3.1 find the exact creators you've been searching for manually.

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