AWMY — Marketing intelligence

We make marketing predictable.

Turn competitor content, customer signals, and market activity into decisions your team can actually use.

For performance teams · agencies · product companies

Early access
No.01 The right infrastructure never existed

You can find data. You can’t find meaning.

Teams have access to more marketing data than ever, yet critical decisions still depend on assumptions, isolated reports, and incomplete context.

01

Fragmented discovery

Insights appear in isolated channels — communities, competitor campaigns, customer reviews. Most teams never see the full picture.

02

Structural blind spots

Teams optimize within their own category and rarely observe adjacent markets where new patterns and expectations emerge first.

03

Research doesn’t scale

Manual research is expensive, inconsistent, and hard to repeat. Discoveries disappear into docs, chats, and individual expertise.

No.02 Who it’s for

Built for teams that run on marketing decisions.

01 Primary

Performance marketing teams

  • Heavy dependence on paid acquisition
  • Constant creative iteration
  • Need for faster market feedback loops
02 Secondary

Brand and full-service agencies

  • Cross-industry intelligence requirements
  • Continuous need for fresh insights
  • Pressure to justify strategic recommendations
03 Tertiary

Product companies

  • Strong internal product expertise
  • Limited cross-category visibility
  • Need to identify emerging opportunities earlier
No.03 The product

Every module answers a different strategic question.

Combined, they reveal opportunities invisible to traditional analytics tools.

Module 01 Live

Organic analysis

Customer intelligence from organic conversations.

Sources
Reddit Threads TikTok Quora YouTube
Outputs
  • Pain maps, personas, voice of customer
  • Buying signals, trend detection
  • Weekly digests, Airtable integration
Module 02 Live

Competitor analysis

Structured analysis of competitor marketing activity.

Sources
Meta
Outputs
  • Creative collection, hook & narrative analysis
  • Scaling & shutdown tracking
  • Weekly digests, Airtable integration
Module 03 Soon

Customer voice

Deep analysis of existing customer feedback.

Sources
App Store Trustpilot Interviews
Outputs
  • Review & interview analysis
  • Motivation mapping, churn signals
  • Messaging & product opportunities
Module 04 Soon

Overlap analysis

Market opportunity detection across datasets.

Sources
Cross-module
Outputs
  • Demand vs supply, competitive gaps
  • Blue & red ocean detection
  • Strategic & tactical recommendations
No.04 How it works

Why not just use ChatGPT?

Large language models generate answers from available information. They do not build structured market intelligence systems by default.

One engine. Five layers.

The system combines data collection, normalization, market mapping, signal extraction, and analytical interpretation into a single research workflow.

01 Collect

Tens of thousands of creatives, licensed Reddit, reviews — data access closed to base models.

02 Methodology

Behavioral economics, cognitive psychology, game theory. A model has no method — we give it one.

03 Model ensemble

Claude · GPT · Gemini · DeepSeek + fine-tuned. We depend on none.

04 Verification

Hallucination detection at generation signal level — not just cross-checks.

05 Interpretation

The hidden drivers behind a decision — the real why surveys never reveal.

No.05 The team

The people behind the methodology.

Execution
Vitaliy
CEO

NLP Engineer, Tech Lead, 3x founder.

LinkedIn
Integration
Oles
COO / CPO

7 years engineering, 2x founder.

LinkedIn
Methodology
Iovana
CGO

17 years marketing & revenue systems, 4x founder.

LinkedIn
Architecture
Oleksandr
CTO

EPAM, Monobank, Superhuman (ex Grammarly).

LinkedIn