NetworkSocial Capital / Network IntelligenceAlpha

HOOS

Human Opportunity Operating System — detects hidden value in networks

HOOS detects Hidden Value inside human networks using Network Theory — Structural Holes, Weak Ties, and Bridging Social Capital — combined with contextual matching. Not a skill-to-job engine; an opportunity detection platform.

Network theory engineStructural holes detectionBridging social capitalContextual opportunity matchingExplainable opportunities5 scoring strategies

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Vision

"A club bartender knows famous athletes. A factory CEO has an unstated need to meet them. HOOS detects the bartender's bridging value, infers the CEO's latent need, and generates a mutual, explainable opportunity."

Development progress40%

StageAlpha

Est. to MVP3 months to runnable dashboard

Suggested pricing$500–$5,000 / org / month

The problem

Why this matters

Hidden value invisible

Network value isn't job titles — it's bridging positions and weak ties.

Title-based matching misses bridging value
Latent needs never surfaced
Opportunities die undiscovered

No contextual matching

Matching engines ignore context (a CEO's latent need to impress his son).

Rigid skill graphs
No need inference
Zero explainability

Social capital unmeasured

Wealth and titles are measured; bridging capital is not.

Weak ties undervalued
Bridgers invisible
No scoring model

The solution

How it works

Network theory engine

Models structural holes, weak ties, and bridging capital explicitly.

5 scoring strategies

Multiple opportunity-scoring algorithms with explainable evidence.

Contextual matching

Infers latent needs from context, not stated profiles.

Explainable opportunities

Every opportunity shows the bridging path and mutual value.

Capabilities

Key features

The core capabilities that make this product distinctive.

Graph Engine

Neo4j + Postgres dual model

5 Scoring Strategies

Multiple opportunity algorithms

Opportunity Matcher

Context-aware matching

Bridging Detector

Finds structural holes

Latent Need Inference

Infers unstated needs

Explainability

Evidence per opportunity

Under the hood

Architecture

Clean Architecture + DDD, Neo4j (graph) + Postgres (transactional)

APIFastAPI + Pydantic

Domain endpoints

DomainPython DDD

Entities, value objects

Graph EngineNeo4j

Network topology & bridging

TransactionPostgreSQL + SQLAlchemy

People, contexts, opportunities

Scoring5 strategies

Opportunity scoring

Built with

Tech stack

Backend

PythonFastAPIPydanticSQLAlchemyAlembic

Databases

PostgreSQLNeo4jRedisRabbitMQ

Infra

Docker ComposeKubernetes

LLM extension points (planned)

The path

Roadmap

Where each product stands and where it's going.

Phase 1Done

Architecture & domain

DOMAIN/ARCHITECTURE/PRD docs
OpenAPI spec
ADR decisions

Phase 2Done

Foundation code

FastAPI backend
Domain entities
Graph engine
5 scoring strategies
Opportunity matcher
Seed data

Phase 3Planned

Dashboard frontend

Runnable dashboard
Real LLM extension
Opportunity explorer

Phase 4Planned

Scale

Multi-org
Enterprise SSO
Marketplace

Phase 1Done

Architecture & domain

DOMAIN/ARCHITECTURE/PRD docs
OpenAPI spec
ADR decisions

Phase 2Done

Foundation code

FastAPI backend
Domain entities
Graph engine
5 scoring strategies
Opportunity matcher
Seed data

Phase 3Planned

Dashboard frontend

Runnable dashboard
Real LLM extension
Opportunity explorer

Phase 4Planned

Scale

Multi-org
Enterprise SSO
Marketplace

For partners

Investment & partnership

An honest look at what's done, what's missing, and what I'm looking for.

What's done

Domain model & ubiquitous language
FastAPI backend
SQLAlchemy models
Neo4j graph engine
5 scoring strategies
Opportunity matcher
Worked-example seed data
OpenAPI 3.1 spec

What's missing

Runnable dashboard frontend
Real LLM integration
Production deployment
Enterprise auth
Data ingestion pipelines

Looking for

AngelCo-FounderStrategic Investor

Needs

Frontend engineer
Funding for graph DB infra
Enterprise design partner
LLM integration

Market potential

Talent intelligence ~$8B, growing fast

Competitive advantages

Network theory vs keyword matching
Explainable opportunities
Dual graph+relational model

Target audience

Executive search firms
Venture firms (founder matching)
Enterprise talent orgs
Strategic partnerships teams

Business model

Enterprise SaaS — Org network intelligence platform
API — Opportunity scoring as a service
Consulting — Custom network analysis engagements

Investor Intelligence

Valuation & Documentation

Estimated Valuation

AI-powered

$3.5M$2.5M - $4.5M

Berkus + Scorecard + Risk Factor SummationUpdated 8/1/2026Confidence: 55%

Valuation Factors

Market Size (TAM)

820%

Team & Execution

515%

Product Completion

415%

Competitive Moat

715%

Revenue Potential

715%

Scalability

610%

Timing & Market Readiness

810%

⚠️ This is an AI-generated estimate for reference only. Actual valuation depends on due diligence, market conditions, and negotiation.

READMEproject documentation

# HOOS

Human Opportunity Operating System — detects hidden value in networks

## Overview
HOOS detects Hidden Value inside human networks using Network Theory — Structural Holes, Weak Ties, and Bridging Social Capital — combined with contextual matching. Not a skill-to-job engine; an opportunity detection platform.

## Vision
A club bartender knows famous athletes. A factory CEO has an unstated need to meet them. HOOS detects the bartender's bridging value, infers the CEO's latent need, and generates a mutual, explainable opportunity.

## Problem
### Hidden value invisible
Network value isn't job titles — it's bridging positions and weak ties.
- Title-based matching misses bridging value
- Latent needs never surfaced
- Opportunities die undiscovered
### No contextual matching
Matching engines ignore context (a CEO's latent need to impress his son).
- Rigid skill graphs
- No need inference
- Zero explainability
### Social capital unmeasured
Wealth and titles are measured; bridging capital is not.
- Weak ties undervalued
- Bridgers invisible
- No scoring model

## Solution
### Network theory engine
Models structural holes, weak ties, and bridging capital explicitly.
### 5 scoring strategies
Multiple opportunity-scoring algorithms with explainable evidence.
### Contextual matching
Infers latent needs from context, not stated profiles.
### Explainable opportunities
Every opportunity shows the bridging path and mutual value.

## Key Features
- **Graph Engine**: Neo4j + Postgres dual model
- **5 Scoring Strategies**: Multiple opportunity algorithms
- **Opportunity Matcher**: Context-aware matching
- **Bridging Detector**: Finds structural holes
- **Latent Need Inference**: Infers unstated needs
- **Explainability**: Evidence per opportunity

## Architecture
**Pattern:** Clean Architecture + DDD, Neo4j (graph) + Postgres (transactional)

- **API** (FastAPI + Pydantic): Domain endpoints
- **Domain** (Python DDD): Entities, value objects
- **Graph Engine** (Neo4j): Network topology & bridging
- **Transaction** (PostgreSQL + SQLAlchemy): People, contexts, opportunities
- **Scoring** (5 strategies): Opportunity scoring

## Tech Stack
### Backend
- Python
- FastAPI
- Pydantic
- SQLAlchemy
- Alembic
### Databases
- PostgreSQL
- Neo4j
- Redis
- RabbitMQ
### Infra
- Docker Compose
- Kubernetes
### AI
- LLM extension points (planned)

## Business Model
- **Enterprise SaaS**: Org network intelligence platform
- **API**: Opportunity scoring as a service
- **Consulting**: Custom network analysis engagements

## Target Audience
- Executive search firms
- Venture firms (founder matching)
- Enterprise talent orgs
- Strategic partnerships teams

## Competitive Advantages
- Network theory vs keyword matching
- Explainable opportunities
- Dual graph+relational model

## Market Potential
Talent intelligence ~$8B, growing fast

## Pricing
$500–$5,000 / org / month

### Pricing Tiers
- **Team** ($500/mo) — Boutique search firms
  - 1 org
  - 5 users
  - 5 scoring strategies
  - Basic API
- **Business** ($2,000/mo) — Mid-size talent orgs
  - 3 orgs
  - 25 users
  - Opportunity matcher
  - Priority API
- **Enterprise** ($5,000+/mo) — Venture firms and enterprise talent orgs
  - Unlimited orgs
  - SSO
  - Custom scoring
  - On-prem option

## Roadmap
### Phase 1: Architecture & domain _(done)_
- DOMAIN/ARCHITECTURE/PRD docs
- OpenAPI spec
- ADR decisions
### Phase 2: Foundation code _(done)_
- FastAPI backend
- Domain entities
- Graph engine
- 5 scoring strategies
- Opportunity matcher
- Seed data
### Phase 3: Dashboard frontend _(planned)_
- Runnable dashboard
- Real LLM extension
- Opportunity explorer
### Phase 4: Scale _(planned)_
- Multi-org
- Enterprise SSO
- Marketplace

## What We Need
- Frontend engineer
- Funding for graph DB infra
- Enterprise design partner
- LLM integration

## Looking For
- Angel
- Co-Founder
- Strategic Investor

## Stage
Alpha (40% complete)

**Estimated MVP:** 3 months to runnable dashboard

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Quick demo launch:

# HOOS Human Opportunity Operating System — detects hidden value in networks ## Overview HOOS detects Hidden Value inside human networks using Network Theory — Structural Holes, Weak Ties, and Bridging Social Capital — combined with contextual matching. Not a skill-to-job engine; an opportunity detection platform. ## Vision A club bartender knows famous athletes. A factory CEO has an unstated need to meet them. HOOS detects the bartender's bridging value, infers the CEO's latent need, and generates a mutual, explainable opportunity. ## Problem ### Hidden value invisible Network value isn't job titles — it's bridging positions and weak ties. - Title-based matching misses bridging value - Latent needs never surfaced - Opportunities die undiscovered ### No contextual matching Matching engines ignore context (a CEO's latent need to impress his son). - Rigid skill graphs - No need inference - Zero explainability ### Social capital unmeasured Wealth and titles are measured; bridging capital is not. - Weak ties undervalued - Bridgers invisible - No scoring model ## Solution ### Network theory engine Models structural holes, weak ties, and bridging capital explicitly. ### 5 scoring strategies Multiple opportunity-scoring algorithms with explainable evidence. ### Contextual matching Infers latent needs from context, not stated profiles. ### Explainable opportunities Every opportunity shows the bridging path and mutual value. ## Key Features - **Graph Engine**: Neo4j + Postgres dual model - **5 Scoring Strategies**: Multiple opportunity algorithms - **Opportunity Matcher**: Context-aware matching - **Bridging Detector**: Finds structural holes - **Latent Need Inference**: Infers unstated needs - **Explainability**: Evidence per opportunity ## Architecture **Pattern:** Clean Architecture + DDD, Neo4j (graph) + Postgres (transactional) - **API** (FastAPI + Pydantic): Domain endpoints - **Domain** (Python DDD): Entities, value objects - **Graph Engine** (Neo4j): Network topology & bridging - **Transaction** (PostgreSQL + SQLAlchemy): People, contexts, opportunities - **Scoring** (5 strategies): Opportunity scoring ## Tech Stack ### Backend - Python - FastAPI - Pydantic - SQLAlchemy - Alembic ### Databases - PostgreSQL - Neo4j - Redis - RabbitMQ ### Infra - Docker Compose - Kubernetes ### AI - LLM extension points (planned) ## Business Model - **Enterprise SaaS**: Org network intelligence platform - **API**: Opportunity scoring as a service - **Consulting**: Custom network analysis engagements ## Target Audience - Executive search firms - Venture firms (founder matching) - Enterprise talent orgs - Strategic partnerships teams ## Competitive Advantages - Network theory vs keyword matching - Explainable opportunities - Dual graph+relational model ## Market Potential Talent intelligence ~$8B, growing fast ## Pricing $500–$5,000 / org / month ### Pricing Tiers - **Team** ($500/mo) — Boutique search firms - 1 org - 5 users - 5 scoring strategies - Basic API - **Business** ($2,000/mo) — Mid-size talent orgs - 3 orgs - 25 users - Opportunity matcher - Priority API - **Enterprise** ($5,000+/mo) — Venture firms and enterprise talent orgs - Unlimited orgs - SSO - Custom scoring - On-prem option ## Roadmap ### Phase 1: Architecture & domain _(done)_ - DOMAIN/ARCHITECTURE/PRD docs - OpenAPI spec - ADR decisions ### Phase 2: Foundation code _(done)_ - FastAPI backend - Domain entities - Graph engine - 5 scoring strategies - Opportunity matcher - Seed data ### Phase 3: Dashboard frontend _(planned)_ - Runnable dashboard - Real LLM extension - Opportunity explorer ### Phase 4: Scale _(planned)_ - Multi-org - Enterprise SSO - Marketplace ## What We Need - Frontend engineer - Funding for graph DB infra - Enterprise design partner - LLM integration ## Looking For - Angel - Co-Founder - Strategic Investor ## Stage Alpha (40% complete) **Estimated MVP:** 3 months to runnable dashboard --- *This is an auto-generated ... (truncated)