Inter OS — Full-Stack Football
Intelligence Operating System

Inter OS · Overview

Executive Summary

Inter OS is a unified "football intelligence operating system" that integrates club data — performance, recruitment, medical, contracts, and operations — into a governed semantic layer, then layers agentic workflows and role-based applications on top. The outcome is faster, more consistent decision-making: recruitment shortlists become repeatable pipelines, match preparation becomes a traceable workflow, and club strategy becomes measurable rather than anecdotal.

The platform borrows proven enterprise patterns: a semantic/operational layer ("ontology") above integrated datasets and models, and an AI layer designed to connect data and operations while maintaining auditability and governance. This is explicitly modelled on operational intelligence platforms where an ontology layer sits above data assets, and agents/automations are deployed with granular security and governance.

Chapter 1

Product Vision

Inter OS exists to make football organizations operate like modern, integrated decision systems. The core thesis is that football value is created by a chain: data → interpretation → decision → execution → feedback. Today those links are fragmented across scouting databases, video tools, spreadsheets, staff silos, and inconsistent definitions.

Inter OS unifies that chain by standardizing football objects — players, roles, matches, actions, training sessions, injuries, contracts — in a single operational model (the "club ontology"), and by instrumenting workflows with audit trails and measurable outcomes.

The vision is club-wide: academy outputs should map to first-team needs; recruitment should align with tactical identity; staff workflows should be measured for throughput and quality. Success is defined not by dashboards alone, but by decisions that are faster, explainable, and more accurate under uncertainty, with continuous improvement from feedback loops.

Design Principles

Design Principles & Reference Patterns

Ontology & Operational Layer

The Inter OS Ontology

The ontology is the club's meaning layer. It standardizes definitions — "what is a role," "what counts as a progressive pass" — aligns them to game models, and governs how decisions and actions are captured (shortlists, approvals, medical clearances). Every object type, link type, action, and function is first-class, and every workflow is auditable.

The ontology is implemented as a property graph because football intelligence is relationship-heavy: player↔agent↔club networks, role compatibility, squad balance, and tactical interactions are fundamentally graph problems. Nodes are connected by typed, directed relationships with properties on both nodes and relationships — e.g., Player_SIMILAR_TO_Player (score=0.82) or Player_FITS_ROLE (system=4-3-3, score=0.74).

Agentic Workflows

Agentic Workflows, Observability & Safe Actions

Inter OS agents operate under strict governance: human-over-the-loop for high-impact actions, audit logging for every decision and tool call, and role-based permissions at the data object level. Every agent interaction is traceable from input context → tool call → output → human review.

Chapter 2

Personas & Stakeholders

Inter OS is designed for role-based adoption. Each persona has distinct jobs-to-be-done, and the UX must be task-based, not dashboard-first.

Chapter 3

Modules Overview

Inter OS is modular so clubs can adopt in phases. Each module uses the same core primitives: the Inter OS ontology, a governed data plane, a tool/action system, and role-based UI surfaces. The modules differ mainly in (a) which objects they prioritize and (b) which workflows they optimize.

Module PRD/SRS

Inter 11 — Team Intelligence

PRD

Inter 11 is team intelligence modelled as an agentic starting XI: 11 positional specialists that analyze roles, tactics, and player performance through the lens of their position. The goal is to translate complex match and scouting data into role-specific insights that coaches and recruitment teams trust.

Inter 11 must support both "analysis mode" (what happened) and "planning mode" (what to do next): opponent prep packs, role adjustments, and fit comparisons for potential signings. Its differentiation is positional cognition and collaboration — the right-back agent reasons about width control and fullback-winger chemistry; the 6 agent reasons about pressing resistance, rest-defense, and tempo control.

SRS — Functional Requirements

• FR1 — Role-Based Match Analysis: ingest event + tracking, generate role execution scorecards per position
• FR2 — Comparable Finder: produce top-N similar players to a target role using graph similarity relationships
• FR3 — Tactical Pattern Detection: identify repeated patterns (build-up shapes, pressing triggers) and tag clips
• FR4 — Agent Collaboration: orchestrate multi-agent reviews and produce a team synthesis memo

SRS — Non-Functional Requirements

• Latency targets for common queries (p95 < 3s for shortlist generation)
• Availability for matchday workloads (99.9% SLA during match windows)
• Audit logging for every agent tool call
• Strict permissioning: scouts cannot access medical data unless authorized

Module PRD/SRS

Inter 25 — Squad Intelligence

PRD

Inter 25 is a squad intelligence system that models the full roster (starters, bench, rotation, loaned players, and key academy prospects) as a portfolio. It answers: "Do we have the right mix of profiles to play our game model across a season?" and "What are the highest-leverage recruitment and development moves?"

Inter 25 exposes classic squad planning surfaces: depth charts by role, succession plans by age curve, and risk dashboards (availability, contract expiration, wage structure stress). When Inter 11 identifies a positional deficiency, Inter 25 translates it into a multi-window plan.

SRS — Functional Requirements

• FR1 — Roster object model with contract + availability + role fit
• FR2 — Scenario simulator: injury/availability, rotation, budget constraints
• FR3 — Succession planner: time-to-replacement, academy readiness
• FR4 — Market intelligence surfaces: pricing bands, scarcity by role

Module PRD/SRS

Inter Backroom — Staff Intelligence

PRD

Inter Backroom is staff intelligence: it measures and improves the club's operational throughput — scouting coverage, report quality, analyst production, medical workflows, and decision handoffs. In many clubs, performance gaps are not due to "lack of data," but workflow bottlenecks: duplicated work, inconsistent definitions, and slow approvals.

The module provides standardized templates for scouting reports, analyst match packs, and medical readiness notes; assignment routing; SLA dashboards; and quality scoring. Staff augmentation agents help create first drafts and surface omissions, while maintaining human ownership and approval gates.

SRS — Functional Requirements

• FR1 — Workflow engine: task assignment, review, approval
• FR2 — Knowledge base + template library
• FR3 — Staff KPIs dashboards
• FR4 — Audit and compliance logs for internal decision documents

Module PRD/SRS

Inter Management — Manager & Coach Intelligence

PRD

Inter Management models the club's tactical identity, training principles, and match management patterns. The module's job is alignment: recruitment should match the coach's game model; academy development should reflect desired role families; and match prep outputs should be consistent with coaching preferences.

The product provides a "game model builder" and a "tactical identity dashboard" that translates qualitative philosophy into measurable indicators — pressing intensity, build-up shapes, chance creation patterns — and formalizes "tactical requirements profiles" used by recruitment.

SRS — Functional Requirements

• FR1 — Game model object types + versioning
• FR2 — Tactical KPI library with configurable weighting
• FR3 — Fit scoring between squad and manager identity
• FR4 — Match management analytics: sub patterns, structural adjustments

Module PRD/SRS

Inter Club — Club Organization Intelligence

PRD

Inter Club is club organization intelligence from academy to first team to executive decision-making. It answers: "Is the club operating as a coherent talent factory?" It unifies academy pipeline, loans, recruitment, and first-team performance into one portfolio view.

Three Flagship Dashboards

• Pipeline Health — position-by-position academy output, readiness timelines, loan effectiveness
• Asset Portfolio — contract value, wage efficiency, resale value, performance contribution
• Strategic Alignment — game model consistency across age groups, coaching principles, recruitment profile adherence

Chapter 4

Platform Architecture

Inter OS architecture is designed around three planes: the Data Plane (ingestion, transformation, quality, and storage), the Ontology Plane (the football knowledge graph), and the Action/Agent Plane (agents, workflows, audits, and evaluations). This separation follows the logic of operational platforms where a semantic layer sits above integrated datasets and AI workflows are deployed with observability and secure tool access.

Architecture Diagram

Logical Architecture

┌──────────────────────────────────────────────────────────────┐
│                    MODULE APPLICATIONS                        │
│   Inter 11 │ Inter 25 │ Backroom │ Management │ Inter Club   │
│                      War Room UI                              │
└──────────────────────────┬───────────────────────────────────┘
                           │
┌──────────────────────────▼───────────────────────────────────┐
│               AGENT & WORKFLOW PLANE                          │
│  Agent Orchestration · Tool Services · Evals · Audit Logs    │
└──────────────────────────┬───────────────────────────────────┘
                           │
┌──────────────────────────▼───────────────────────────────────┐
│                  INTER OS ONTOLOGY                            │
│  Knowledge Graph · Object Security · Actions & Functions      │
└──────────────────────────┬───────────────────────────────────┘
                           │
┌──────────────────────────▼───────────────────────────────────┐
│                      DATA PLANE                               │
│  Ingestion · Quality Gates · Lineage · Storage · Features    │
└──────────────────────────┬───────────────────────────────────┘
                           │
┌──────────────────────────▼───────────────────────────────────┐
│           EXTERNAL & INTERNAL SOURCES                         │
│  Event feeds · Tracking/XY · Video · Medical · Wearables     │
│  Scouting CRM · Contracts / Finance · Identity Provider      │
└──────────────────────────────────────────────────────────────┘

Data Model

Data Model & Knowledge Graph Schema

The Inter OS ontology is a football knowledge graph representing "nouns" (players, teams, roles, matches, staff) and "verbs" (actions, decisions, workflows). The property graph model — nodes connected by typed, directed relationships with properties on both — maps precisely to football intelligence needs.

// Core nodes
(Player { player_id, canonical_id, name, dob, dominant_foot, nationality })
(Role   { role_id, name, phase_responsibilities })
(Club   { club_id, name, league, country })
(Match  { match_id, date, competition, home_team, away_team })
(Scout  { scout_id, name, regions })

// Core relationships
(Player)-[:PLAYS_FOR]->(Club)
(Player)-[:FITS_ROLE { fit_score, system, derived_from }]->(Role)
(Player)-[:SIMILAR_TO { score: 0.82 }]->(Player)
(Player)-[:APPEARED_IN]->(Match)
(Scout)-[:WRITES]->(ScoutReport)-[:EVALUATES]->(Player)
(Manager)-[:DEFINES]->(GameModel)-[:HAS_ROLE]->(Role)
(AcademyPlayer)-[:PROGRESSES_TO { stage }]->(PathwayStage)

// Metrics model
(Match)-[:CONTAINS]->(MatchEvent { event_id, type, x, y, minute })
(Match)-[:CONTAINS]->(TrackingFrame { frame_id, ts_ms, x, y, speed })

Chapter 5

Agents & Automation

Inter OS uses specialized agents as controlled operators, not general assistants. The system is built around context engineering, tool services, and observability — with strict governance: human-over-the-loop for high-impact actions, audit logging for every decision and tool call, and role-based permissions at the data object level.

Agent Architecture

Agent Specifications Overview

Inter OS agents are "governed operators." The philosophy has three dimensions:

Agent Roster

Inter 11 Positional Agents

Inter 11 — Agent Collaboration Contracts

Inter 11 consists of 11 positional agents that must collaborate on shared workflows. Agent collaboration contracts define which agents are consulted for each workflow type.

Memory & Context

Memory Model & Context Engineering

Each agent maintains layered memory: short-term (current session / match window), medium-term (season + transfer window context), and long-term (club history, player trajectories, game model evolution). Context is retrieved via graph queries and assembled into structured bundles before each agent invocation.

Chapter 6

API & Integrations

Inter OS APIs are designed for two-world operation: (a) internal product surfaces and (b) external ecosystem interoperability — data providers, video platforms, club CRMs, BI tools, custom notebooks. The platform adopts RESTful, OAuth 2.0 authenticated, JSON-first interfaces to keep integrations robust and developer-friendly.

API Design

API Principles

• RESTful with consistent resource naming: /v1/ontology/{resource}
• OAuth 2.0 (Authorization Code flow) with scopes: interos.read, interos.write, interos.admin
• Standard JSON request/response; consistent error envelopes
• Idempotency keys for all write/action endpoints
• Pagination via cursor (not offset) for all list endpoints
• Webhooks for async events (shortlist updated, approval required, agent output ready)
• All actions return 202 Accepted + approval_workflow_id for governed writes

Integration Catalog

Integration Catalog & Priorities

API Contract Example

Sample API Contract — Player Fit Query

// POST /v1/ontology/roles/ROLE_6_CONTROLLER/fit-search
// Authorization: Bearer <token>  scopes: [interos.read]

REQUEST:
{
  "system": "4-3-3",
  "constraints": {
    "age_max": 25,
    "fee_max_eur": 8000000,
    "work_permit_ok": true
  },
  "weights": {
    "press_resistance":     0.25,
    "tempo_control":        0.25,
    "defensive_positioning": 0.20,
    "progressive_passing":  0.20,
    "availability":         0.10
  }
}

RESPONSE 200:
{
  "shortlist": [
    {
      "player_id":  "PLY_123",
      "fit_score":  0.81,
      "evidence": {
        "matches": ["MATCH_991", "MATCH_876"],
        "clips":   ["CLIP_221", "CLIP_305"],
        "notes":   ["REPORT_19"]
      },
      "risks": ["limited minutes vs high press opponents"],
      "recommended_next_action": "create_scouting_task"
    }
  ],
  "meta": {
    "candidates_evaluated": 412,
    "agent_ids": ["AGENT_6_V2"],
    "audit_ref": "AUDIT_20240315_001"
  }
}

Chapter 7

Security & Compliance

Inter OS must handle sensitive personal data — athlete health, biometric, contract data, and internal evaluations. Governance must be designed, not bolted on. GDPR requires lawful, fair, and transparent processing, data minimization, storage limitation, and appropriate security and confidentiality. Inter OS translates these principles into concrete product controls.

Security Controls Checklist

Security Controls

GDPR & DPIA

GDPR & DPIA Playbook

Data Classification & Lawful Basis

DPIA Decision Rule

A DPIA is required when processing is likely to result in high risk to individuals. For Inter OS, this includes systematic processing of health data, large-scale profiling of athletes, and processing data about minors. DPIA templates must be maintained and evidence documented for each deployment.

Chapter 8

Commercial & GTM

Inter OS is an enterprise platform sold on outcomes: improved recruitment hit rate, reduced injury risk, faster match analysis cycles, and stronger academy-to-first-team conversion. Packaging aligns to how clubs buy: core platform + module add-ons + data integrations.

Packaging & Pricing

Pricing Tiers

$100M ARR Plan

GTM Milestones to $100M ARR

Chapter 9

Roadmap & KPIs

North Star Metrics

• Decision Velocity — time from question → shortlist/recommendation → approved action
• Decision Quality — post-decision review score (transfer success; tactical change impact)
• Adoption — weekly active decision makers; agent-assisted workflows completed
• Governance — % of actions with auditable evidence; DPIA compliance coverage

Module KPIs

Chapter 10

Competitor Landscape

Inter OS positioning: "Inter OS connects data, workflows, and decisions across the whole club — turning best-in-class inputs (data providers, video, tracking, wearables) into governed actions and repeatable performance outcomes."

Appendices

Appendices

Working artifacts for sales engineering, onboarding, and internal enablement. Each team involved in a club deployment can take these examples and adapt them.

Templates

PRD / SRS Templates

PRD Template

# PRD: <Module Name>

## Summary
What this module does, for whom, and why now.

## Problem Statement
- Current pain points
- Who experiences them
- Cost of inaction

## Goals and Non-Goals
Goals:
Non-goals:

## Personas
Primary: / Secondary:

## Jobs to be Done
Top 3–7 jobs.

## User Stories
As a <role>, I want <capability>, so that <outcome>.

## Scope
In scope: / Out of scope:

## Functional Requirements
FR1... / FR2...

## Non-Functional Requirements
Latency, availability, scale, audit, privacy.

## Data Requirements
Inputs / Outputs / Quality checks / Retention

## UX Requirements
Key screens and flows.

## Dependencies
Data providers, internal systems, roles needed.

## Success Metrics
Adoption, value, speed, accuracy.

## Risks and Mitigations

## Open Questions
Unspecified constraints: budget / timeline / hosting region.

SRS Template

# SRS: <Module Name>

## System Context
How the module fits in Inter OS.

## Interfaces
APIs consumed / APIs exposed / Events & webhooks

## Functional Requirements
FR1: ...
  Acceptance criteria:
  Edge cases:

## Data Model
Objects, relationships, schemas.

## Security Requirements
RBAC rules, audit, encryption.

## Performance Requirements
p95 latency, throughput, concurrency.

## Reliability Requirements
SLOs, DR, backup.

## Observability
Logging, metrics, traces.

## Testing
Unit, integration, load, security, evals.

## Deployment
Environments, release process.

## Assumptions and Constraints
Budget / timeline / hosting region: unspecified.

OpenAPI Starter

OpenAPI Starter Spec

openapi: 3.0.3
info:
  title: Inter OS API
  version: 0.1.0
servers:
  - url: https://api.inter-os.example.com
paths:
  /v1/ontology/players:
    get:
      summary: List players
      parameters:
        - in: query
          name: q
          schema: { type: string }
        - in: query
          name: limit
          schema: { type: integer, default: 50 }
      responses:
        "200":
          description: OK

  /v1/ontology/actions/apply:
    post:
      summary: Apply a governed action
      requestBody:
        required: true
      responses:
        "202":
          description: Accepted — approval workflow triggered

components:
  securitySchemes:
    oauth2:
      type: oauth2
      flows:
        authorizationCode:
          authorizationUrl: https://auth.inter-os.example.com/authorize
          tokenUrl: https://auth.inter-os.example.com/token
          scopes:
            interos.read:  Read access to ontology objects
            interos.write: Propose and submit governed actions
            interos.admin: Full administrative access

security:
  - oauth2: [interos.read]

Agent Prompt Template

Agent System Prompt Template

SYSTEM ROLE:
You are <Agent Name>, specialized in <scope — e.g., "Controller 6 positional intelligence">.

MISSION:
Produce football-native, evidence-linked recommendations that are
explainable and safe. Never speculate beyond available evidence.

CONTEXT RULES:
- Use only authorized data objects provided in this context bundle.
- Prefer ontology objects + evidence links over raw claims.
- If data is missing, state uncertainty and propose next retrieval steps.
- Do not infer medical status from non-medical data.

TOOLS AVAILABLE:
Read  → graph_search, match_query, video_clip_lookup, report_store
Write → create_task, propose_shortlist, request_approval
       (all write tools require explicit user confirmation)

SAFETY & GOVERNANCE:
- Never execute high-impact actions without confirmed approval state.
- Always log rationale, evidence links, and confidence level.
- Avoid medical inferences unless explicitly authorized (MEDICAL scope).
- Flag conflicts of interest (e.g., agent/player relationships) if known.

OUTPUT FORMAT:
1. Position / Role Perspective
2. Evidence Summary (matches, clips, report refs)
3. Recommendation (ranked options with rationale)
4. Risks & Alternatives
5. Proposed Next Actions (with required approvals noted)

Glossary

Glossary