Plutopus

AI-Powered Predictive Network Operations Platform

Plutopus is an air-gap-ready Network Operations Intelligence Platform that combines telemetry ingestion, topology awareness, predictive analytics, incident correlation, and an AI copilot to help network teams detect, understand, and respond to issues before they impact operations.

Vision

Most network operations tools are reactive. Alerts fire after outages begin. Operators diagnose in isolation with no topological context. Root cause analysis is manual, slow, and often wrong. Dashboards show raw metrics with no intelligence layered on top. Air-gapped deployments require expensive licensed platforms with no offline model support.

Plutopus was built to address these gaps:

Problem	How Plutopus Addresses It
Alert fatigue	Anomalies are correlated by topology into prioritized incidents, not raw alerts
No topology context	A NetworkX graph model maps sites → devices → interfaces → tunnels in real-time
Reactive troubleshooting	Linear regression forecasting predicts degradation 15m, 30m, and 60m ahead
Poor root cause analysis	Correlation engine groups anomalies by shared hub/underlay relationships
Operational toil	AI Copilot grounded in live platform data reduces manual investigation
Air-gap challenges	Fully offline-capable: bundled images, local Ollama models, no external calls

Platform Overview

Plutopus is a multi-service platform organized into a clear layered architecture:

[Operator / NOC Engineer]
        │
        ▼
┌───────────────────────────────────────────────┐
│         Next.js Dashboard  ·  CLI             │  ← Presentation Layer
└───────────────┬───────────────────────────────┘
                │ HTTP REST
        ┌───────▼────────────────────┐
        │       FastAPI Gateway      │  ← API Layer (RBAC + JWT + Audit)
        └──┬──────┬──────┬───────┬──┘
           │      │      │       │
    ┌──────▼──┐ ┌─▼───┐ ┌▼────┐ ┌▼──────────────┐
    │Topology │ │Pred.│ │Corr.│ │ AI Copilot     │  ← Intelligence Layer
    │ Engine  │ │Eng. │ │Eng. │ │ (Ollama/RAG)   │
    └──────┬──┘ └─┬───┘ └┬────┘ └┬──────────────┘
           │      │      │       │
    ┌──────▼──────▼──────▼───────▼──────────────┐
    │      TimescaleDB (PostgreSQL 16)           │  ← Storage Layer
    └────────────────────────────────────────────┘
           │
    ┌──────▼─────────────────────────────────────┐
    │   Redpanda (Kafka-compatible broker)        │  ← Streaming Layer
    │   Topics: metrics_raw, events_raw           │
    └─────────────────────────────────────────────┘
           │
    ┌──────▼─────────────────────────────────────┐
    │       Telemetry Worker (Consumer)           │  ← Ingestion Layer
    └─────────────────────────────────────────────┘

High-Level Architecture Diagram

graph TB
    subgraph Clients["Client Layer"]
        DASH["Next.js Dashboard\n:3000"]
        CLI["Python CLI\n(Typer)"]
    end

    subgraph API["API Gateway — FastAPI :8000"]
        direction LR
        AUTH["JWT + RBAC\nMiddleware"]
        METRICS_MW["Prometheus\nMiddleware"]
        ROUTES["REST Endpoints\n/api/v1/*"]
    end

    subgraph Intelligence["Intelligence Services"]
        TOPO["Topology Engine\n(NetworkX Graph)"]
        PRED["Prediction Worker\n(Forecasting + Anomaly)"]
        CORR["Correlation Engine\n(Incident Grouping)"]
        COP["Copilot Worker\n(Context + LLM)"]
    end

    subgraph Storage["Storage Layer"]
        PG[("TimescaleDB\nPostgreSQL 16")]
        QDRANT[("Qdrant\nVector Store")]
        OLLAMA["Ollama\nLocal LLM Runtime"]
    end

    subgraph Stream["Streaming Layer"]
        RP["Redpanda Broker\n:9092 Kafka-compat"]
        TW["Telemetry Worker\n(Consumer)"]
    end

    subgraph Observability["Observability"]
        PROM["Prometheus\n:9090"]
        GRAF["Grafana\n:3001"]
    end

    DASH --> API
    CLI --> API
    API --> TOPO
    API --> CORR
    API --> COP
    TOPO --> PG
    PRED --> PG
    CORR --> PG
    COP --> PG
    COP --> OLLAMA
    COP --> QDRANT
    TW --> RP
    TW --> PG
    API --> METRICS_MW
    METRICS_MW --> PROM
    PROM --> GRAF

Key Capabilities

Capability	Status	Details
Telemetry Ingestion	✅ Implemented	Redpanda consumer normalizing raw metrics and events into TimescaleDB
Topology Intelligence	✅ Implemented	NetworkX digraph of sites, devices, interfaces, and tunnels
Network Inventory	✅ Implemented	Full CRUD-queryable inventory: sites, devices, interfaces, tunnels
Linear Forecasting	✅ Implemented	15m / 30m / 60m look-ahead per interface/tunnel with confidence scores
Anomaly Detection	✅ Implemented	Z-score detection per metric with severity classification
Risk Scoring	✅ Implemented	0–100 risk index per site and tunnel with signal attribution
Event Correlation	✅ Implemented	Topology-aware grouping of anomalies into root-cause incident candidates
Incident Management	✅ Implemented	Priority-ranked incidents with status lifecycle and export
Runbook Engine	✅ Implemented	6 structured markdown runbooks (congestion, latency, flapping, etc.)
AI Copilot	✅ Implemented	Context-grounded Q&A via local Ollama with deterministic fallback
Air-Gap Deployment	✅ Implemented	Full offline bundle: images, models, charts, runbooks, checksums
RBAC	✅ Implemented	Three roles: `admin`, `operator`, `viewer` enforced via JWT claims
Audit Logging	✅ Implemented	Immutable `audit_logs` table: user, action, resource, IP, result
Prometheus Metrics	✅ Implemented	`/metrics` endpoint: request rate, latency, incidents, webhooks
Grafana Dashboard	✅ Implemented	Versioned JSON dashboard configuration
Helm Deployment	✅ Implemented	Helm chart v0.1.0 with Deployment, Service, and Ingress templates
Outbound Webhooks	✅ Implemented	Incident export with retry/backoff to external NOC systems
Backup & Recovery	✅ Implemented	pg_dump-based backup, restore scripts, and validation reporter
Offline Model Packaging	✅ Implemented	SHA256-verified model export/import scripts for Ollama

System Architecture

1. Logical Layer Architecture

graph LR
    subgraph Presentation
        A["Next.js\nDashboard"]
        B["Typer CLI"]
    end

    subgraph API["API Gateway (FastAPI)"]
        C["Authentication\nJWT + RBAC"]
        D["Audit Logger"]
        E["Prometheus\nExporter"]
    end

    subgraph Logic["Business Logic Services"]
        F["Topology\nEngine"]
        G["Prediction\nWorker"]
        H["Correlation\nEngine"]
        I["Copilot\nService"]
        J["Webhook\nIntegration"]
    end

    subgraph Persistence
        K[("TimescaleDB")]
        L[("Qdrant")]
    end

    A --> C
    B --> C
    C --> D
    C --> E
    C --> F
    C --> H
    C --> I
    C --> J
    G --> K
    F --> K
    H --> K
    I --> K
    I --> L

2. Telemetry Data Flow

sequenceDiagram
    participant SRC as Network Source
    participant RP as Redpanda Broker
    participant TW as Telemetry Worker
    participant PG as TimescaleDB
    participant PW as Prediction Worker

    SRC->>RP: Publish raw metric/event (metrics_raw topic)
    TW->>RP: Subscribe & consume message
    TW->>TW: Normalize, validate, type-check
    TW->>PG: INSERT into metrics / events tables
    PW->>PG: SELECT recent metrics (15 samples)
    PW->>PW: fit_linear_trend() → forecast_metric()
    PW->>PW: detect_anomaly() Z-score check
    PW->>PW: calculate_tunnel_risk() / calculate_site_risk()
    PW->>PG: INSERT forecasts, anomalies, risk_scores

3. Copilot Request Flow

sequenceDiagram
    participant User as NOC Operator
    participant API as FastAPI /copilot/query
    participant CTX as Context Engine
    participant RTV as Retrieval Layer
    participant LLM as Ollama Runtime
    participant FB as Fallback Engine

    User->>API: POST /api/v1/copilot/query {question, site_id}
    API->>CTX: get_site_context(site_id)
    CTX->>CTX: Query: sites, devices, interfaces, tunnels
    CTX->>CTX: Query: active anomalies, risk_scores, forecasts
    CTX-->>API: Structured context dict
    API->>RTV: retrieve_runbooks(question)
    RTV->>RTV: Keyword match against 6 runbook files
    RTV-->>API: Matching runbook markdown
    API->>LLM: POST /api/generate {prompt, system, context}
    alt Ollama reachable (4s timeout)
        LLM-->>API: Generated response text
    else Timeout or error
        API->>FB: generate_fallback_response()
        FB-->>API: Deterministic structured diagnostic
    end
    API-->>User: {answer, sources, runbooks, context_used}

4. Prediction Pipeline

flowchart TD
    A[Prediction Worker Starts\nSLEEP_INTERVAL=300s] --> B[Query all Interfaces]
    B --> C[For each Interface]
    C --> D[SELECT last 15 utilization metrics]
    D --> E{Enough data?}
    E -- No --> C
    E -- Yes --> F[fit_linear_trend\nalpha + beta × t]
    F --> G[forecast_metric\n+15m, +30m, +60m]
    G --> H[detect_anomaly\nZ-score vs historical]
    H --> I{Z-score > threshold?}
    I -- Yes --> J[INSERT Anomaly\nseverity + score]
    I -- No --> K[INSERT Forecast]
    J --> K
    K --> L[Repeat for Tunnels\nlatency + packet_loss]
    L --> M[calculate_tunnel_risk\n0-100 risk index]
    M --> N[calculate_site_risk\naggregate + weights]
    N --> O[INSERT RiskScore]
    O --> P[Sleep SLEEP_INTERVAL]
    P --> A

5. Incident Correlation Pipeline

flowchart TD
    A[POST /api/v1/incidents/correlated] --> B[EventCorrelationEngine.run_correlation]
    B --> C[SELECT latest 50 anomalies]
    C --> D[Separate: hub_anoms vs spoke_anoms]

    D --> E{Hub anomalies exist?}
    E -- Yes --> F[For each hub anomaly\nfind matching spoke anomalies\nsame metric + branch ID]
    F --> G{≥2 affected spokes?}
    G -- Yes --> H[Create Incident:\nCorrelated Hub Congestion\nroot_cause = hub tunnel ID\nconfidence = 0.92]
    G -- No --> I[Skip]

    D --> J[Group spoke anomalies by site_id]
    J --> K{≥2 anomalies per site?}
    K -- Yes --> L[Create Incident:\nLocal Site Degradation\nroot_cause = site_id\nconfidence = 0.85]
    K -- No --> M[Skip]

    H --> N[AlertPrioritizationEngine.calculate_priority\nrisk + criticality + time + scope]
    L --> N
    N --> O[Score 0-100\nLevel: low/medium/high/critical]
    O --> P[UPDATE Incident.priority + severity]
    P --> Q[Return active incidents list]

6. Air-Gap Deployment Architecture

flowchart TD
    A[pack-offline-bundle.sh] --> B[distribution/\ndocker-images/\nmodels/\nrunbooks/\ndeployment/helm/\nchecksums/]
    B --> C[plutopus-offline-bundle.tar.gz\n+ SHA256 checksum]

    C --> D[Transfer to Air-Gapped Host\nUSB / Secure File Transfer]

    D --> E[Target: Isolated Network\nNo internet, No DNS resolution\nNo external model downloads]

    E --> F[docker load images]
    F --> G[import-model.sh\nLoad Ollama model layers]
    G --> H[docker compose up]
    H --> I[AIRGAP_MODE=true]

    I --> J[All services operational\nTimescaleDB ✓\nRedpanda ✓\nOllama ✓\nAPI ✓\nDashboard ✓]

    I --> K[scripts/airgap/verify.sh\nairgap-report.md generated]

Technology Stack

Layer	Technology	Version	Purpose
API Gateway	FastAPI	0.100+	REST API framework with auto-generated OpenAPI docs
Language	Python	3.9+	Backend service implementation language
Frontend	Next.js	15	React-based dashboard with server-side rendering
Styling	TailwindCSS	3	Utility-first styling for the dashboard UI
Primary Database	PostgreSQL + TimescaleDB	PG 16	Relational storage with time-series optimizations
Vector Database	Qdrant	Latest	Semantic similarity storage for copilot context retrieval
Message Broker	Redpanda	Latest	Kafka-compatible streaming broker for telemetry
LLM Runtime	Ollama	Latest	Local, self-hosted language model inference
Graph Engine	NetworkX	3.x	In-memory directed graph for topology relationships
ORM	SQLAlchemy	2.x	Database session management and model mapping
Data Validation	Pydantic	2.x	Request/response schema validation
Authentication	PyJWT	2.13	HS256 JWT signing and verification
Metrics	prometheus-client	0.25	Prometheus exposition format exporter
Visualization	Grafana	Latest	Metrics dashboards and alerting
Metrics Scraper	Prometheus	Latest	Time-series metrics collection
Container Runtime	Docker + Compose	v3.8	Multi-service containerized deployment
Kubernetes	Helm	v2 (Chart 0.1.0)	Kubernetes packaging and deployment
CLI	Typer	Latest	Terminal interface for operators

Repository Structure

plutopus/
│
├── apps/                           # User-facing application layer
│   ├── api/                        # FastAPI backend gateway
│   │   └── src/
│   │       ├── api/v1/endpoints/   # Route handlers (12 modules)
│   │       ├── core/               # Auth, audit, config, metrics
│   │       ├── schemas/            # API request/response models
│   │       └── main.py             # ASGI application entrypoint
│   │
│   ├── dashboard/                  # Next.js frontend
│   │   └── src/app/
│   │       ├── dashboard/          # Dashboard pages
│   │       │   ├── incidents/      # Incident management UI
│   │       │   ├── predictions/    # Forecasting & anomaly UI
│   │       │   └── copilot/        # AI Copilot chat interface
│   │       └── topology/           # Interactive topology graph
│   │
│   └── cli/                        # Typer-based operator CLI
│
├── services/                       # Backend intelligence services
│   ├── telemetry/                  # Redpanda consumer & normalizer
│   ├── prediction/                 # Forecasting, anomaly, risk engine
│   │   ├── forecasting/            # Linear regression forecaster
│   │   ├── anomaly/                # Z-score anomaly detector
│   │   ├── risk/                   # 0-100 risk index calculator
│   │   ├── correlation/            # RiskCorrelationEngine
│   │   └── src/worker.py           # Scheduled prediction loop
│   │
│   ├── topology/                   # Network graph engine
│   │   ├── graph/                  # NetworkX topology builder
│   │   ├── health/                 # Site health scoring
│   │   ├── intelligence/           # Path analysis and correlation
│   │   ├── repository/             # Database query layer
│   │   └── topology.yaml           # Lab topology definition
│   │
│   ├── copilot/                    # AI Copilot service
│   │   ├── context/                # Context Engine & Summarizer
│   │   ├── llm/                    # Ollama client with fallback
│   │   ├── memory/                 # Per-user memory cache
│   │   ├── prompts/                # System prompt templates
│   │   ├── retrieval/              # Runbook retrieval layer
│   │   └── runbooks/               # 6 structured markdown runbooks
│   │
│   ├── correlation/                # Event Correlation Engine
│   │   ├── engine.py               # Topology-aware anomaly grouper
│   │   └── prioritization.py       # 0-100 priority index calculator
│   │
│   └── integrations/               # External integration adapters
│       └── webhooks.py             # Outbound webhook with retry/backoff
│
├── packages/                       # Shared internal libraries
│   ├── shared/                     # ORM models, DB session, shared logic
│   │   └── src/plutopus_shared/
│   │       ├── models.py           # All 11 SQLAlchemy table definitions
│   │       ├── db.py               # Database engine + session factory
│   │       └── correlation.py      # Shared metric query helpers
│   │
│   ├── schemas/                    # Shared Pydantic API schemas
│   └── utils/                      # Common utility helpers
│
├── infrastructure/                 # Deployment & operations tooling
│   ├── helm/                       # Helm chart (Chart.yaml, values.yaml)
│   │   └── templates/              # K8s Deployment, Service manifests
│   ├── k8s/network-policies/       # Default-deny + allow-internal policies
│   ├── monitoring/
│   │   ├── prometheus.yml          # Prometheus scrape configuration
│   │   └── grafana/dashboards/     # Versioned Grafana dashboard JSON
│   └── database/                   # Migration and init scripts
│
├── distribution/                   # Air-gap deployment bundle
│   ├── pack-offline-bundle.sh      # Bundle packager script
│   ├── models/                     # Ollama model export/import scripts
│   ├── docker-images/              # Docker image tarballs
│   ├── runbooks/                   # Offline runbook copies
│   ├── deployment/helm/            # Bundled Helm chart copy
│   └── checksums/                  # SHA256 integrity manifests
│
├── docs/                           # Technical documentation
│   ├── architecture/               # System design documents
│   ├── roadmap/                    # Phase planning
│   ├── adr/                        # Architecture Decision Records
│   ├── capacity-planning.md        # Resource sizing guide
│   ├── compliance/                 # Compliance readiness guide
│   ├── operations/                 # Operations & upgrade guide
│   ├── runbooks/                   # Disaster recovery runbooks
│   └── production-certification.md # Platform certification report
│
├── scripts/                        # Operational scripts
│   ├── backup.sh                   # pg_dump-based backup
│   ├── restore.sh                  # Database restore
│   ├── init-redpanda.sh            # Topic initialization
│   ├── generate-demo-telemetry.py  # Demo data injector
│   ├── airgap/verify.sh            # Air-gap compliance check
│   ├── backup-validation/          # Automated restore validator
│   ├── security-audit/audit.sh     # Static security scanner
│   └── upgrade/                    # Schema migration + rollback
│
├── tests/                          # Test suite (45 tests, 90% coverage)
│   ├── test_api.py                 # API endpoint integration tests
│   ├── test_phase2.py              # Topology engine tests
│   ├── test_phase3.py              # Prediction pipeline tests
│   ├── test_phase4.py              # Copilot tests
│   ├── test_phase5.py              # Incident management tests
│   └── test_phase6.py              # Audit, air-gap, security tests
│
├── docker-compose.yml              # Full-stack local deployment (13 services)
├── .env.example                    # Environment variable template
├── Makefile                        # Developer task runner
└── documentation.md                # Living project documentation

Core Components

Telemetry Engine

Located in services/telemetry/. A Kafka-protocol consumer running against the Redpanda broker.

Subscribes to metrics_raw and events_raw topics
Normalizes and type-checks incoming payloads
Inserts validated records into the metrics and events TimescaleDB tables
Runs as an always-on background Docker service

Prediction Engine

Located in services/prediction/. A scheduled worker (src/worker.py) running every PREDICTION_INTERVAL seconds (default: 300s).

Forecasting (forecasting/): Implements ordinary least squares linear regression (fit_linear_trend) over the last 15 metric samples per entity. Generates forecasts at +15m, +30m, and +60m horizons with a confidence coefficient.

Anomaly Detection (anomaly/): Computes a Z-score of the latest observation against recent history. Classifies severity (info, warning, critical) by Z-score magnitude.

Risk Scoring (risk/): Aggregates anomaly signals, utilization trends, and tunnel states into a 0–100 composite risk index per site and tunnel. Includes signal attribution for explainability.

Risk Correlation (correlation/): Cross-entity correlation that checks if multiple interfaces on the same site are simultaneously degraded.

Correlation Engine

Located in services/correlation/. The EventCorrelationEngine scans the latest 50 anomalies and groups them into incidents by applying topology knowledge:

Hub Congestion Scenario: If a hub tunnel is anomalous and ≥2 branch sites show the same metric deviation, a correlated incident is created with the hub as root cause (confidence: 0.92).
Local Site Isolation Scenario: If ≥2 anomalies appear on the same site, a site-level incident is created (confidence: 0.85).

The AlertPrioritizationEngine then scores each incident 0–100 using weighted factors:

Factor	Weight	Scoring Range
Risk Score	30%	0–100 from risk engine
Business Criticality	35%	`mission_critical`=90, `high`=60, `medium`=30, `low`=10
Time to Impact	20%	≤15m=95, ≤30m=75, ≤60m=50, >60m=25
Scope (node count)	15%	Scales with number of affected sites

AI Copilot

Located in services/copilot/. A context-grounded diagnostic assistant with deterministic fallback.

Context Engine (context/engine.py): For a given site, retrieves the full topology subgraph (devices, interfaces, tunnels), active anomalies, latest risk scores, and recent forecasts into a single structured context dictionary.

Retrieval Layer (retrieval/): Keyword-matches the user's question against 6 bundled runbooks: congestion, high_latency, interface_flapping, packet_loss, route_instability, tunnel_failure.

LLM Client (llm/): Calls the local Ollama /api/generate endpoint with the context-injected prompt. Enforces a 4-second timeout. On failure, delegates to generate_fallback_response() which builds a structured diagnostic from the raw context data without LLM involvement — ensuring responses are always grounded and never hallucinated.

Memory (memory/): Per-session memory cache for conversation continuity.

Incident Management

Located in apps/api/src/api/v1/endpoints/incidents.py.

GET /api/v1/incidents — Paginated incident list with status/severity filtering (requires viewer+)
GET /api/v1/incidents/correlated — Triggers a live correlation run (requires operator+)
GET /api/v1/incidents/{id} — Single incident detail view
POST /api/v1/incidents/export — Export incident to external webhook endpoint
POST /api/v1/incidents/integrations/webhook — Inbound alert receiver from external systems

Monitoring Stack

Prometheus: Scrapes the /metrics endpoint on the API. Tracks api_requests_total, api_request_latency_seconds, incidents_generated_total, copilot_queries_total, webhook_delivery_total.
Grafana: Pre-configured dashboard at infrastructure/monitoring/grafana/dashboards/system_health.json with panels for API request rate and incident severity distribution.

Deployment Framework

Docker Compose: docker-compose.yml defines 13 services including API, Dashboard, Telemetry Worker, Prediction Worker, Copilot Worker, TimescaleDB, Qdrant, Redpanda, Ollama, Prometheus, and Grafana.
Helm Chart: infrastructure/helm/ (Chart v0.1.0) provides Kubernetes Deployment, Service, and Ingress templates parameterized via values.yaml.
Network Policies: infrastructure/k8s/network-policies/ implements default-deny-all and allow-internal-db policies for namespace isolation.

Database Design

Eleven tables across the SQLAlchemy ORM, all mapped from packages/shared/src/plutopus_shared/models.py.

erDiagram
    sites {
        string id PK
        string name
        string role
        string business_criticality
    }
    devices {
        string id PK
        string site_id FK
        string name
        string role
        string ip
        string business_criticality
    }
    interfaces {
        string id PK
        string device_id FK
        string name
        string type
        string status
    }
    tunnels {
        string id PK
        string src_interface_id FK
        string dst_interface_id FK
        string status
    }
    metrics {
        int id PK
        string target_id
        string name
        float value
        datetime timestamp
    }
    events {
        int id PK
        string device_id FK
        string severity
        string message
        datetime timestamp
    }
    anomalies {
        int id PK
        string entity_id
        string entity_type
        string metric
        string severity
        float score
        string description
        datetime timestamp
    }
    forecasts {
        int id PK
        string target_id
        string metric
        float current_val
        float forecast_15m
        float forecast_30m
        float forecast_60m
        float confidence
        datetime timestamp
    }
    risk_scores {
        int id PK
        string entity_id
        string entity_type
        int risk_score
        string risk_level
        string signals
        datetime timestamp
    }
    incidents {
        string id PK
        string title
        string severity
        int priority
        string status
        string root_cause
        float confidence
        string affected_entities
        string source_anomalies
        datetime created_at
    }
    audit_logs {
        int id PK
        datetime timestamp
        string username
        string action
        string resource
        string resource_id
        string result
        string source_ip
    }

    sites ||--o{ devices : "contains"
    devices ||--o{ interfaces : "has"
    devices ||--o{ events : "generates"
    interfaces ||--o{ tunnels : "src_interface"
    interfaces ||--o{ tunnels : "dst_interface"

API Reference

All routes are served under the /api/v1 prefix. Interactive documentation available at http://localhost:8000/docs.

Route Group	Prefix	Auth Required	Roles
Health	`/health`	No	—
Sites	`/api/v1/sites`	Yes	viewer+
Devices	`/api/v1/devices`	Yes	viewer+
Tunnels	`/api/v1/tunnels`	Yes	viewer+
Metrics	`/api/v1/metrics`	Yes	viewer+
Events	`/api/v1/events`	Yes	viewer+
Topology	`/api/v1/topology`	Yes	viewer+
Predictions	`/api/v1/predictions/*`	Yes	viewer+
Copilot	`/api/v1/copilot/*`	Yes	operator+
Incidents	`/api/v1/incidents`	Yes	viewer+
Incident Correlation	`/api/v1/incidents/correlated`	Yes	operator+
Incident Export	`/api/v1/incidents/export`	Yes	operator+
Audit Logs	`/api/v1/audit/logs`	Yes	admin only
Prometheus Metrics	`/metrics`	No	—

Prediction Endpoints

Endpoint	Method	Description
`/api/v1/predictions/forecasts`	GET	Latest forecasts per interface
`/api/v1/predictions/anomalies`	GET	Active anomalies with severity
`/api/v1/predictions/risk`	GET	Current risk scores per entity

Topology Endpoints

Endpoint	Method	Description
`/api/v1/topology/graph`	GET	Full NetworkX graph as adjacency JSON
`/api/v1/topology/sites/{id}/health`	GET	Site health score and signals
`/api/v1/topology/paths`	GET	Shortest paths between two sites

Copilot Endpoints

Endpoint	Method	Description
`/api/v1/copilot/query`	POST	Submit a diagnostic question
`/api/v1/copilot/context/{site_id}`	GET	Retrieve the context payload for a site
`/api/v1/copilot/runbooks`	GET	List all available runbooks

AI Copilot

The Copilot is a retrieval-augmented diagnostic assistant that runs entirely on local infrastructure. It never sends data to external APIs.

Request Flow

Context Assembly: CopilotContextEngine queries TimescaleDB for the site's full inventory, active anomalies, latest risk scores, and forecast horizon.
Runbook Retrieval: Keyword matching against 6 structured markdown runbooks selects the most relevant procedures.
Prompt Construction: Context and runbooks are serialized and injected into a system prompt with explicit grounding instructions.
LLM Call: The Ollama /api/generate API is called with a 4-second timeout. Default model: qwen:0.5b.
Fallback: If Ollama is unavailable or times out, generate_fallback_response() constructs a deterministic diagnostic from the raw context data. No response is ever hallucinated.
Source Attribution: Every response includes context_used and runbooks fields listing exactly what data sourced the answer.

Available Runbooks

Runbook	Trigger Keywords
`congestion.md`	congestion, utilization, bandwidth, saturation
`high_latency.md`	latency, delay, RTT, slow
`interface_flapping.md`	flapping, cycling, instability, up/down
`packet_loss.md`	loss, drops, reachability
`route_instability.md`	BGP, route, prefix, instability
`tunnel_failure.md`	tunnel, VPN, down, failed

Predictive Analytics

The prediction engine runs as a scheduled background worker with these stages:

Forecasting

Algorithm: Ordinary least squares linear trend fitting
Input: Last 15 metric samples per interface/tunnel
Output: forecast_15m, forecast_30m, forecast_60m + confidence coefficient
Metrics covered: utilization, latency, packet_loss

Anomaly Detection

Algorithm: Z-score deviation from historical mean
Thresholds: Configurable by severity level
Output: severity (info/warning/critical), score, description

Risk Scoring

Output: Integer 0–100 per site and tunnel
Levels: low (0–25), moderate (26–50), elevated (51–75), high (76–100)
Signal attribution: JSON list of contributing factors stored per score record

Incident Generation

Incidents are created by the EventCorrelationEngine, not the prediction worker directly. The prediction worker feeds the anomaly table which the correlation engine then processes on demand.

Air-Gap Readiness

Plutopus is designed to operate in fully isolated, offline environments.

Offline Bundle

The distribution/pack-offline-bundle.sh script assembles a self-contained deployment archive:

./distribution/pack-offline-bundle.sh
# Produces: plutopus-offline-bundle.tar.gz
# Checksum: distribution/checksums/plutopus-offline-bundle.tar.gz.sha256

Bundle contents:

distribution/docker-images/ — Docker image tarballs
distribution/models/ — Ollama model export/import scripts
distribution/runbooks/ — Offline copies of all runbooks
distribution/deployment/helm/ — Bundled Helm chart

Model Packaging

# Export a locally-pulled Ollama model
./distribution/models/export-model.sh qwen:0.5b ./models
# Produces: ollama_model_qwen_0.5b.tar + .sha256

# Import on target host
./distribution/models/import-model.sh ./models/ollama_model_qwen_0.5b.tar

AIRGAP_MODE

Setting AIRGAP_MODE=true signals the platform to enforce offline constraints. Verification:

AIRGAP_MODE=true ./scripts/airgap/verify.sh
# Produces: airgap-report.md

The verification script checks:

AIRGAP_MODE environment variable is set
Outbound HTTP is not reachable
All model storage is local

Offline Deployment

tar -xzf plutopus-offline-bundle.tar.gz
docker load -i distribution/docker-images/plutopus_images.tar
AIRGAP_MODE=true docker compose up -d

Security

Authentication

Standard: HS256 JWT tokens issued by create_access_token() with 60-minute expiry
Key Rotation: Multiple signing keys supported via JWT_ROTATION_SECRETS env var (comma-separated). Decoding is attempted against each key in order.
Minimum Key Length: Keys shorter than 32 characters raise a ValueError at startup — the API will not start with a weak secret.

RBAC

Three roles enforced at the route level via RoleChecker FastAPI dependencies:

Role	Capabilities
`viewer`	Read-only access to inventory, metrics, incidents, forecasts
`operator`	viewer + incident correlation triggers, copilot queries, webhook exports
`admin`	operator + audit log access, all management operations

Audit Logging

Every authentication event, resource access, and export action is recorded to the immutable audit_logs table via log_audit_event(). Fields: timestamp, username, action, resource, resource_id, result, source_ip.

Network Isolation

Kubernetes NetworkPolicy resources in infrastructure/k8s/network-policies/:

default-deny-all.yaml — Denies all ingress and egress by default in the plutopus namespace
allow-internal.yaml — Permits API pods to communicate with the database pods only

Input Validation

All API inputs are validated through Pydantic model schemas before handler execution. SQLAlchemy parameterized queries prevent SQL injection.

Observability

Prometheus

The /metrics endpoint (no authentication required) exposes:

Metric	Type	Description
`api_requests_total`	Counter	Total API requests by method, endpoint, status
`api_request_latency_seconds`	Histogram	Request processing duration by method, endpoint
`prediction_jobs_total`	Counter	Prediction worker job completions by status
`copilot_queries_total`	Counter	Copilot request count by status
`incidents_generated_total`	Counter	Incidents created by severity
`webhook_delivery_total`	Counter	Outbound webhook deliveries by status

Prometheus is configured to scrape the API every 15 seconds (infrastructure/monitoring/prometheus.yml).

Grafana

Pre-configured dashboard (infrastructure/monitoring/grafana/dashboards/system_health.json) with panels for:

API request rate (by endpoint and status code)
Correlated incident severity distribution

Grafana is available at http://localhost:3001 (admin/admin).

Health Check

curl http://localhost:8000/health
# {"status": "healthy"}

Deployment

Prerequisites

Docker Engine 24+ and Docker Compose v2
Node.js 20+ (dashboard development only)
Python 3.9+ (development/testing only)

Quick Start (Docker Compose)

# Clone the repository
git clone https://github.com/ayushkumar2601/Plutopus.git
cd Plutopus

# Configure environment
cp .env.example .env

# Start all 13 services
docker compose up -d

# Verify services are healthy
docker compose ps
curl http://localhost:8000/health

Service ports after startup:

Service	Port	URL
FastAPI	8000	http://localhost:8000
Dashboard	3000	http://localhost:3000
Grafana	3001	http://localhost:3001
Prometheus	9090	http://localhost:9090
TimescaleDB	5432	—
Redpanda (Kafka)	19092	—
Ollama	11434	http://localhost:11434
Qdrant	6333	http://localhost:6333

Seed Demo Data

# Seed the lab topology (sites, devices, interfaces, tunnels)
docker compose exec api python services/topology/seed.py

# Inject synthetic telemetry
python scripts/generate-demo-telemetry.py

Helm Deployment (Kubernetes)

# Deploy to a Kubernetes cluster
helm install plutopus ./infrastructure/helm/ \
  --namespace plutopus \
  --create-namespace \
  --set image.tag=latest

# Apply network isolation policies
kubectl apply -f infrastructure/k8s/network-policies/

Offline Deployment (Air-Gapped)

# On internet-connected machine:
./distribution/pack-offline-bundle.sh

# Transfer plutopus-offline-bundle.tar.gz to isolated host
# On isolated host:
tar -xzf plutopus-offline-bundle.tar.gz
docker load -i distribution/docker-images/plutopus_images.tar
./distribution/models/import-model.sh ./distribution/models/ollama_model_qwen_0.5b.tar
AIRGAP_MODE=true docker compose up -d

Development Setup

Requirements

Python 3.9+
Node.js 20+
Docker + Docker Compose

Backend

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# Install shared packages (editable)
pip install -e packages/shared
pip install -e packages/schemas

# Install API dependencies
pip install -e apps/api

# Install prediction service
pip install -e services/prediction

# Install copilot service
pip install -e services/copilot

# Start infrastructure services only
docker compose up postgres redpanda qdrant ollama -d

# Run the API locally
cd apps/api/src && uvicorn main:app --reload --port 8000

Frontend

cd apps/dashboard
npm install
npm run dev
# Dashboard available at http://localhost:3000

Environment Variables

Variable	Default	Description
`DATABASE_URL`	`postgresql://postgres:postgres@localhost:5432/plutopus`	TimescaleDB connection string
`REDPANDA_BROKERS`	`localhost:19092`	Kafka-compatible broker address
`OLLAMA_BASE_URL`	`http://localhost:11434`	Ollama inference server URL
`OLLAMA_MODEL`	`qwen:0.5b`	Model name for copilot queries
`JWT_SECRET`	—	JWT signing key (min. 32 characters)
`JWT_ROTATION_SECRETS`	—	Comma-separated rotation key list
`PREDICTION_INTERVAL`	`300`	Prediction worker sleep interval (seconds)
`AIRGAP_MODE`	`false`	Enables offline-only enforcement
`NEXT_PUBLIC_API_URL`	`http://localhost:8000`	API URL for dashboard

Testing

# Activate virtual environment
source .venv/bin/activate

# Run all tests
pytest tests/

# Run with coverage report
pytest --cov=apps/api --cov=services/ --cov-report=term-missing tests/

# Run a specific phase suite
pytest tests/test_phase5.py -v

Test Coverage Summary

Module Group	Tests	Coverage
API Gateway (endpoints, auth, metrics)	15	~91%
Topology Engine (graph, health, intelligence)	7	~93%
Prediction Engine (forecasting, anomaly, risk)	7	~88%
Copilot Service (context, LLM, retrieval)	7	~93%
Incident Management + Correlation Engine	9	~92%
Audit Logging + Air-Gap (Phase 6)	4	~87%
Total	45	90%

Test Organization

File	Scope
`test_api.py`	Core API endpoint integration tests
`test_normalization.py`	Telemetry normalization logic
`test_phase2.py`	Topology graph, health scoring, path analysis
`test_phase3.py`	Forecasting, anomaly detection, risk scoring
`test_phase4.py`	Copilot context, LLM fallback, retrieval
`test_phase5.py`	Incident correlation, RBAC, webhooks
`test_phase6.py`	Audit logs, JWT rotation, air-gap mode
`test_seeding.py`	Lab topology seeder
`test_topology.py`	NetworkX graph construction

Performance

Benchmarks measured on local development hardware (Apple M-series, 16 GiB RAM) during integration testing:

Operation	Measured Latency	Target
API health check (`/health`)	< 1ms	< 10ms
JWT token validation	~0.8ms	< 100ms
Incident correlation run	~22ms	< 2000ms
Incident priority scoring	~15ms	< 1000ms
Outbound webhook dispatch	~10ms (mock)	< 3000ms
Copilot context assembly	~45ms	< 500ms
Ollama inference (qwen:0.5b)	~800ms–2000ms	< 4000ms
Copilot fallback response	< 5ms	< 100ms
Forecast generation (per entity)	~3ms	< 50ms
Air-gap verification script	~3s	< 60s
Full test suite (45 tests)	~1.3s	—

Production Readiness

Area	Status	Notes
Air-Gap Deployment	✅ Ready	Full offline bundle with model packaging and checksum verification
Security	✅ Hardened	JWT with rotation, RBAC, 32-char key enforcement, audit logs
Backup Strategy	✅ Implemented	`pg_dump` automation, restore scripts, validation reporter
Recovery Procedures	✅ Documented	Runbooks in `docs/runbooks/disaster-recovery.md`
Monitoring	✅ Operational	Prometheus scraping + Grafana dashboards
Compliance	✅ Documented	`docs/compliance/compliance-readiness.md` (AU-2, SC-7, MP-6)
Horizontal Scaling	✅ Documented	`docs/capacity-planning.md` (100–5000 devices)
Database Upgrade	✅ Scripted	`scripts/upgrade/` with pre-upgrade backup + rollback
Kubernetes Deploy	✅ Ready	Helm chart v0.1.0 with NetworkPolicies

Roadmap

Completed Phases

Phase	Objective	Key Deliverables	Status
Phase 1	Telemetry Foundation	Redpanda pipeline, TimescaleDB, FastAPI scaffold, Demo generator	✅ Complete
Phase 2	Topology Intelligence	NetworkX graph, site health scoring, path analysis, topology dashboard	✅ Complete
Phase 3	Predictive Analytics	Linear forecasting, Z-score anomaly, 0-100 risk scores, explainability	✅ Complete
Phase 4	AI Copilot	Context engine, Ollama integration, runbook retrieval, chat dashboard	✅ Complete
Phase 5	Workflow Automation	Event correlation, incident management, RBAC, Prometheus, Helm, webhooks	✅ Complete
Phase 6	Air-Gap & Hardening	Offline bundle, model packaging, audit logs, K8s policies, capacity planning	✅ Complete

Future Work

The following capabilities are not yet implemented and represent realistic next steps:

Multi-tenancy: Organization-scoped data isolation within a shared database
OAuth 2.0 / SSO: OIDC integration to replace local JWT issuance
High-Availability Deployments: Multi-replica services with shared session state
Distributed LLM Inference: vLLM or Triton backends for larger model support
Automated Topology Discovery: SNMP/gRPC-based device discovery to replace YAML seeding
Multi-Cluster Support: Federation of multiple Plutopus instances across regions

Contributing

Contributions are welcome. Please follow this workflow:

Fork the repository and create a feature branch from main
Read the architecture documents in docs/architecture/ before implementing
Check existing ADRs in docs/adr/ to understand prior decisions
Write tests for any new module. Coverage regression below 85% will block merge
Update documentation: documentation.md must reflect any state change
Commit convention: Use type(scope): description — e.g., feat(copilot): add memory persistence

Development Standards

Python type annotations are required on all function signatures
All API responses must use defined Pydantic schemas
No external HTTP calls from within the API process (copilot Ollama calls are via services/copilot/llm/)
Database queries must go through the packages/shared ORM layer

Running Security Audit

./scripts/security-audit/audit.sh
# Produces: security-audit-report.md

License

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Built for NOC teams operating real SD-WAN and MPLS networks.

Documentation · API Reference · Issues

Name		Name	Last commit message	Last commit date
apps		apps
distribution		distribution
docs		docs
infrastructure		infrastructure
packages		packages
scripts		scripts
services		services
tests		tests
.env		.env
.env.example		.env.example
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
Makefile		Makefile
README.md		README.md
airgap-report.md		airgap-report.md
backup-validation-report.md		backup-validation-report.md
docker-compose.yml		docker-compose.yml
documentation.md		documentation.md
pitch.md		pitch.md
plutopus-offline-bundle.tar.gz		plutopus-offline-bundle.tar.gz
security-audit-report.md		security-audit-report.md
test_bug.py		test_bug.py

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Plutopus

AI-Powered Predictive Network Operations Platform

Table of Contents

Vision

Platform Overview

High-Level Architecture Diagram

Key Capabilities

System Architecture

1. Logical Layer Architecture

2. Telemetry Data Flow

3. Copilot Request Flow

4. Prediction Pipeline

5. Incident Correlation Pipeline

6. Air-Gap Deployment Architecture

Technology Stack

Repository Structure

Core Components

Telemetry Engine

Prediction Engine

Correlation Engine

AI Copilot

Incident Management

Monitoring Stack

Deployment Framework

Database Design

API Reference

AI Copilot

Request Flow

Available Runbooks

Predictive Analytics

Forecasting

Anomaly Detection

Risk Scoring

Incident Generation

Air-Gap Readiness

Offline Bundle

Model Packaging

AIRGAP_MODE

Offline Deployment

Security

Authentication

RBAC

Audit Logging

Network Isolation

Input Validation

Observability

Prometheus

Grafana

Health Check

Deployment

Prerequisites

Quick Start (Docker Compose)

Seed Demo Data

Helm Deployment (Kubernetes)

Offline Deployment (Air-Gapped)

Development Setup

Requirements

Backend

Frontend

Environment Variables

Testing

Test Coverage Summary

Test Organization

Performance

Production Readiness

Roadmap

Completed Phases

Future Work

Contributing

Development Standards

Running Security Audit

License

About

Resources

Stars

Watchers