SignalOps

Turn application logs into actionable operational insights.

SignalOps is an AI-powered application log intelligence platform designed to help developers understand application behavior, detect operational issues, and investigate incidents.

It transforms raw application logs into structured semantic events, identifies meaningful signals, correlates related events into incidents, and uses AI-assisted analysis to provide contextual insights.

Overview

Traditional log management often requires developers to search through large volumes of raw log data to understand what went wrong.

SignalOps aims to simplify this process through a structured intelligence pipeline that combines deterministic processing with AI-assisted reasoning.

Key capabilities include:

• Log ingestion — Receive application logs from supported clients.
• Semantic parsing — Transform raw log records into structured events.
• Signal detection — Identify meaningful operational conditions.
• Incident correlation — Group related signals and events into incidents.
• AI-assisted analysis — Generate contextual insights using Amazon Bedrock.
• Operational insights — Help developers investigate application failures and understand potential causes.

How It Works

flowchart TD
    A(["Application Logs"]) --> B["Log Ingestion"]
    B --> C["Semantic Parsing"]
    C --> D["Signal Detection"]
    D --> E["Incident Correlation"]
    E --> F["AI-Assisted Analysis"]
    F --> G(["Operational Insights"])

    classDef source fill:#dbeafe,stroke:#2563eb,color:#1e3a8a
    classDef processing fill:#dcfce7,stroke:#16a34a,color:#14532d
    classDef intelligence fill:#f3e8ff,stroke:#9333ea,color:#581c87

    class A,G source
    class B,C,D,E processing
    class F intelligence

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SignalOps follows a deterministic-first approach: explicit parsing and detection logic handle predictable operations, while AI provides contextual interpretation where it adds value.

The pipeline represents the conceptual processing model rather than prescribing how individual stages are deployed or executed.

Architecture at a Glance

flowchart TB
    DEV(["Users"]) --> FE["React Frontend"]
    CLIENT(["Applications and CLI Clients"]) --> API["FastAPI Backend"]
    FE <--> API

    subgraph PIPELINE["Log Intelligence Pipeline"]
        direction LR
        ING["Log Ingestion"] --> PAR["Semantic Parsing"]
        PAR --> DET["Signal Detection"]
        DET --> COR["Incident Correlation"]
        COR --> AI["AI-Assisted Analysis"]
    end

    API --> ING

    API <--> DB[("PostgreSQL")]
    API <--> REDIS[("Redis")]
    AI <--> BEDROCK["Amazon Bedrock"]
    API --> AWS["AWS Integrations"]

    classDef app fill:#dbeafe,stroke:#2563eb,color:#1e3a8a
    classDef processing fill:#dcfce7,stroke:#16a34a,color:#14532d
    classDef intelligence fill:#f3e8ff,stroke:#9333ea,color:#581c87
    classDef infrastructure fill:#f3f4f6,stroke:#6b7280,color:#111827

    class FE,API app
    class ING,PAR,DET,COR processing
    class AI,BEDROCK intelligence
    class DB,REDIS,AWS infrastructure

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The platform uses a React-based web interface, a FastAPI backend, PostgreSQL, Redis, and AWS integrations, including Amazon Bedrock.

For detailed component relationships, data flows, and deployment design, see the architecture documentation.

Technology Stack

Component	Technology
Frontend	React, Vite, React Router
State management	Redux Toolkit, TanStack Query
Styling	Tailwind CSS
Backend	Python, FastAPI
Database and migrations	PostgreSQL, SQLAlchemy, Alembic
Supporting infrastructure	Redis (optional during development)
Authentication	JWT, Google Identity Services (GIS) Sign-In
AI analysis	Amazon Bedrock
Email services	Amazon SES
Object storage	Amazon S3
Containerization	Docker
Container image registry	Amazon Elastic Container Registry (Amazon ECR)
Cloud infrastructure	Amazon Web Services (AWS)
Testing and code quality	Pytest, Ruff, ESLint, Prettier

Repository Structure

.
├── backend/
├── frontend/
├── docs/
├── README.md
├── CONTRIBUTING.md
└── SECURITY.md

Getting Started

Prerequisites

Ensure the following tools and services are available in your development environment:

• Git — To clone and manage the repository.
• Python — Use a version compatible with the backend dependencies.
• Node.js — Use a version compatible with the frontend dependencies.
• PostgreSQL — A running database instance for backend persistence.
• Redis — Optional for development; configure it if required by the features or workflows you want to run
• AWS configuration — Required for development workflows that use AWS integrations, such as Amazon Bedrock, S3 or SES.

Install the package manager specified by the frontend configuration if it differs from npm.

Clone the Repository

git clone https://github.com/siddharthk8/SignalOps.git
cd SignalOps

Configure the Environment

Before starting the application:

1. Follow the instructions in the backend setup guide to configure the backend environment, including the PostgreSQL connection.
2. Follow the instructions in the frontend setup guide to configure the frontend environment.
3. Configure Redis only if needed for the development workflows you intend to use.
4. Configure AWS credentials and related settings.

Refer to the backend and frontend setup guides for the exact environment variables and configuration steps.

Run the Application

Once the prerequisites are installed and both environments are configured, follow the backend and frontend READMEs for the exact commands.

Project Documentation

Document	Description
Frontend documentation	Frontend setup, configuration, development, and tooling
Backend documentation	Backend setup, configuration, APIs, database, and development
Architecture	System architecture, components, and data flows
Contributing	Contribution guidelines
Security	Security policy

Engineering Principles

• Deterministic first: Prefer predictable parsing and detection logic.
• AI where it adds value: Use AI for contextual reasoning rather than replacing deterministic processing.
• Modular architecture: Maintain clear boundaries between system responsibilities.
• Structured events: Build operational intelligence on normalized data.
• Security and reliability: Validate inputs, protect sensitive information, and handle failures explicitly.
• Maintainability: Favor clear interfaces, testable logic, and focused changes.

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SignalOps turns application logs into structured signals, correlated incidents, and actionable operational understanding.