Arcuni Business Intelligence Platform

Overview

The Arcuni Business Intelligence Platform addresses the challenge of gathering, analyzing, and managing company intelligence. In a landscape often dominated by broad data aggregation tools, Arcuni distinguishes itself by providing deep, company-specific insights. Understanding target companies requires more than surface-level data; it demands insights into their operations, market position, and strategic direction. This platform, developed as a Ruby on Rails application, offers a structured approach to this problem, integrating automated data acquisition with analytical capabilities.

Arcun automates the collection of intelligence data from public sources, such as company websites. Users can initiate data collection by specifying target company URLs or names, and the platform then gathers relevant information. This raw information is processed and enriched through the integration of Large Language Models (LLMs). LLMs are employed to extract insights, such as sentiment around recent news or shifts in business strategy.

A core feature is the Ideal Customer Profile (ICP) scoring system. This system matches and ranks companies against predefined criteria, aiding in strategic targeting. For example, a sales team can define an ICP based on industry, revenue, and technology stack. Arcuni then scores potential leads against these criteria.

To manage data, a customizable folder system allows for flexible organization tailored to specific research needs. This enables you to tailor data organization to projects or client engagements.

The platform’s multi-tenant architecture ensures data isolation for various organizations. Data lineage tracking maintains transparency regarding information sources and history, allowing users to trace intelligence back to its origin. Contact management features are also integrated, providing a structured way to track relationships and key personnel within target companies.

The platform generates detailed company profiles that extend beyond basic firmographics. These profiles encompass industry classifications, business model analyses, and insights into technology stacks and infrastructure choices. We also track key contacts and organizational relationships, alongside market positioning and competitive analysis data. The system also captures attributes related to organizational culture, leadership profiles, and environmental/social initiatives, providing a comprehensive view for decision-making.

In summary, the Arcuni platform offers a solution for company intelligence, streamlining data acquisition, analysis, and management.

Technical Stack

We carefully selected the technology stack for the Arcuni platform to meet specific functional and performance requirements. Each component contributes to the system’s overall capabilities and development efficiency. The chosen technologies are robust and practical. While specific versions are not detailed here to maintain focus on the architectural choices, it is crucial for deployment to pin exact versions for stability and reproducibility, a practice we rigorously followed during development.

• Backend: Ruby on Rails with PostgreSQL. Ruby on Rails offers a robust framework for rapid application development and maintainable code, which was essential for this project, enabling us to balance development speed with long-term maintainability and deliver features efficiently. PostgreSQL serves as our relational database, providing reliable storage for structured company data, chosen for its ACID compliance and extensibility over simpler alternatives.
• Analytics: ClickHouse for data analytics. We leverage ClickHouse for its exceptional performance in analytical queries, allowing us to process and aggregate large datasets efficiently for business intelligence, a critical factor given the volume of data. Its column-oriented storage provides significant advantages for OLAP workloads compared to row-oriented databases.
• Authentication: FusionAuth integration. FusionAuth provides our authentication and authorization platform, securely managing user access control within the multi-tenant architecture. We opted for a dedicated solution like FusionAuth to offload complex security concerns and leverage its robust feature set, rather than building authentication from scratch.
• Frontend: For the frontend, we chose Inertia.js with Svelte components. This combination allows us to build a reactive user interface while integrating seamlessly with Rails for routing and controllers, balancing dynamic user experiences with established backend conventions, and offering a more lightweight and performant alternative to heavier SPA frameworks. This choice minimized the cognitive overhead of a full SPA while still delivering a modern UI.
• AI/ML: LLM integration for analysis. We’ve integrated Large Language Models (LLMs) to power the platform’s AI capabilities, facilitating advanced data enrichment, sentiment analysis, and extracting nuanced insights from unstructured text data. The decision to integrate LLMs directly allows for flexible model updates and customization, rather than relying on fixed, external AI services.

In essence, our technology stack combines the agility of Ruby on Rails with specialized tools like ClickHouse for analytics and LLMs for AI. This combination ensures a powerful, scalable, and secure platform for business intelligence. Each choice reflects a deliberate trade-off to optimize for performance, security, and developer efficiency.