use case

AI-Driven Operational Intelligence & Automation

The challenge

Organizations operating in fast-moving industries face a common set of hurdles:

Fragmented systems lead to duplicated effort and inconsistent data.
Manual processes for handling financial workflows such as invoice validation, fleet management approvals, or support escalations.
Limited accessibility of documentation and contextual company data for partners and employees.

Scalability challenges when demand spikes (e.g., monthly closings, seasonal operations, or large fleet coordination).

Delivering an AI-Activated Company Context Platform that unifies multiple AI applications under a secure and scalable infrastructure:

AI Chat Bot for Business Partners – providing instant access to over 200K Confluence documents, embedding links and references into answers for validation, and enabling escalation with full chat history.
AI Automated Invoice Processing – extracting key details (amount, currency, due date) from invoices, integrating with email systems and ERP for end-to-end automation, and offering quick UI validation plus CSV/Excel exports.
Fleet Operational Services – supporting real-time GPS tracking, invoicing, secondary logistics workflows, operational approvals, and notifications for both internal teams and Rompetrol partners.
AI & Company Semantic Layer – leveraging contextual company data with LLMs, OCR, recognition, transcription, and real-time governance through RLS policies. Secure embedding into existing company tools ensures seamless adoption without forcing transitions.

Reduced operational costs by automating invoice validation and payment processing.
Increased efficiency for business partners who can now self-serve documentation instead of relying on support calls.
Improved fleet reliability and transparency, thanks to unified invoicing, approval workflows, and live tracking.
Faster decision-making with real-time AI context activation and cross-system semantic search.
Higher compliance and security with strict role-based access and RLS policies embedded across all applications

Processing time for invoices reduced by 70%, freeing finance teams from manual data entry.
Over 80% of documentation queries were resolved instantly through the chatbot without escalating to human support.
Fleet operations gained real-time visibility, enabling proactive interventions and cutting delays in logistics workflows.

Cloud Platforms: AWS, Azure, GCP (cloud-independent architecture).
AI Models: LLMs, OCR, recognition & detection, transcription.
Data Architecture: Semantic layer with Row-Level Security (RLS) policies.
Integration: Confluence (200K+ docs), ERP systems, email automation, fleet GPS and logistics tools.
Infrastructure: Scalable, secure embedding into existing company tools; service health & model accuracy monitoring.