Harnessing the POWER of AI
to revolutionize your business

Challenge

In complex energy operations—such as oilfield services and produced water management—traditional administrative workflows required manual reconciliations across up to 23 separate approval steps, leading to payment delays of 60 to 90 days, data discrepancies, and high dispute-resolution costs.

Solution

The Blockchain for Energy (B4E) consortium engineered a decentralized platform integrating real-time telemetry from field IoT sensors directly with custom Smart Contracts.

• Automated Verification: Telemetry devices automatically measure volume, weight, and delivery events directly at the wellhead or transport tanker.
• Programmable Logic: The Smart Contract independently validates field data against pre-agreed master service agreements (MSAs).
• Instant Execution: Once metrics are verified, the contract automatically triggers payment settlements and notarizes the event on an immutable ledger.

Results

• Efficiency: Reduced multi-party approval workflows from 23 manual steps down to just 4.
• Auditability: Created a 100% tamper-proof, real-time audit trail accessible by operators, haulers, and regulators.
• Dispute Elimination: Replaced manual invoice reconciliation with single-source-of-truth cryptographic verification.

Executive Summary

The City Highway Concessionarie is responsible for the management, construction, and maintenance of the highway network in the City of Buenos Aires. To elevate its infrastructure management, the company implemented an AI Agent designed to automate road surveys, eliminate manual inspection tasks, and transition toward a predictive maintenance model focused on ensuring safer and smoother highways.

Challenges

• Limited access to information: Key data within the GIS system of the City Highway Concessionarie was restricted to technical staff, making it difficult for other departments to quickly query highway information.
• Manual review process: Inspecting asset conditions relied on human visual review of thousands of field-captured photos, which consumed valuable time and caused delays.
• Reactive infrastructure management: The detection of pavement anomalies (potholes and cracks), signage issues, guardrails, and expansion joints typically occurred after incidents had already happened, affecting traffic flow.

Solution

An Intelligent AI Agent was developed and directly integrated into the City Highway Concessionarie Maps database, Microsoft Teams, and Outlook:

• Conversational Chatbot in Teams (NLP): Enables any employee, without technical expertise, to query asset details using natural language (e.g., "Show me the status of signs on Au. Illia at km 5"), receiving instant responses with photos and location details.
• Computer Vision: Massive processing of 360°/GoPro video frames to automatically detect and classify pavement distress, guardrail conditions, crash cushions, and signage.
• Georeferenced Alert System: Automated email notifications featuring photographic evidence, traffic direction, exact kilometer mark, and detected incidence to immediately dispatch maintenance crews.

Results

• Shift to Proactive Maintenance: The maintenance team evolved from a reactive response model to taking proactive action, driven by continuous automated alerts.
• Substantial reduction in damaged assets: Early detection and rapid repair significantly reduced the volume of severe or accumulated infrastructure defects across the highway network.
• 90% reduction in time spent searching, auditing, and querying road infrastructure information.
• 100% democratization of road asset data across the organization.