Rimba

Core Features

Rimba.ai is built around automating the collection, verification, reconciliation, and reporting of operational data used in renewable fuel and environmental compliance programs. Its main features include:

• AI-Powered Document Processing — Extracts and validates data from bills of lading, sustainability declarations, invoices, PTDs, scale tickets, utility bills, and other operational documents using OCR and large language models.

• LCFS, RFS, and ISCC Reporting — Supports compliance workflows for renewable fuel and sustainability certification programs including LCFS, RFS, and ISCC reporting requirements.

• Automated Reconciliation — Cross-checks supplier invoices, shipment records, purchase orders, and operational data to identify discrepancies before reporting and audits.

• Industrial Data Integration — Connects with SCADA systems, data historians, ERP platforms, SharePoint environments, and operational monitoring tools to centralize compliance data.

• Predictive Compliance Monitoring — Uses AI-driven monitoring and anomaly detection to flag incomplete records, out-of-range values, and potential compliance risks in near real time.

• Audit Preparation & Traceability — Organizes documentation and reporting records into audit-ready workflows intended to reduce manual preparation work and improve traceability across renewable fuel supply chains.

Closing Insights

Rimba was founded by Timothy Daniel and Akshay Sharma, combining backgrounds in energy-sector compliance, legal operations, and AI-based document automation. Daniel previously worked in legal counsel and compliance roles within energy and mining supply chains, while Sharma worked on AI-driven document processing systems before co-founding the company.

The platform focuses particularly on renewable fuels, renewable natural gas (RNG), renewable diesel (RD), sustainable aviation fuel (SAF), and related industrial operations where compliance requirements intersect with operational data collection. Rimba functions less as a sustainability disclosure platform and more as an operational compliance layer connecting fragmented industrial systems with audit-ready reporting workflows.

Recent company materials have emphasized AI-assisted reporting, predictive analytics, industrial data monitoring, and automated document parsing for environmental compliance. The company has participated in Y Combinator’s Spring 2025 batch and has highlighted integrations with industrial platforms such as AVEVA, Siemens, Ignition, and Rockwell Automation.

For organizations operating under LCFS, RFS, ISCC, or related renewable fuel programs, Rimba may appeal particularly to teams seeking to reduce manual compliance work while improving auditability and operational data consistency across complex industrial environments.