Tinamu Labs provides a LiDAR‑based inventory intelligence platform that uses plug‑and‑play drone kits or automated robotic systems to capture 3‑D data of bulk material stockpiles. Their proprietary analytics process the point clouds to deliver digital twins, volumetric measurements, unit counts and anomaly detection within two hours, all accessible via a web dashboard and API. This enables operators of mines, commodity traders and large‑scale warehouses to perform fast, accurate and repeatable inventory assessments without GPS, pilots or extensive infrastructure.
Funding
$1.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Many industrial stockpile inventories are performed manually with paper methods, resulting in inaccurate counts, safety hazards, and infrequent updates that hinder operational planning and increase costs.
Solution
Tinamu Labs offers a LiDAR‑based inventory intelligence platform that captures 3‑D data of bulk materials using plug‑and‑play drone kits or automated robotic systems. The captured point clouds are processed by proprietary analytics to generate digital twins, volumetric measurements, unit counts, and anomaly detection within two hours. Automated indoor navigation relies on computer‑vision matching of natural features, eliminating the need for GPS or skilled pilots. Results are delivered through a web dashboard and API‑integrated reports, enabling rapid, repeatable, and trustworthy inventory assessments for a range of commodities.
Target Audience
Primary customers are operators of bulk material stockpiles such as metals, minerals, fertilizers, coal, rice, and sugar, including mining companies, commodity traders, and large‑scale warehouse managers.
Features
- Portable LiDAR kit (TINAMU RMS Light) with 360° coverage and 40 m range for indoor and outdoor scanning, usable on drones or handheld
- Automated robotic monitoring system (TINAMU RMS) that conducts scans at the push of a button without GPS or pilot intervention
- Computer‑vision‑based indoor navigation algorithm that requires no local infrastructure or installations
- Fully automated cloud processing pipeline delivering >99 % accurate volumetric measurements and weight estimates in under two hours
- Real‑time digital twin generation with change tracking, anomaly detection, and semantic segmentation
- Web‑based visualization platform with customizable reports (Excel, Word, PDF) and API integration for customer systems