Review of Kardinal.ai, Supply Chain Software Vendor

By Léon Levinas-Ménard

Last updated: April, 2025

Kardinal.ai is a software company founded in 2015 that delivers an always‑on, cloud‐based route optimization and logistics intelligence platform for last mile delivery operations. By harnessing advanced combinatorial optimization, machine learning, and real‑time data integration, Kardinal.ai continuously refines delivery tours—dynamically adapting to traffic fluctuations, operational constraints, and unpredictable events—to improve resource allocation, reduce operating costs, and augment human decision‑making. Backed by a series of funding rounds totaling approximately $12.6M and built on a modern tech stack featuring microservices, Kubernetes, Golang, and even Rust, the company positions itself as a nimble yet robust solution for complex logistics challenges in today’s fast‑paced supply chain environment.

Company Background & Funding

Kardinal.ai was established in 2015 by Jonathan Bouaziz, Cedric Hervet, and Hugo Farizon, born from the convergence of deep mathematical expertise and firsthand insights into logistical challenges. The company’s genesis and continuous development are detailed on their “À propos” page¹ and have been further chronicled through profiles on PitchBook² and Tracxn³. The vendor has raised roughly $12.6M—with a noteworthy Series A round of about $10.4M in 2022—signaling investor confidence even as it remains focused on a niche within last mile optimization.

Product Overview: What Kardinal.ai Delivers

Kardinal.ai’s SaaS platform offers real‑time route optimization designed to:

Optimize Last Mile Delivery: Create and adjust delivery tours dynamically by factoring in drivers’ conditions, traffic patterns, and delivery windows. This real‑time re‑optimization ensures practical route recommendations that actively respond to on‑the‑ground uncertainties⁴.
Enhance Operational Efficiency: By leveraging sophisticated algorithms, the platform claims cost reductions ranging from 10% to 40% while improving overall service quality and resource allocation. Its decision support model augments human judgment—operators review and validate suggestions, rather than relying on full automation⁴.
Seamlessly Integrate: Delivered as a cloud service with robust API integrations, the solution is engineered to plug into existing TMS, ERP, or other enterprise systems, supporting both big bang IT deployments and gradual, phased implementations⁵.

Technical & Operational Mechanisms

Core Technologies and Algorithms

Kardinal.ai’s platform is built around advanced mathematical and machine learning techniques:

Combinatorial Optimization: The engine handles “an unlimited number of constraints” to construct delivery routes that mirror real‑world variables, as showcased on the homepage⁶.
Machine Learning for Continuous Improvement: Field data captured via drivers’ mobile devices feeds into machine learning models that predict delivery times, identify performance patterns, and fine‑tune subsequent route calculations. This iterative process ensures that the solution leverages historical and real‑time data for increasing accuracy⁴.
Real‑Time Data Integration: Dynamic variables such as traffic conditions and delivery windows are continuously ingested, allowing immediate re‑optimization “before, during, and after” deliveries.

Deployment and Integration

The platform is offered as a SaaS solution and is designed to be effortlessly integrated via well‑documented APIs. This facilitates quick onboarding as well as hybrid and gradual IT deployments, making it possible to link the service with existing logistics systems such as TMS or ERP⁵.

Technical Stack & Team Insights

While granular details remain limited, available insights suggest the use of a modern tech stack that includes microservices orchestrated with Kubernetes and backend components developed in Golang and Rust. Team insights shared by co‑founder Hugo Farizon highlight a commitment to high‑performance, scalable systems and agile, cross‑functional development practices⁷⁸.

Real‑World Use Cases and Operational Impact

Practical deployments of Kardinal.ai’s platform have been demonstrated in several case studies:

A case study on incorporating traffic data has shown significant improvements in navigational predictions, leading to more reliable tours⁹.
Additional case studies detail scenarios in depot management and parcel delivery pricing strategies, further underlining the system’s ability to enhance operational efficiency across varied environments.
Partnerships, such as those with DPD France, underline the practical impact and external validation of the solution in diverse delivery contexts.

Kardinal.ai vs Lokad

While Kardinal.ai focuses on the operational challenges of last mile delivery, particularly dynamic route planning and real‑time logistics intelligence, Lokad represents a different paradigm within supply chain optimization. Founded in 2008, Lokad has evolved from cloud‑based forecasting into a comprehensive platform for predictive supply chain optimization that spans demand forecasting, inventory management, pricing strategies, and production planning. Lokad’s platform leverages a custom domain‑specific language called Envision and incorporates advanced techniques such as deep learning and differentiable programming to generate actionable recommendations¹⁰¹¹¹²¹³.

Key contrasts include:

• Focus Area:
Kardinal.ai is engineered exclusively for last mile delivery, whereas Lokad takes a holistic view of the supply chain by integrating a broader array of decision optimizations.

• Technical Approach:
Kardinal.ai builds its strength on combinatorial and real‑time optimization of delivery tours using live data feeds. In contrast, Lokad employs probabilistic forecasting and embeds supply chain logic into its Envision DSL, enabling end‑to‑end decision automation.

• Implementation and Integration:
Both use cloud‑based, SaaS delivery models and API integrations. However, Lokad’s self‑developed platform emphasizes a custom, programmable approach to managing complex supply chain oscillations, while Kardinal.ai focuses on dynamically re‑optimizing routes to manage the variable nature of last mile delivery.

Conclusion

Kardinal.ai provides an innovative, technically robust SaaS solution for last mile delivery optimization. Its blend of advanced combinatorial optimization, machine learning, and real‑time data integration positions it as an effective augmentor of human decision‑making in logistics. Although some aspects lean on industry buzzwords and high‑level descriptions, the platform’s agile technology stack and demonstrated operational impact indicate its real‑world potential. In comparing it to a broader supply chain solution like Lokad, Kardinal.ai stands out for its laser‑focused approach to route optimization, while Lokad offers a more expansive framework for quantitative supply chain management. Companies with a primary emphasis on last mile challenges will find Kardinal.ai’s dynamic re‑optimization and agile integration particularly compelling.