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Perfect order fulfillment: a Tevec case study

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Algorithmia is fortunate to work with companies across many industries with varied use cases as they develop machine learning programs. We are delighted to showcase the great work one of our customers is doing and how the AI Layer is able to power their machine learning lifecycle.

Tevec is a Brazil-based company that hosts Tevec.AI, a supply chain recommendation platform that uses machine learning to forecast demand and suggest optimized replenishment/fulfillment order for logistics chains. Put simply, Tevec ensures retailers and goods transport companies deliver their products to the right place at the right time.

In founder Bento Ribeiro’s own words, the “Tevec Platform is a pioneer in the application of machine learning for the recognition of demand behavior patterns, automating the whole process of forecasting and calculation of ideal product restocking lots at points of sale and distribution centers, allowing sales planning control, service level, and regulatory stocks.”

Tevec runs forecasting and inventory-optimization models and customizes user permissions so they can adjust the parameters of their inventory routine, such as lead times, delivery dates, minimum inventory, and service levels. Users can fine-tune the algorithms and adapt for specific uses or priorities. 

The challenge: serving and managing at scale

Initially, Tevec was embedding ML models directly into its platform, causing several issues:

  • Updating: models and applications were on drastically different update cycles, with models changing many times between application updates
  • Versioning: model iterating and ensuring all apps were calling the most appropriate model was difficult to track and prone to error
  • Data integrations: manual integrations and multi-team involvement made customization difficult
  • Model management: models were interacting with myriad endpoints such as ERP, PoS systems, and internal platforms, which was cumbersome to manage

Algorithmia provides the ability to not worry about infrastructure and guarantees that models we put in production will be versioned and production-quality.”  

Luiz Andrade, CTO, Tevec

The solution: model hosting made simple with serverless microservices

Tevec decoupled model development from app development using the AI Layer so it can seamlessly integrate API endpoints, and users can maintain a callable library of every model version. Tevec’s architecture and data science teams now avoid costly and time-consuming DevOps tasks; that extra time can be spent on building valuable new models in Python, “the language of data science,” Andrade reasons. That said, with the AI Layer, Tevec can run models from any framework, programming language, or data connector—future-proofing Tevec’s ML program.

With Algorithmia in place, Tevec’s data scientists can test and iterate models with dependable product continuity, and can customize apps for customers without touching models, calling only the version needed for testing. 

Algorithmia’s serverless architecture ensures the scalability Tevec needs to meet its customers demands without the costs of other autoscaling systems, and Tevec only pays for compute resources it actually uses.

Looking ahead

Tevec continues to enjoy 100-percent year-on-year growth, and as it scales so will its ML architecture deployed on Algorithmia’s AI Layer. Tevec is planning additional products beyond perfect order forecasts and it is evaluating new frameworks for specific ML use cases—perfect for the tool-agnostic AI Layer. Tevec will continue to respond to customer demands as it increases the scale and volume of its service so goods and products always arrive on time at their destinations.

Algorithmia is the whole production system, and we really grabbed onto the concept of serverless microservices so we don’t have to wait for a whole chain of calls to receive a response.”

Luiz Andrade, CTO, Tevec

Read the full Tevec case study.