Product descriptions:
A2go, a supply chain AI specialist founded in 2018, helps companies convert data into actionable decisions through agentic AI solutions tailored to each customer’s technology landscape. For one customer — a major Brazilian beef producer — A2go chose to implement its agentic AI and data orchestration on the Databricks Data Intelligence Platform and Databricks Apps to unify its set of disparate tools and complex deployment cycles. Using Databricks, A2go cut application delivery time by 50% while enabling the customer to reduce forecast cycles from 28 hours to under 1 hour and simulate pricing scenarios across 60,000+ SKUs in minutes.
Disconnected tools slow AI application delivery
A2go specializes in supply chain AI solutions, developing agentic AI applications that autonomously plan, reason and act on complex supply chain challenges. Its client — one of Brazil’s largest beef producers, with operations spanning multiple countries, 65 production facilities, 17 sales channels and over 150,000 customers — needed sophisticated AI capabilities to manage rising volatility in demand, pricing and logistics across tens of thousands of SKUs. However, A2go’s existing technical architecture created critical barriers that limited its ability to scale and deliver value efficiently to this customer.
In this use case, both A2go and the beef producer had strategic alignment around Databricks, making the Databricks Data Intelligence Platform and Databricks Apps the optimal choice for delivering agentic AI and data orchestration. The customer needed to integrate high volumes of data from warehouse management systems, ERP platforms, CRM databases, and external sources like weather patterns, market fluctuations, and feed costs. Because Apache Spark™ and lakehouse architecture—the foundation of the Data Intelligence Platform—fit those needs so well, Databricks emerged as the most effective foundation for this engagement.
Before Databricks, A2go's technology stack was not well-suited to deliver AI agents to this customer. "We faced problems managing and integrating disparate tools and performance optimization, and we lacked Spark in our stack," explained Rafael Jacomeli, Technical Implementation Lead at A2go. With Databricks, A2go was able to process data at scale and deliver real-time agility, whereas before, reacting to changes in data could require days.
Streamlining development with an integrated platform
To eliminate a fragmented tool chain, A2go standardized this project on the Databricks Data Intelligence Platform, using Unity Catalog for centralized governance and the Lakehouse with a medallion architecture to unlock and transform legacy data for AI applications. Python, Spark and Lakeflow Jobs handled large-scale processing and batch orchestration natively.
The integrated Databricks environment allowed unprecedented component reuse and team efficiency gains. A2go restructured delivery into specialized workstreams — data engineer, data scientist and at least one developer — dramatically reducing project staffing while increasing quality and speed for this specific implementation. “Databricks helped us standardize and repeat the application creation process, enabling scalability and productivity while maintaining governance standards,” explained Cesar Oliveira, COO and Solution Architect at A2go.
The ability to iterate and test apps directly within the Databricks workspace cut overall delivery time by 50% in this case, illustrating what is possible when A2go aligns its agentic AI framework with Databricks Apps.
Delivering measurable business transformation at enterprise scale
For their beef producer customer, the business impact of Databricks Apps has been transformative. A2go built agentic AI applications that run entirely within Databricks, enabling autonomous price optimization, demand forecasting and inventory allocation across more than 60,000 SKUs spanning 65 production facilities and 17 sales channels. These AI agents continuously analyze regional demand patterns, pricing elasticities, product shelf life, weather conditions, market fluctuations and feed costs to generate actionable recommendations. Regional demand and pricing forecasts that previously took 28 hours are now completed in under one hour. Pricing scenarios across all SKUs can now be simulated across multiple markets in minutes rather than days.
The true transformation lies in how Databricks Apps democratized access to these AI-powered insights across the beef producer’s organization. At least 25 users across the market intelligence department now engage with the platform daily and concurrently, each running complex simulations in a safe, governed environment without requiring data engineering support. This represents a dramatic shift from their previous state, where pricing analysts would spend hours manually extracting data into Excel, running calculations offline and distributing static recommendations that were often outdated by the time they reached sales teams.
What was once a centralized, batch-driven process has become a self-service capability, allowing pricing decisions to be made simultaneously by distributed teams with access to the same trusted data and AI models. The beef producer can now dynamically respond to shifts in market conditions, translating operational agility into improved pricing accuracy, optimized inventory allocation and reduced waste.
This successful deployment has positioned A2go to scale its agentic AI solutions across industries with the same efficient, repeatable model. The combination of Unity Catalog’s governance, Databricks Apps deployment simplicity and the lakehouse architecture’s performance creates a foundation that A2go can apply to new customers and use cases with increasing efficiency. Each new implementation leverages components from previous projects, creating a compounding effect that allows A2go to democratize AI-driven decision-making across more enterprises, faster, with the same lean team.