Wholesale agricultural lender modernizing legacy systems to Salesforce and cloud infrastructure
AgFirst is a $48 billion wholesale lender serving the Farm Credit system across 15 states and Puerto Rico. The tech stack reveals a mid-migration posture: Salesforce, nCino, and MuleSoft anchor lending operations, while a parallel cloud-native layer (AWS, Kubernetes, Terraform, Kafka, Snowflake) handles analytics and data pipelines. Active replacement of Dynamics 365 with Salesforce, combined with six active projects around CRM transition, cloud security standards, and infrastructure-as-code, shows AgFirst prioritizing operational modernization and compliance maturity.
AgFirst Farm Credit Bank is the wholesale funding and technology backbone for agricultural and rural lenders across 15 states and Puerto Rico, originating loans to farms, rural homebuyers, and land buyers through a cooperative network. Founded in 1916, it operates as a privately held entity within the nationwide Farm Credit system, providing correspondent lending, technology services, and business support to local partner lenders. The organization manages complex loan transaction servicing, payment workflows, and HIPAA-compliant data handling at scale. With 201–500 employees based in Columbia, South Carolina, AgFirst invests in leadership development, flexible work arrangements, and cross-functional teams spanning finance, engineering, product, security, and compliance.
Core systems: Salesforce, nCino (lending platform), MuleSoft (API management), Bloomberg, SQL Server. Cloud: AWS, Azure, Kubernetes, Terraform. Data: Snowflake, BigQuery, Redshift, Apache Spark, Kafka, dbt, Airflow. ML: TensorFlow, PyTorch, scikit-learn, OpenAI API. Training: Cornerstone OnDemand.
Yes. Dynamics 365 is listed as actively being replaced. A major CRM transition project from legacy systems to Salesforce is underway, with Salesforce, nCino, and MuleSoft forming the target architecture for lending operations.
AgFirst Farm Credit Bank's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.