echoloc

AgFirst Farm Credit Bank Tech Stack

Wholesale agricultural lender modernizing legacy systems to Salesforce and cloud infrastructure

Banking Columbia, South Carolina 201–500 employees Founded 1916 Privately Held

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.

Tech Stack 57 technologies

Core StackSalesforce MuleSoft .NET SQL Server AWS Docker Kubernetes Terraform Apache Spark Kafka dbt Apache Airflow Snowflake BigQuery Redshift TensorFlow PyTorch scikit-learn Python React nCino AWS API Gateway Bloomberg Microsoft Access Azure OpenAI API Cornerstone OnDemand Articulate 360 Camtasia Cornerstone+23 more
ReplacingDynamics 365

What AgFirst Farm Credit Bank Is Building

Challenges

  • Complex loan transaction servicing
  • Payment delays causing penalties
  • Data integrity safeguarding
  • Quality assurance improvement program
  • Maturing tprm program
  • Hipaa compliance
  • Overdue reporting items
  • Maintaining compliance maturity
  • Migrating legacy crm to salesforce
  • Conflict resolution during releases

Active Projects

  • Quality assurance improvement program
  • Transition from microsoft dynamics to salesforce
  • Crm roadmap definition
  • Cloud security architecture standards
  • Cloud resource automation with iac
  • Cloud monitoring and alerting

Hiring Activity

Accelerating15 roles · 9 in 30d

Department

Engineering
3
Finance
3
Product
3
Security
3
Ops
2
Design
1
HR
1

Seniority

Senior
12
Mid
2
Lead
1
Manager
1
Company intelligence

Find more companies like AgFirst Farm Credit Bank by tech stack, pain points and active projects

Get started free

About AgFirst Farm Credit Bank

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.

HeadquartersColumbia, South Carolina
Company Size201–500 employees
Founded1916
Hiring MarketsUnited States

Frequently Asked Questions

What is AgFirst Farm Credit Bank's tech stack?

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.

Is AgFirst replacing Dynamics 365?

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.

How this profile is built

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.