Afresh Tech Stack

AI platform for grocery operations and fresh-food supply chain

Software Development San Francisco, California 51–200 employees Founded 2017 Privately Held

Afresh operates a machine-learning platform purpose-built for grocery—forecasting, ordering, and production decisions across produce, meat, and center-store categories. The stack reflects a data-intensive, real-time operation: Python + PostgreSQL + Databricks + Snowflake for model training, paired with TypeScript + React frontends and Kubernetes infrastructure for deployment at scale. Active hiring is heavily engineering-focused (13 of 19 roles), tilted toward senior and staff levels, suggesting they're stabilizing a complex inference and data-integration layer while scaling a high-revenue ordering product.

Tech Stack 25 technologies

Core StackSlack TypeScript JavaScript React Python PostgreSQL GraphQL dbt Databricks PySpark Snowflake Terraform Kubernetes Datadog Sentry Salesforce HubSpot LinkedIn Recruiter Stack Overflow Discord Azure Azure Kubernetes Service Entra

What Afresh Is Building

◆Challenges

Scaling real-time inference
Optimizing in-store operations
Stabilizing food supply chain
Scaling high-revenue ai ordering product
Improve observability and operational readiness
Rendering complex real-time data at scale
Closing ml platform gaps
Rebuilding distributed inference layer
Scaling customer data integration
Implement security controls for sensitive data

▲Active Projects

Analytics and monitoring system
Generalize model configuration feature
Corporate hub web platform
Production planning tool for store associates
Distributed inference layer rebuild
Model configuration and deployment redesign
Real-time inference architecture
Next-generation ordering product
Customer integration tooling
Etl development for billions of records

Hiring Activity

Accelerating20 roles · 20 in 30d

Department

Engineering

Data

Sales

Product

Seniority

Senior

Mid

Staff

Lead

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About Afresh

Afresh builds AI forecasting and ordering software for grocery retailers, helping store managers and distribution centers make decisions about purchasing, inventory, and production. Founded in 2017, the company serves major chains across 40 US states, supporting over 12,500 store departments with real-time demand prediction and supply optimization. The platform spans the full grocery operation: fresh perimeter (produce, meat), center-store categories, store-level POS integration, and distribution-center planning. Revenue scales with order volume and waste reduction delivered; the technical roadmap prioritizes real-time inference performance, customer data integration, and observability as the product expands.

HeadquartersSan Francisco, California

Company Size51–200 employees

Founded2017

Hiring MarketsUnited States, Canada

Frequently Asked Questions

What tech stack does Afresh use?

Afresh runs Python, PostgreSQL, Databricks, and Snowflake for data pipelines and ML training; TypeScript and React for web interfaces; Kubernetes and Azure for production infrastructure; dbt for transformation; and Datadog and Sentry for observability.

How many grocery stores does Afresh support?

Afresh supports more than 12,500 departments across 40 states, partnering with retailers including Albertsons Companies, Stater Bros., Meijer, and Wakefern.

How this profile is built

Afresh'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.