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Chalk Tech Stack

AI data platform for real-time feature pipelines and agent context

Software Development San Francisco, California 51–200 employees Privately Held
chalk.ai LinkedIn

Chalk builds infrastructure for AI agents and models to access real-time feature data—combining a query planning engine, streaming ingest (Kafka, Spark), and transformation layer (dbt) deployed in customer cloud environments. The hiring mix (engineering-heavy at 15 of 30 open roles, with accelerating velocity across mid and senior tiers) reflects active infrastructure work: query optimization, productionization of the first generation, and expansion into healthcare and financial-services use cases.

Tech Stack 25 technologies

Core StackFigma Sanity After Effects Rust Python Salesforce HubSpot Apache Spark Kafka dbt TypeScript Go React Terraform AWS Marketo Looker Tableau Rippling Webflow Spline Lottiefiles Outreach GCP Google Analytics 4

What Chalk Is Building

◆Challenges

  • Complexity, latency, and scale barriers
  • Fraudulent credit card swipes
  • Identity verification
  • Clean energy capture
  • Revenue growth through adoption
  • Query planning optimization
  • Increasing demo bookings
  • High-leverage paid channels
  • Distributed analytical data processing
  • Expanding into new verticals

▲Active Projects

  • Feature pipelines for healthcare, finance, recommendation systems
  • Account plans
  • Productionize first iteration
  • Real-time data infra community building
  • Use-case expansion pipeline
  • Production use-case expansion
  • Feature pipelines for cancer detection, fraud prevention, product recommendation
  • Query planning and execution engine
  • Attribution and reporting end-to-end
  • Landing-page strategy and conversion-rate optimization

Hiring Activity

Accelerating30 roles · 25 in 30d

Department

Engineering
15
Sales
8
Marketing
3
Ops
2
Design
1

Seniority

Mid
15
Senior
9
Manager
5
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About Chalk

Chalk provides a data platform purpose-built for AI agents and large language models to retrieve contextual data at inference time. The product sits between feature stores and real-time analytics, handling low-latency feature computation for use cases like fraud prevention, identity verification, and medical diagnosis. Engineering teams deploy Chalk in their own cloud (AWS or GCP), integrating it with existing data pipelines (Kafka, Spark, dbt). The company is in active growth mode across product maturity (productionizing core v1), go-to-market (expanding verticals into healthcare and finance), and geographic hiring (seven countries including Peru, Israel, Japan, and Singapore).

HeadquartersSan Francisco, California
Company Size51–200 employees
Hiring MarketsUnited States, Canada, Peru, Ireland, Israel, Japan, Singapore

Frequently Asked Questions

What is Chalk's tech stack?

Rust, Python, Go, TypeScript, and React for core infrastructure. Data layer: Kafka, Apache Spark, dbt, deployed on AWS or GCP. Sales and marketing: Salesforce, HubSpot, Outreach, Marketo.

What is Chalk working on?

Real-time feature pipelines for healthcare (cancer detection), finance (fraud prevention), and recommendations. Core platform work: query planning and execution engine optimization, productionizing first-generation infrastructure, and expanding use cases into new verticals.

How this profile is built

Chalk'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.