Able Tech Stack

Product studio building AI systems and data pipelines for partners

Software Development New York, New York 51–200 employees Founded 2008 Privately Held

Able is a product development studio where teams of designers and engineers build software and AI systems for external partners. The tech stack reveals an AI-forward operation: Claude 3.5, LangChain, and LangGraph sit alongside data infrastructure (Kafka, Snowflake, BigQuery, Airflow, Dagster), with active work on prompt engineering, graph ETL, and PDF processing. Hiring is concentrated in senior and principal engineering roles in Peru, suggesting they're scaling delivery capacity for complex, client-facing builds rather than expanding a core product.

Tech Stack 26 technologies

Core StackKafka LangChain Snowflake BigQuery Redshift Databricks Python Apache Airflow Prefect Dagster Looker Tableau Metabase Superset Apache Spark AWS Terraform Amplitude Mixpanel Cursor Claude 3.5 LangGraph PyMuPDF Kinesis GCP Segment

What Able Is Building

◆Challenges

Legacy techniques slow software delivery
Cost optimization for ai processing
Messy pdf structure
Scaling data pipelines
Data integration from multiple sources
Standardizing ai practices
Integrating ai into products

▲Active Projects

Graph etl pipeline development
Kafka-based data ingestion
Graph hygiene automation
Visual data extraction pipeline
Pdf processing pipeline
Prompt engineering for financial charts
Production ai systems for partners
Ai strategy implementation
Partner ai consulting

Hiring Activity

Accelerating5 roles · 2 in 30d

Department

Engineering

Data

Seniority

Senior

Principal

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About Able

Able operates as a product strategy and development studio for entrepreneurs and institutions building technology. Founded in 2008 and based in New York, the company fields dedicated cross-functional teams—designers, engineers, and product builders—embedded with partner organizations. Current work spans AI strategy implementation, production AI systems, data pipeline architecture, and visual data extraction. The pain-point surface (legacy delivery speed, AI cost optimization, data integration complexity) maps directly to the project mix: graph pipelines, Kafka-based ingest, PDF processing, and prompt engineering for financial use cases. With 51–200 employees across North and Latin America, Able operates on a talent-and-outcomes-first model where each team integrates directly into partner orgs.

HeadquartersNew York, New York

Company Size51–200 employees

Founded2008

Hiring MarketsPeru

Frequently Asked Questions

What is Able's tech stack?

Able uses Claude 3.5, LangChain, and LangGraph for AI; Kafka, Snowflake, BigQuery, and Redshift for data; and Airflow, Dagster, and Prefect for orchestration. Supporting tools include Terraform, Amplitude, Segment, and Looker for analytics.

What is Able working on?

Active projects include Kafka-based data ingestion, graph ETL pipeline development, PDF processing, visual data extraction, prompt engineering for financial charts, and AI strategy implementation for partner organizations.

How this profile is built

Able'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.