Turing company profile: valuation, funding & revenue

This Turing company profile covers the company's valuation, revenue, funding rounds, investors and history. Turing works in artificial intelligence, with a focus on AI training data and engineering services. It was founded in 2018 and is headquartered in Palo Alto, California, United States. Its latest valuation on record is US$2.2 billion (March 2025). Every figure links to a public source, and anything the company hasn't disclosed is marked as such.

Revenue

About US$300 million — Annualised revenue run rate (reported), as of March 2025. Source: TechCrunch.

Funding history

Turing timeline

Key dates from the sources on this page, oldest first. Metric snapshots are listed after the funding events.

• 2018 — Turing is founded in Palo Alto by Jonathan Siddharth (CEO) and Vijay Krishnan.
• Mar 2025 — Series E: US$111M, valuing the company at US$2.2B. Investors: Khazanah Nasional (lead) and others.
• March 2025 — Run rate when the round was priced: About US$167 million.

Investors

Overview

Turing provides AI labs and enterprises with the human expertise and data needed to train and improve large language models, along with engineering services to build AI systems. Its network of software engineers, scientists and domain experts writes code, solves reasoning problems, evaluates model outputs and creates training data, especially for coding and complex reasoning, which has become crucial as AI labs compete to build better coding and agentic models. TechCrunch described it as a key coding data provider for OpenAI and other model developers. In March 2025 it raised a US$111 million Series E at a US$2.2 billion valuation, and reported annualised revenue of about US$300 million.

Jonathan Siddharth, the CEO, and Vijay Krishnan founded Turing in 2018. Both previously founded Rover, a content recommendation company acquired by Revcontent. Turing began as a platform for vetting and hiring remote software engineers from around the world, using AI to assess skills. That global network of engineers became the foundation for its AI data business when demand for expert training data surged after the launch of ChatGPT.

Products and services

• AI training data: code, reasoning problems, evaluations and other data created by experts for model training and fine-tuning.
• Model evaluation: human evaluation of model outputs for accuracy, safety and quality.
• Agent and environment building: creating tasks and environments that test and train AI agents.
• Enterprise AI services: engineering teams that help companies build and deploy AI applications.
• Talent platform: its original service matching companies with vetted remote engineers.

Funding

Turing raised earlier rounds from investors including Foundation Capital, WestBridge Capital and AltaIR Capital. In March 2025 it announced US$111 million in committed capital for its Series E at a US$2.2 billion valuation, led by Malaysia's sovereign wealth fund Khazanah Nasional, bringing total funding to about US$225 million since its founding. TechCrunch reported that the round was priced when Turing's annualised revenue run rate was about US$167 million, and that it had since grown to around US$300 million.

Business model

Turing earns revenue mainly from AI labs and enterprises paying for expert-generated data and engineering work, typically priced by project or by the time of experts involved. Its margins depend on the difference between what it charges and what it pays its expert network, as well as the efficiency of its platform for managing and checking work. A few large AI lab customers can account for a significant share of revenue.

Competition

Turing competes with Scale AI, Surge AI, Mercor, Invisible Technologies, Handshake, Snorkel AI and others in AI data, and with IT services firms in enterprise AI work. Its strengths are a large network of engineers and a focus on coding and reasoning data.

Turing at a glance

Turing is a Palo Alto AI training data and engineering services company founded in 2018 by Jonathan Siddharth and Vijay Krishnan. It provides expert-created data for AI labs, especially for coding and reasoning, and builds AI systems for enterprises. It raised a US$111 million Series E at a US$2.2 billion valuation in March 2025, led by Khazanah Nasional, with a reported revenue run rate of about US$300 million.

Timeline

• 2018: founded as a remote engineering talent platform.
• 2021: becomes a unicorn as remote hiring grows.
• 2023: shifts focus to AI training data and services.
• March 2025: raises US$111 million at US$2.2 billion.

Why AI labs pay for expert data

Early large language models learned mostly from text on the internet. To become good at coding, mathematics and complex reasoning, and to act reliably as agents, models increasingly need high-quality examples and feedback from experts: worked solutions, step-by-step reasoning, careful evaluations of model answers, and tasks that test skills. Such data doesn't exist in sufficient quantity online, and creating it requires skilled people. AI labs spend heavily on it, creating a fast-growing market for companies that can recruit, manage and quality-check expert contributors at scale.

The neutrality question

After Meta invested heavily in Scale AI in 2025, some AI labs reportedly reduced their work with Scale, concerned about sharing information with a company partly owned by a competitor. That shift benefited independent data providers such as Turing, Surge and Mercor. Neutrality, confidentiality and quality controls have become important selling points in the AI data market.

Who uses Turing

Turing's customers include frontier AI labs, large technology companies and enterprises building AI applications.

Valuation context

A US$2.2 billion valuation on a reported US$300 million run rate implies a modest revenue multiple compared with AI software companies, reflecting the services-heavy nature of data work. Our guide to AI training data companies compares Turing with peers.

Risks

• Customer concentration: a few AI labs may account for much of revenue.
• Synthetic data: AI-generated training data could reduce demand for human data.
• Competition: many well-funded firms compete for the same contracts.
• Labour issues: managing large contractor networks raises quality and labour questions.

Questions for buyers

• How are experts vetted and their work quality-checked?
• How is confidential model information protected?
• How quickly can teams scale for a project?

How Turing's expert network works

Turing's original business vetted software engineers worldwide using automated tests and interviews, building a large pool of developers with verified skills. For AI work, it draws on that pool and recruits additional experts in fields such as mathematics and science. Engineers might write coding problems with tests, solve tasks step by step, review model-generated code for bugs, or build environments where AI agents attempt realistic software tasks. Quality is checked through reviews and automated tests. Because coding tasks can often be verified automatically, they have become especially valuable for training models to reason and act reliably, and demand for high-quality coding data grew rapidly as AI coding tools took off.

Who funds Turing

Turing's Series E was led by Khazanah Nasional, Malaysia's sovereign wealth fund, and was oversubscribed, according to the company.

For investors, the key question is whether Turing can maintain growth as AI labs diversify suppliers and increasingly use synthetic data alongside expert-created data.

Latest developments (October 2026)

Turing has rebranded itself around frontier AI. In September 2026 it named Ece Kamar as chief technology officer, and chief executive Jonathan Siddharth has argued that training and evaluating AI models will become one of the most common jobs, as reported by Sources. The company supplies coding and reasoning data and reinforcement-learning environments to AI labs. Its last valuation remains US$2.2 billion from March 2025. See our AI training data guide.

What to watch

Watch new funding, revenue growth, AI lab relationships and competition from larger data providers.

Takeaways

AI labs rely on expert data to improve coding and reasoning. Founders can see how an existing talent network can pivot to a booming market. Investors should watch customer concentration.

Sources

Every figure on this page comes from the public sources below. Where sources disagreed, we used the company's own announcement or a primary news report. Private-company data changes; check the date beside each figure.

1. TechCrunch
2. Turing blog (company announcement)