Meta Acquires AI Analytics Startup Sundial to Strengthen Its AI Strategy

Meta has acquired Sundial, an AI-powered data analytics startup, in a move designed to strengthen its internal analytics capabilities as demand for artificial intelligence continues to grow. The acquisition was confirmed by a Meta spokesperson to Axios on October 23, 2026. Sundial develops technology that allows businesses to analyse complex datasets by asking questions in ordinary language, helping teams obtain insights without relying entirely on specialised data analysts. Financial terms of the acquisition have not been disclosed. The deal reflects Meta’s increasing interest in using AI not only to develop products for billions of users but also to improve how the company analyses information and makes decisions internally.

Sundial was founded in 2021 by Julie Zhuo and Chandra Narayanan, both of whom previously worked at Meta. Zhuo formerly served as Meta’s vice president of product design, while Narayanan held analytics leadership positions at Facebook and Instagram before working at investment firm Sequoia Capital. Following the acquisition, both founders and other members of the Sundial team are expected to join Meta. Narayanan will lead work involving data analytics tools and AI agents within the company’s new analytics organisation. The acquisition therefore brings experienced personnel and an existing technology platform into Meta, potentially allowing the company to advance its analytics ambitions more quickly than if it developed every capability independently.

Sundial’s technology focuses on making data analysis more accessible across organisations. Its platform translates natural-language questions and complicated datasets into actionable insights through a chatbot-based interface. This allows employees to investigate business performance, identify trends and answer questions without waiting for a central analytics department to process every request. Meta intends to integrate the startup into its Data Analytics Tooling & Agents unit, known as DATA, which is led by Alex Schultz, the company’s first chief data officer. The unit is responsible for analysing business performance, running experiments to improve products and monitoring important AI systems. By incorporating Sundial’s technology, Meta hopes to strengthen the tools available to employees and make data-driven decisions more efficient.

The acquisition also highlights Meta’s ambition to use AI agents for more advanced business operations. According to Schultz, AI is creating enormous demand for insights, encouraging companies to rethink traditional approaches to analytics. Meta’s broader objective includes using AI to identify questions worth investigating, propose experiments, evaluate results and monitor systems for significant changes. In principle, these capabilities could help teams identify problems more quickly and make better-informed decisions. However, allowing AI systems to influence experiments and business decisions also requires appropriate safeguards, reliable data and human oversight. Meta has not disclosed precisely how Sundial’s technology will be integrated into its existing tools or how much responsibility future AI agents will have for making operational decisions.

The deal has wider implications for the AI analytics industry. Sundial had raised approximately $23 million before the acquisition, including a $16 million Series A funding round in July 2025 led by GPV, with Sequoia Capital among its backers. Its customer base reportedly included OpenAI, a major competitor to Meta in the AI market. Sundial is expected to stop serving external customers once the transaction closes, meaning businesses that relied on its services may need to consider alternative solutions. For Meta, the acquisition provides an opportunity to bring analytics expertise and technology in-house as competition intensifies among technology companies investing heavily in AI. Although the financial terms remain undisclosed, the transaction demonstrates that the AI race increasingly involves acquiring specialised teams and tools capable of delivering practical business advantages, rather than focusing exclusively on developing larger AI models.