Benzinga, a provider of real-time financial news, market data, and financial APIs, has announced an expanded relationship with WNSTN, an AI personalization and engagement platform built for financial institutions. Under the expanded partnership, the two companies will go to market together with a combined solution for brokerages: WNSTN will supply the AI intelligence layer, while Benzinga provides the real-time and historical market data that grounds it.

The expanded offering builds on a year of collaboration within Benzinga's own products. Since rolling out across Benzinga Pro, the company's platform for active traders, in 2025, Benzinga AI — powered by WNSTN — has delivered natural language chat, news summarization, and market analysis to Benzinga's base of active traders and financial professionals. The product has generated strong user adoption, including a 25x increase in engagement, giving both companies the confidence to take the technology beyond Benzinga and make it available to a broader set of financial platforms.

For brokerages, the joint offering is designed to shorten the distance between wanting AI in the client experience and being able to defend it internally. WNSTN contributes multiple touchpoints that sit on top of data, including a conversational layer, portfolio-aware personalization, always-on market monitoring, actionable investing ideas, and tailored summaries. The platform also includes a compliance agent trained on global regulatory frameworks — including FINRA and the SEC, which oversee U.S. broker-dealers, CySEC in Cyprus, and MiFID II across the European Union — and every output is routed through that compliance layer before it reaches an end user. Benzinga, in turn, supplies the underlying market intelligence: real-time equities news, analyst ratings, earnings and corporate events, SEC filings, and 'Why Is It Moving' explanations of price action, alongside historical archives for retrieval and context.

Data quality has become a practical constraint on financial AI. Models can produce confident answers regardless of whether the information beneath them is current, correctly attributed, or tagged to the right security, and in a brokerage environment those failures carry regulatory weight. Structured, timestamped, entity-tagged market data reduces that exposure at the retrieval layer, before any guardrail is asked to catch it.

“Most of the failure modes in financial AI trace back to the retrieval layer rather than the model itself,” said Dave Maher, CEO of Benzinga. “If the data arrives late, or without clean timestamps and entity tagging, the output is confidently wrong, and in this industry that gets expensive. Running Benzinga AI on our own feeds inside Benzinga Pro for the past year gave us a live test of that, and answer quality tracked directly to the structure of the data underneath it. WNSTN built the orchestration and compliance layer that makes that structure useful inside a brokerage, so bringing the two to market together was the logical next step.”

The announcement also reflects a broader shift in how Benzinga licenses its data. Over the past year, the company has expanded its API infrastructure to serve large language model and retrieval-augmented generation workloads — an approach in which a model retrieves source material from an indexed corpus at the time of each query — and has established licensing frameworks designed specifically for AI use cases rather than treating them as exceptions to legacy redistribution terms. Benzinga now licenses content and data to more than 400 clients across 30 countries, a growing share of which are building AI-native products.

“Brokers choose WNSTN because we turn complex market data into something personally relevant, understandable and actionable for their clients,” said Rakover, Co-Founder and CRO of WNSTN. “WNSTN doesn't just summarize information, it digests it, puts it in context, and tailors how it's delivered to each user. It's about giving people the right information, at the right time, in the way they can understand it best. That's how we turn data into action within the broker's platform, with compliance built into the experience. Benzinga's real-time and historical coverage strengthens that capability, while its use of WNSTN in Benzinga Pro demonstrates our technology in production with a demanding audience of active traders.”

The impact, according to the companies, is measurable. WNSTN's analysis of one year of data with a U.S. broker-dealer client found that investors who engaged with its AI executed 2.3 times more trades than those who did not. Roughly half of users who interacted with WNSTN chat placed a trade within 24 hours of interacting with the platform.

The combined offering is available to brokerages and financial platforms today, with white-label deployment and API integration options. White-label deployment lets the AI features appear inside a brokerage's own client experience under that firm's brand, while API integration allows platforms to embed the underlying data and AI capabilities directly into existing tools.