Something shifted this week in how AI agents get built for financial products. eToro's Tori, the investing AI agent serving more than 40 million users across 75 countries, just got wired up to xAI's models with direct access to real-time signals from X. The result: an agent that doesn't just answer questions about markets, it reads how market mood is shifting right now and folds that into the investing workflow.
That's a meaningful step up from what most AI product demos show. The typical pattern is: grab a large language model, wrap a chat interface around it, call it an AI assistant. What Tori's doing is different. It's pulling live signals, processing them with a capable model, and surfacing actionable context at the moment a user is making a decision. That's what proper product integration looks like.
Why real-time changes everything
AI models have a knowledge cutoff. Ask a model what happened in markets this morning and it'll either hallucinate something or admit it doesn't know. That's fine for plenty of use cases. For investing, it's a fundamental limitation.
Plugging a live data feed into the agent loop changes the character of what the product can do. You're no longer querying static training data. You're giving the model a window onto right now. In eToro's case, that window is X, where a lot of market-moving conversation happens first (for better or worse). The model can then reason over fresh signals rather than pattern-matching against historical text.
The same architecture pattern works wherever your product needs to reason over information that keeps changing. News dashboards, competitive intelligence tools, customer-facing support agents that should know about this morning's outage, logistics tools that need to account for today's delays. The feed changes, the model stays the same, and what the product can do changes dramatically.
What xAI is making available
xAI's API console is now the bridge that made this possible for eToro. The same integration is available to any team. Whether you want to build a research copilot, a sentiment dashboard, or a daily briefing assistant, live context from X is accessible through the API in the same way you'd call any other model endpoint. The barrier here is mostly architectural rather than technical, and that's actually the interesting bit.
Most teams that come to us aren't blocked by cost or model quality. They're blocked by not knowing how to connect a live data source to a model in a way that's reliable, fast, and actually useful in a real product. That's an engineering problem and it's a solvable one.
Where Dakik fits
We build AI agents and RAG pipelines for product teams who want something that ships, not a proof-of-concept that looks good in a slide deck.
If your product needs to track live data rather than just query static documents, we can put that together. Pick your data source (social feeds, market data APIs, your own events stream, news aggregators), we build the ingestion and retrieval layer, connect it to the right model, and wire the whole thing into your product. We've delivered this kind of architecture using Qdrant for vector search, and the same principles apply whether you're building in fintech, media, e-commerce, or anywhere else. We looked at a related pattern recently in our write-up on Gopuff's proactive AI agent, which shows how real-time awareness plays out on the e-commerce side.
The Tori integration is a useful proof point to take to your stakeholders. Real-time AI that reads live signals and acts on them isn't a research demo anymore. It's in production, at scale, for 40 million users. If your product should be doing something similar, let's have a conversation.