Stravito, the Insights Intelligence Platform trusted by global brands like Lavazza, Heineken, and Nestlé, has announced a major upgrade to its AI Assistant: Deep Research Agent.
For food and beverage brands, the pressure to move faster is intensifying. Prolonged inflation is reshaping consumer behaviour, private label competition is eroding loyalty and shifting health and wellness trends are shortening innovation cycles. Product, category, and insights teams are being asked to defend pricing, reformulation, and portfolio decisions with greater precision and speed.
Deep Research Agent is designed for that reality. Rather than generating surface-level summaries, it autonomously plans multi-step research workflows, reads full reports in parallel (including charts and tables), and cross-checks findings before delivering citation-backed answers grounded exclusively in a company’s own research.
The platform compresses the time between question and boardroom-ready recommendations, enabling F&B teams to move from “what does the research say?” to “what should we do next?” with documented proof. Founder & CEO Thor Olof Philogène discusses what this means specifically for food and beverage teams navigating shorter innovation cycles, tighter margins and rising complexity.
Food and beverage brands are under increasing commercial pressure. At the same time, many are sitting on years of consumer, market and category research. How are leading organisations closing the gap between the intelligence they already own and the decisions that depend on it?
The challenge most food and beverage organisations face is not a shortage of research. It is that the research they already have risks not reaching decisions before they are made.
Often years of consumer studies, category analysis, and market intelligence sits across teams, systems, and markets. When a major innovation or expansion decision is being made, that knowledge is either too hard to find or arrives too late to change anything.
The organisations closing this gap are doing one thing differently. Instead of instantly commissioning new research or relying on remembering where a study lives, they are using AI purpose-built to work with their own existing knowledge, finding it, synthesising it, and surfacing answers they can apply when the decision is actually being made.
What role can AI play in helping Food & Beverage organisations make the most of the research they already have?
What changes with the right AI is not the volume of research available. It is the speed at which the right intelligence reaches the right person. A category manager preparing for a pricing discussion, an innovation lead stress-testing a concept, both can surface what the organisation already knows in minutes rather than days.
Decisions that would previously risk moving forward on incomplete information are instead made on evidence. Research that would have sat unused starts shaping outcomes.
How can AI help organisations pressure-test decisions before investment is locked in?
AI allows teams to introduce consumer insight much earlier in the decision cycle. Traditionally, early-stage ideas are shaped through internal discussions and intuition, with formal testing occurring only after concept development is already in-motion.
Tools like Stravito AI Personas help shift that dynamic by turning static segmentation studies into interactive consumer profiles grounded in a company’s own research. Teams can use these personas to pressure-test packaging, campaign concepts, or product ideas before major investments are committed.
For example, Lavazza Group integrated Stravito AI Personas into its marketing and innovation process, building consumer personas from thousands of interviews. The tool has already been used to refine packaging and campaign decisions.
What once required weeks of stop-and-go validation can now begin with focused working sessions where teams quickly test and iterate ideas while staying anchored in existing research. The goal is to reduce risk earlier, strengthen concepts before formal testing, and ensure investment is directed toward the strongest ideas.
For strategic business decisions, speed alone is not enough. Decision-makers need to know where an answer comes from and be able to stand behind it. How does AI deliver that kind of confidence?
Speed matters, but it is not what makes a decision defensible. When a commercial director is deciding whether to enter a new market, or a CMO is committing a significant media budget, the question is not just what the answer is. It is where it came from and whether it can be trusted.
This is where generic AI tools fall short in a business context. An answer drawn from internet data has no grounding in the company’s own consumer research, category understanding, or market history. It might be fast but it is unlikely to be something a senior leader can stand behind.
What changes when AI works entirely on a company’s own research is that every answer comes with a source. The study it came from, the market it covered, the date it was conducted. Decision-makers can follow the reasoning, challenge the conclusion, and ultimately own the decision because they can see exactly what it is built on.
As AI tools become part of everyday workflows across the enterprise, what practical steps can food and beverage organisations take to ensure teams are using them effectively and where should they start?
Start with a clear expectation from leadership: major decisions must be backed up with intelligence the business already owns. When that becomes the standard, it stops being optional.
Then identify internal champions. When they are visibly drawing on evidence to shape decisions, adoption spreads fast.
Finally, pair access with ongoing enablement. Teams need guidance on how to ask the right questions and what to do with the answers. That is what turns a platform people have been given into a platform people actually use.
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