i-Genie.ai‘s acceptance into the invite-only Microsoft for Startups Pegasus Program helps high-potential B2B startups scale rapidly by providing exclusive access to Microsoft’s technology, mentorship and global enterprise ecosystem. This collaboration also provides i-Genie.ai with access to powerful AI tools, Microsoft Azure credit and a global customer network to help scale faster and smarter.
Trevor Sumner, CEO of i-Genie.ai, explained the benefits of the program and hope they plan to take advantage going forward.
Question: i-Genie.ai was selected as one of only 150 companies from tens of thousands of startups for Microsoft’s invite-only Pegasus Program. How is access to Azure’s advanced AI capabilities and enterprise-grade infrastructure specifically accelerating your ability to scale insights delivery for multinational brands, and what technical or go-to-market advantages does this partnership provide that weren’t available before?
Sumner: By being elevated to a high-profile partner for Microsoft, we have access to dedicated resources to help us proliferate throughout the Microsoft ecosystem. We have a dedicated success manager who has been amazing, helping us get listed across the Azure Marketplace, become eligible for co-selling, and even have i-Genie.ai license being applied to a customers minimum Azure spend commitments, which eases budget allocaiton and spend at clients. We also are being promoted by Microsoft at conferences like National Retail Federation and Mobile World Congress.
And we get early access to upcoming products and updates and access to architectural resources. That said, I think the most valuable benefit will be the ways in which we cosell with them as a channel. Their account executives have relationships with the vast majority of our large enterprise target partners and have been wonderfully receptive to the way we leverage AI and large scale data to disrupt traditionally survey-based consumer insights.
Q: With native NLP across 20+ languages and insights delivery in 30+ countries, how does i-Genie.ai’s AI ensure contextual accuracy and cultural nuance across diverse markets, particularly when analyzing billions of digital signals from searches, social conversations, reviews, and videos simultaneously?
Sumner: Great question. Some people will take a language like Arabic, translate it to English, run their algorithms and then translate it back. This is a fools errand, but let’s people claim language support. One struggle with AI is that it can confidently output faulty conclusions that lack nuance and understanding.
i-Genie has native libraries for language understanding that we finely tune before we say we can support it. We also have robust Master Data Management around products, ingredients, formats, benefits and more to deeply understand the context. If I say “Axe body spray is sick,” that’s good. If I I say “this yogurt made me sick,” that’s very, very bad. Context matters and the i-Genie platform we have built provides the highest levels of context and understanding so clients can confidently make multi-million dollar decisions faster.
Q: How does i-Genie.ai’s passive signal processing approach overcome the self-reporting biases and time delays inherent in surveys, and what specific use cases demonstrate the 80% faster insights claim for brand equity tracking or trend spotting?
Sumner: Henry Ford famously said that if he asked customers what they want, they would say a faster horse. Surveys are such a flawed data source, in their self-reporting biases when so much of our desires are subconscious. Or look at the pure limitations of the format. You can ask about maybe 5-10 brands on a survey before the respondent gets “fatigued.”
There are more than 1,900 hair care brands and according to Ipsos, 70% of new market value is created by disruptor brands. Can a survey give you a real sense of a market? And they take a lot of time to design them to minimize bias, and analyze them. A lot of companies are using AI to try and speed that process up, but if you have garbage in, you get garbage out, but you get it faster and at scale. A very dangerous combination.
Faster needs to be coupled with reliability and better data sources, whether you are looking at macro trends and direct competitors or looking at sub-segments and undiscovered opportunities in the market. Typical new product innovation cycles take 12-18 months, with another 18 months to production. That means brands are launching products based on trends discovered 3 years ago. You can’t compete at internet speed.
We reduce innovation cycles to just 2-3 months, and in a head to head challenge from a large cleaning products company, our top 10 ideas outperformed the 20 ones they had been incubating, with 9 of 10 top ideas i-Genie.ai identified ones according to consumers.
For brand equity tracking, surveys are usually done quarterly or annually. It’s a post-mortem that often isn’t descriptive or actionable, where our Brand Pulse is refreshed monthly for most clients, giving not only near real-time feedback to marketing campaigns in motion, but also providing in depth, granular analysis of what is happening and prescriptive actions on what to do next.
Speed determines competitiveness in many of these markets. Why would you rely on slow data originating from 19th century door-to-door interviews when you can harness digital signals at scale? Your marketing is now mostly digital. Your data should be, too.
Q: The article highlights capabilities to “track brand equity, spot emerging trends, benchmark product experiences, and test innovations” in real-time. How does i-Genie.ai’s AI synthesize unstructured external data sources into actionable brand equity drivers and trend forecasts 4-6 months earlier than traditional methods, and what role does agentic AI play in this process?
Sumner: Part of our secret sauce is how we ingest data, including some proprietary AI methods for analyzing how consumers search, which is a high integrity consumer intent signal.
The next level is using AI for natural language processing and understanding (NLP/NLU). We use finely-tuned large language models (LLMs) and NLP libraries to understand what is being said, sentiment and intention across text, video, audio and reviews. We leverage our Master Data Management, which deeply understands every product across key dimensions, so that for example a social post about a product is actually a signal across a universe of brand, ingredients, formats, benefits and more.
The next level is pulling all these signals together with AI so we can tell you what consumers think about every brand, their product superiority, price value, trust, affinity, loyalty and intent, as well as their overall buzz across search, social and reviews.
And the last level is proprietary AI models that translate these into scores that highly correlate with market share performance and diagnose what areas need improvement for you to move the needle.
We expose all of this in dashboards as well as an agentic AI layer we call Presto, which allows you to ask any question and get answers rooted in observable data and proven predictive algorithms. You can literally ask about any product or brand, any attribute and go from high level SWOT analyses to brand and CX scorecards to the exact verbatims of what people are saying on every channel.
Q: In 2026, how should brands integrate consumer insights from your platform with their ERP transactional data to create a unified intelligence layer that connects customer sentiment, product innovation, and operational planning?
Sumner: In 2026, leading brands won’t treat consumer insights and ERP data as separate systems but connect What consumers feel → What they buy → What you make → What you plan. They’ll integrate i-Genie.ai’s real-time sentiment, trend, and attribute signals directly into their ERP and planning environments to create a unified intelligence layer. By mapping consumer themes to SKU, margin, and inventory data, brands can connect shifts in sentiment to velocity, returns, and supply constraints.
This enables trigger-based decisioning, which includes adjusting pricing, reformulating products, reallocating inventory or accelerating innovation based on live consumer signals. The result is a closed-loop system where customer voice directly informs demand planning, product development, and operational execution in near real time.
Q: How does i-Genie.ai’s platform architecture enable seamless integration with existing enterprise systems, and what advantages does an AI-native approach provide for organizations trying to connect consumer insights with demand sensing, innovation pipelines and product development workflows?
Sumner: We are built on an Azure stack, and our data is easily exported or connected to third party systems. Our agentic layer Presto is build on the industry standard MCP architecture to enable connection to other agentic systems and applications.
Presto is in its own way an organizational integration infrastructure, because it provides an intelligence layer that can be exposed to any one. You don’t have to wade through dense infomatic dashboards to get answers and we are seeing clients expose i-Genie.ai intelligence to executives and departmental leaders who for the first time can get meaningful answers to granular or complex questions in real-time, even without the help of an analyst.
Language is the ultimate human API. It’s how we communicate and how we think. Presto is native human OS API, amplifying and augmenting human intelligence across the organization with the power of everything consumers are saying in your industry and all your competitors. All in your pocket and always on. It’s incredible.
Q: With clients adopting the platform, what specific metrics or success stories demonstrate the “measurable outcomes” and “tangible business value” that i-Genie.ai delivers—such as faster product launches, improved innovation success rates, or enhanced competitive positioning based on early trend identification?
Sumner: There are so many success stories to tout. We helped a large skincare brand catch the collagen trend and launch a $70m product before their competitors. We helped a hair care company understand a key hole in the sub-segment of the haircare market, heat protectorant, where customers were complaining you can only put it in wet hair. They pounced and launched a heat protectorant that you could put in dry hair that was an immediate Amazon best-seller and the best product launch in their history.
There are so many success stories and we are proud to be the secret weapon for large brands like Kenvue, Unilever, Bayer, Coca-Cola, Clorox, and Danone to compete at digital speed and win their markets.
What This Means for ERP Insiders
Consumer intelligence integration is becoming architectural, not peripheral. The convergence of real-time sentiment analysis with ERP transactional systems signals a fundamental shift in how demand planning, product development, and operational execution are orchestrated. For ERP vendors and GSIs, this creates pressure to architect native consumer signal ingestion layers that connect unstructured external data directly to demand sensing, SKU planning and innovation pipelines.
Agentic AI emerges as the connective tissue between specialized SaaS and core ERP. Microsoft’s Pegasus Program validation of i-Genie.ai underscores the strategic role of AI-native, specialized solutions operating alongside monolithic ERP systems. This best-of-breed model, powered by agentic architectures like Model Context Protocol (MCP), enables ERP ecosystems to absorb domain-specific innovation without bloating core platforms. For transformation leaders and enterprise architects, this validates investment in integration frameworks and agentic middleware that orchestrate autonomous agents across finance, operations and customer intelligence domains.
Speed-to-insight becomes the primary competitive differentiator. The documented compression of innovation cycles using passive digital signal processing exposes critical vulnerabilities in traditional survey-based planning workflows. ERP vendors must evaluate whether their planning, PLM, and forecasting modules can ingest and operationalize predictive consumer signals refreshed monthly or weekly rather than quarterly.
