Ilyas Kurklu, Co-founder and CEO of Replenit talks about the benefits of AI powered decision intelligence and why modern retailers should capitalize on it in this SalesTechStar interview:
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Hi Ilyas, tell us about Replenit, your recent funding, and what inspired the platform?
Replenit is an AI decision engine for retailers that turns customer signals into real-time, individualized decisions. It acts as an intelligence layer on top of existing systems, helping brands understand what each customer needs and what to do next. The company was inspired by a clear gap we observed while working with hundreds of retailers. Most had strong data infrastructure and execution tools, but very little true intelligence guiding decisions. Retailers could send campaigns and collect data, but they struggled to determine the right action for each individual customer. We recently raised $2.5 million in a pre-seed round led by Movens Capital and Vastpoint, with participation from several global investors. We’ll use the funds to deepen product development, expand AI research, grow our engineering teams in Poland and the Netherlands, and support our US market entry.
For the uninitiated, how does an AI-powered decision engine enable better revenue processes?
Our AI-powered decision engine improves revenue by helping retailers make better decisions for each customer at the right moment. Instead of relying on campaigns, segments, or fixed rules, Replenit looks at customer, product, and transaction data together to understand each customer-product relationship. It then determines what action is most relevant, such as when someone is likely to purchase again, what product they may need, or when they might be at risk of churn. These decisions are then activated through the retailer’s existing tools. The result is more relevant interactions, stronger retention, and higher repeat purchase rates, which directly translate into revenue growth. L’Occitane en Provence saw a 235% increase in post-purchase revenue after implementing Replenit’s decision engine.
What retail tech trends are redefining how retailers interact with and engage buyers today? How is AI playing a significant role here? Can you share some thoughts and perspectives based on what global brands are doing?
A number of shifts are happening simultaneously. Retail is moving from execution to intelligence, while AI is playing a more central role in how customers interact with products. Decision-making has replaced execution as retailers’ most pressing challenge, while AI search and assistants increasingly drive product discovery on the customer side. Facing less control over the top of the funnel, retailers are relying ever more heavily on strong customer relationships to make better decisions across owned channels. AI moves these retailers beyond static rules and historical analysis toward more contextual and individualized decision-making.
How will new retail tech and sales technology that are impacting the market change the entire scope of retail in the future?
Now that retail is evolving into a more intelligence-led model, companies are coming to depend on systems that can continuously understand customers and make decisions in real time. The old methods that relied so heavily on rules, segments and manual workflow are declining as agentic commerce ascends. Agentic commerce, in which AI plays an essential role not just in product recommendation but in influencing and shaping decisions, isn’t widespread yet, but it’s on an undeniable upward trajectory.
Over time, this is likely to change how retailers operate across the entire customer journey. Success in retail will come to depend less on bigger toolboxes than on having finely-tuned systems that can understand context, interpret behaviour, and act in a more adaptive and individualized way.
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As revtech / salestech / retail tech slowly influences how customers interact with brands and businesses, what are some of the top of mind tips and best practices that you feel retailers should abide by during this evolutionary phase?
The most important thing retailers should keep in mind is that adding more technology does not automatically create better customer experiences. In many cases, it simply creates more noise. The real challenge today is not whether a brand has data or channels, but whether it can make better decisions with them. One best practice is to stop thinking in terms of campaigns first and start thinking in terms of customer context. Retailers should ask what a specific behaviour means, what the customer is trying to achieve, and whether any action is necessary at all. Not every signal should trigger a message.
Another is to avoid treating AI as a cosmetic layer on top of legacy workflows. Many companies are still applying AI to static rules, fixed segments, and rigid calendars. That may improve efficiency, but it does not fundamentally improve decision quality. Retailers should instead focus on systems that can adapt at the individual level and respond to real-time context.
It is also important to work with the existing stack rather than rushing into expensive replacement projects. In most cases, the issue is not a lack of channels or infrastructure, but the absence of intelligence to decide what should happen next. The most effective transformation often comes from adding a strong decision layer on top of what is already in place.
Finally, retailers should measure success beyond engagement metrics. Opens, clicks, and send volumes are no longer enough. The more meaningful question is whether each interaction is driving better customer outcomes and more profitable growth. In the years ahead, the winners will be the brands that send fewer, smarter, and more relevant actions, rather than simply increasing the volume of communication.
Five wrap up thoughts you’d leave our readers with?
First, most retailers do not have a data problem. They have a decision problem. The industry has spent years investing in collecting signals and building execution capacity, yet many brands still rely on rules, segments, and calendars to decide what to do next. That is the real bottleneck.
Second, much of what is marketed as personalization still is not true personalization. Changing creative, inserting names, or recommending adjacent products is not the same as understanding what an individual customer actually needs in context. Retail has used the word heavily, but has rarely delivered on its true meaning.
Third, a lot of so-called AI in retail is just legacy logic with a new label. If the system still depends on static workflows, predefined scenarios, and human-maintained rules, then the intelligence has not really changed. Retailers should be careful not to confuse automation dressed up as AI with genuine decisioning capability.
Fourth, the competitive edge in the next era of retail will not come from who can send more messages or launch more campaigns. It will come from who can make better decisions per customer, per moment, at scale. That is a very different operating model, and not every existing platform is built for it.
Fifth, we believe retail is entering a structural shift. The last generation of software helped brands collect data and execute at scale. The next generation will decide. Companies that recognize this early will build stronger customer relationships and more efficient growth. Those that do not may find themselves running increasingly sophisticated systems with increasingly outdated logic.
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About Replenit
Replenit is an AI-powered “decision and action engine” designed specifically for retail, focusing on enhancing customer retention and automating replenishment strategies
About Ilyas Kurklu
Ilyas Kurklu, is Co-founder and CEO of Replenit
