We Don’t Just Build AI, We Deliver Measurable Impact

Join us for an exclusive interview with Hocine Ousmer, who shares how the organizations transform AI from a concept into a concrete force for efficiency, transparency, and growth across industries. Discover what “real intelligence drives real results” truly means in practice.

hocine ousmer We Don’t Just Build AI, We Deliver Measurable Impact

Could you describe Datategy and what it does for organizations looking to operationalize artificial intelligence?

Datategy is a company born out of a simple but powerful belief: AI should create measurable value for businesses, not just complex models and prototypes. We help organizations move from experimentation to execution by making artificial intelligence practical, explainable, and impactful at scale. Our mission is to empower every enterprise, regardless of its technical maturity, to transform data into actionable insights that drive strategic outcomes, enhancing performance, efficiency, and informed decision-making.

At the heart of our ecosystem is papAI, our end-to-end AI platform designed to simplify the entire lifecycle of AI projects. From data preparation to model deployment, papAI enables teams to collaborate, monitor, and manage AI solutions with complete transparency. It bridges the gap between technical experts and business leaders, ensuring that AI development always remains aligned with operational goals

What inspired your vision of focusing not only on AI development but on delivering measurable business impact

The inspiration behind my vision comes from years of seeing the same pattern across industries: companies pouring resources into AI while struggling to turn prototypes into real, operational value. I’ve always believed that the true definition of AI success must shift from technical brilliance to measurable business outcomes. For me, real innovation isn’t about building another model; it’s about reshaping how organizations operate, make decisions, and create long-term impact.

From my personal standpoint, AI should be accessible, responsible, and anchored in tangible results. That means rethinking how we approach data science: not as a set of isolated experiments, but as a structured process tied to ROI, adoption, and operational execution. My conviction has always been that AI must evolve from a laboratory concept into a strategic growth engine for every enterprise ready to embrace it.

The quote says “real intelligence drives real results.” — how does Datategy ensure that its AI solutions translate into tangible outcomes for clients across industries?

For us, “real intelligence” starts with understanding the client’s context. Before any line of code is written, our teams work closely with business stakeholders to identify the specific problems that matter most: cost reduction, process optimization, risk mitigation, or customer satisfaction. This ensures that every AI initiative begins with a clear definition of success and measurable performance indicators.

Datategy’s platform, papAI, plays a central role in turning intelligence into results. It integrates the entire AI lifecycle — from data preparation to deployment — in a way that makes experimentation fast, governance transparent, and monitoring continuous. This allows organizations to measure impact in real time, track ROI, and fine-tune models as business conditions evolve. The goal is not to build “AI for AI’s sake,” but to create solutions that grow with the organization’s needs.

Moreover, we place a strong emphasis on feedback loops. Once an AI model is deployed, it doesn’t stay static; it learns and adapts as new data comes in. This continuous improvement cycle ensures that the solutions we deliver remain relevant and impactful long after the initial deployment, proving that “real intelligence” is not just technical.

Datategy’s vision is centered on empowering organizations to leverage AI as a transparent and insightful tool, fostering enhanced business performance.

How does your platform, papAI, help bridge the gap between data science experimentation and real-world business deployment?

papAI was designed to tackle one of the biggest bottlenecks in AI adoption: the gap between experimentation in the lab and sustainable deployment in production. Too often, data scientists create impressive models that never make it into operational systems because of scalability, compliance, or integration issues. papAI removes these barriers by providing a unified environment where data scientists, engineers, and business experts can collaborate seamlessly.

The platform enables users to automate the transition from model training to deployment, with built-in governance, explainability, and version control. That means organizations can deploy AI models faster, monitor their behavior in production, and adapt them to evolving business realities, all without losing transparency or trust. In short, papAI transforms what used to be a fragmented, manual process into an integrated workflow from end to end.

What makes papAI stand out is its accessibility. It’s designed not just for AI experts but also for decision-makers who need clear insights into model performance and business impact. By combining usability, traceability, and scalability, papAI empowers organizations to operationalize AI confidently, turning innovative ideas into reliable, real-world applications that deliver measurable value.

Can you share a success story where Datategy’s AI solutions made a real difference for a client?

One of our standout success stories comes from Infogreffe, the economic interest grouping of the French commercial court registries. Infogreffe needed a more efficient way to predict financial difficulties of companies in real-time, enabling courts to intervene earlier and provide more proactive support. Traditionally, they calculated financial risk based on annual balance sheets, which limited their ability to act quickly enough.

In 2017, Infogreffe turned to Datategy rather than one of the big players to implement an AI solution. Our papAI platform enabled them to move from an annual prediction model to one based on real-time data. By analyzing more than fifteen key financial indicators and updating them regularly, we helped Infogreffe accurately predict financial instability on a daily basis.

The results were impressive: our AI model achieved a 95% detection rate for business failures, with a 90% true positive rate for predicting insolvencies. It allowed the commercial courts to receive daily reports, significantly improving the speed and precision with which they could assess companies’ financial health. Additionally, papAI platform provided an ergonomic interface, making it easy for Infogreffe to explore new models, refine predictions, and test various approaches to scoring financial risk.

Through this partnership, Infogreffe not only improved its forecasting accuracy but also gained deeper insights into market disruptions and opportunities that could impact companies’ financial stability. Datategy’s ongoing support, from product evolution to data science expertise, has made us more than just a solution provider—we are a trusted partner in their continuous pursuit of better decision-making.

What distinguishes Datategy’s approach to AI governance and transparency from traditional AI providers in the market?

Datategy was one of the first companies to embed governance and explainability as core pillars of its AI strategy, not optional features. We believe that no AI system can be truly valuable if it’s not trustworthy, auditable, and compliant with evolving regulations. Our approach goes beyond compliance checklists; it’s about creating a culture of transparency and accountability throughout the entire AI lifecycle.

With papAI, governance is built directly into the workflow. Every model developed and deployed through the platform is traceable, explainable, and monitored for drift or bias. This allows organizations to maintain full visibility on how decisions are made and to justify outcomes both internally and to regulators. In an era of increasing scrutiny around AI ethics and data use, this level of transparency is not just a competitive advantage; it’s a necessity.

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We Don’t Just Build AI, We Deliver Measurable Impact
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