Quantify uncertainty with universal probabilistic modelling

Untangle complex datasets once and for all, with auto-built mathematical models.
Understand the past and look into the future to take action faster with confidence and precision.

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  • Corporate-grade suite for data.

    Forget about drowning in a swamp of cumbersome data science pipelines. Escape the endless loop of model building, fine-tuning, maintenance… Get seamless insight and automate advanced data workloads to achieve your business goals.

  • Genuine maths perfection.

    Leverage probability, the branch of maths and only scientific tool to deal with uncertainty. Solve the toughest combinatorial problems from large datasets, and set the benchmark for any further analytics.

  • New questions, instant answers.

    Get real-time, 100% accurate and traceable results on the most complex datasets. GoFlek auto-adapts on the fly as new questions arise and new data comes in.

  • Conceived for profitability.

    Reach clear ROI on data and AI initiatives, with scalable tools to boost and align your teams. Drastically cut costs of scarce expertise and expensive providers that lock you in and keep your organization in the dark.

  • Transparent and traceable "AI".

    Take decisions with confidence, build trust among all parties, and actually act upon GoFlek’s unique open-box, determinisitc outcomes. Set your organisation for the long-run in the AI race.

  • Sovereign data capabilities.

    Avoid over-reliance on tech oligopolies and their intermediaries to handle your precious data and critical business insights. Shield your organization with self-standing tech disruptors.

Infinite data capabilities

GoFlek’s core technology, the Flek Machine, integrates countless possibilities into one coherent suite to unlock the most complex datasets.

  • Connect the Flek Machine to your dataset to automatically generate its unique universal probabilistic model that auto-adapts on the fly.

    As the number of datapoints (rows), variables (columns) and features (values on variables) increase, combinatorial permutations skyrocket. This is modelled and tamed within the Flek Machine.

  • Multi-dimensional datasets (with many datapoints, variables and features) can be difficult to apprehend. The auto-discovery module immediately sets you on the right track, by detecting interesting and notable probabilistic patterns and relationships.

  • Assess and visualize key relationships among variables, from common (joint and conditional probability, association…) to highly complex patterns that no other toolset can do today (influence, causality, anomaly, polymaly...).

  • Make targeted predictions given different multi-faceted hypothesis and conditions. Use predictive capabilities for both complex strategic foresight, and mundane dataset cleaning and auto-filling of missing values.

  • Provide customized recommendation with the highest chance of conversion into desired outcome. For instance, provide both item and profile-based purchasing recommendations
    (Amazon cannot do it).

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Some business dilemmas we solve

  • Because the future is unknown and the business world is forward-looking, organizations need tools that rely on past trends to peek into what is coming. The one and only scientific method for this purpose is probability. To outpace competition, businesses also require speed and flexibility to ask new questions and explore new scenarios.

    Common data science approaches (decision trees, random forests, neural networks…) are computer science algorithms - not scientific methods - and have limitations such as bias, lack of flexibility and lack of interpretability.

  • Some decisions faced by organizations can have massive consequences, from multi-million dollar losses to human lives or regulatory breaches. Taking the optimal route based on available data with confidence, and the ability to easily demonstrate and back decisions, is critical for executives and business-leaders.

    Yet current developments in AI automation and black-box computer science algorithms go the opposite route, and put businesses at risk of serious backlash in risky environments.

  • In many situations, data is scarce and variables might be missing. Nontheless, organizations still need reliable tools that work and provide accurate answers to their questions, even with small sample sizes.

    Data science algorithms are not equipped to face this common situation, and require huge amounts of data to be trained and fine-tuned to provide satisfying results.

  • Data science and “AI” is not something new: many organizations have been collecting, storing and structuring quantitative data across the board for years. Overwhelmed by the sheer amount of data, it can be tricky to even know where to start, and to sort relevant variables with meaningful relationships from noise. To uncover hidden value, both a mathematical foundation for benchmarking and auto-discovery tools are required.

    Even the most advanced data science algorithms are unfit to perform these high-precision, multi-dimensional, and wide exploratory workloads.

  • Off-the-shelf computer science algorithms and top gen-AI tools are already a public commodity. To beat competition in this business environment (whether for business intelligence, strategic and scenario planning, quantitative research…), innovative organizations must build their ability to take bold business decisions with the speed of certainty.

    This combination of speed and confidence in data operations cannot be achieved without shifting the paradigm and without technological breakthroughs and new math-based standards.

  • All organizations have teams, stakeholders and business partners with various levels of expertise and affinity with data. To build true, deeply-rooted data literacy in your business, the same “benchmarking” tools must be shared and scaled. They also need to bring the highest level of rigor, efficiency and understandability.

    The diversity, complexity and opaqueness of current data science tools and techniques is a major limitation. because they cannot be shared, understood and scaled easily. Some data practitionners might use specific data science algorithms for targetted tasks of course, but this is not enough to build a “data-driven” organization.

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