top of page

BizML: Revolutionizing Business Optimization

Updated: May 5

BizML stands at the forefront of Semantic Brain's efforts to elevate AI's impact on business operations. Developed meticulously, this innovation brings forth cutting-edge algorithms in Feature Engineering and Selection, enhancing the accuracy of Predictive, Diagnostic, and Prescriptive Analytics by 5% to 20%. Moreover, BizML optimizes data usage, further boosting efficiency.

Designed for easy integration, BizML complements your existing tech, data, and AI frameworks.

Through its synergy with Generative AI, BizML drives greater automation and improved business results. This collaborative approach yields substantial productivity gains, potentially exceeding 10 times the current levels. Productivity gains factor in both quantitative and qualitative improvements.

BizML transcends the conventional boundaries of a complex tool meant only for data scientists. It is crafted to be user-friendly for business professionals and analysts, transforming complex statistical and machine-learning concepts into accessible business insights. This approach democratizes advanced data-driven decision-making, enabling users from various backgrounds to develop and refine analytical models.

1 The Problem: Why BizML?

1.1 No ROI on 70% of Traditional AI Projects

Traditional AI projects fail to deliver ROI due to various reasons, including:

  • Unnecessary overemphasis on Big Data and Deep Learning

    • Substantially increases initial investment requirements

    • Increases ongoing operating costs

    • Increases complexity driving more risk

  • Insufficient data quantity and data quality

  • Lack of business emphasis and domain expert involvement

  • Model performance and trust

    • Accuracy and explainability of negative feedback loop

1.2 LLMs Fail at Analytics

LLMs were designed to understand human language(now increasingly more capable of understanding image content).

However, they have the following disadvantages(relative to APIs and Traditional AI)

  1. Higher cost(especially larger models)

  2. Slower

  3. Accuracy

Even if LLMs surpass human intelligence in all areas, they will not outperform APIs and Traditional AI in analytics.

2 What is BizML?

BizML is a proprietary technology composed of advanced Feature Engineering algorithms, including Feature Normalization and Selection, designed to enhance the accuracy of Supervised Learning Models while often requiring significantly less data. It operates by amplifying the signal and minimizing the noise within the data.

The BizML process, developed by Semantic Brain, begins with identifying available data and consulting with Domain Experts to pinpoint formulas that transform this data into Derived Input. BizML then processes this Derived Input into Features, leading to the creation of more accurate models. Additionally, a feedback loop is established between Feature Engineering and Domain Expertise, which facilitates the codification, validation, and refinement of Domain Expertise over time.

3 BizML Benefits

3.1 Increased Accuracy/Reduced Error Rates

Increased Accuracy and reduced Error Rates drive

  1. Operational Excellence:

    1. Marketing & Sales(focus Digital Marketing): Ads, Website, SEO, Cross Selling, Up-selling, Lead scoring

    2. Supply Chain Optimization: Inventory Management

  2. Better Risk Management:

    1. Marketing & Sales: Customer Churn Prediction

    2. Supply Chain: Supplier Risk Management, Inventory Risk Management

    3. Financial Services: Credit Risk, Fraud Detection, Portfolio Risk Management

BizML has consistently delivered the following results

  • 5% to 20% increase in Accuracy

  • and/or 10% to 50% reduction in error rate

In the above example, 

  • Accuracy increased from 0.79 to 0.87

  • This is a ~10% increase in Accuracy=(0.87-0.79)/0.79

  • Similarly errors declined from 0.21(1-0.79)  to 0.13(1-0.87)

  • This is a ~38% reduction in Error Rate = Error Reduction(0.87-0.79=0.08)/Pre BizML Error Rate(1-0.79=0.21)

In this particular case, the algorithm identified less than 20% of Hallucinations before using BizML and more than 50% of Hallucinations after.

3.2 Requires Much Less Data

BizML substantially reduces data requirements, which can benefit enterprises in numerous ways.

3.2.1 Rescue Projects

Numerous projects stall or fail to deliver a return on investment due to insufficient data quality and quantity. BizML has the capability to revive and steer these projects back on track.

3.2.2 Time to Market

Thanks to BizML's lower data requirements, many AI/ML projects can be accelerated into production phases more quickly. Once in production, BizML enables quicker optimization adjustments, reducing waste and enhancing agility.

3.2.3 Lower TCO

Lower data requirements lead to reduced data acquisition and infrastructure expenses, thereby decreasing the initial investment and ongoing operational costs.

3.2.4 De-risk Projects Early

Enterprises can evaluate the feasibility of projects with minimal data, allowing them to identify and mitigate risks at an earlier stage.

3.3 Optimize Human Resources

AI is poised to greatly influence IT, particularly in software development, where productivity is anticipated to increase by at least threefold over the next three years. By integrating BizML with AutoML tools like PyCaret, Data Analysts and Software Engineers can create robust AI/ML solutions. This integration lowers the barrier to entry and significantly boosts overall productivity.

4 BizML Analytics Capabilities

Business Intelligence (BI) and traditional analytics provide insights from historical data, helping organizations understand past and current performance through descriptive analytics. However, they primarily focus on explaining what happened and why, after events have occurred.

BizML enhances these capabilities by supporting more advanced forms of analytics:

4.1 Predictive Analytics

BizML improves predictive models by using sophisticated feature engineering. This allows organizations to forecast future outcomes with greater precision, which is crucial for areas like demand forecasting and risk assessment.

4.2 Diagnostic Analytics

BizML refines diagnostic analytics by identifying relationships and factors contributing to outcomes more efficiently. It enables deeper insights into operational issues and market dynamics, helping organizations understand the root causes behind events.

4.3 Prescriptive Analytics

Moving beyond prediction, BizML supports prescriptive analytics by suggesting actionable strategies and automating decisions. For example, in supply chain management, it can prescribe optimal inventory strategies and delivery routes.

Continuous Improvement with Feedback Loops

BizML integrates feature engineering with domain expertise through a feedback loop, enabling continuous model improvement and adaptation to new data and conditions. This dynamic process enhances the system’s intelligence and responsiveness over time.

In summary, while BI and traditional analytics help analyze historical data, BizML broadens the scope by enhancing predictive, diagnostic, and prescriptive analytics. This allows businesses to analyze past and present data and proactively manage and influence future events, leading to significant competitive advantages and operational efficiencies.

5 Semantic Brain Platform

The Semantic Brain Platform is anchored by two main features: Semantic Precision and Semantic Shield. Semantic Precision drives intelligent orchestration, weaving together:

  • BizML: As previously outlined, it fine-tunes predictive modelling and analytics.

  • Semantic Graph: It taps into Graph Analytics to yield distinctive insights and fuses quantitative data with linguistic elements.

  • Semantic Query: It empowers users to interact with data sources using conversational language and to carry out instructions articulated in natural language.

Together, these elements of the Semantic Brain Platform synergize to enhance analytical clarity, streamline user queries, and enrich the decision-making process.

Note: Semantic Shield is an open-source initiative, and its details can be found here 

Incorporating BizML and Semantic Query, the Semantic Brain Platform goes beyond the query-response functionality typical of traditional LLMs, propelling productivity gains upwards of 10X. Beyond standard interactions, Semantic Precision facilitates a range of additional use cases, such as:

  • Batch Reports: Automates the generation of periodic analytics reports, such as daily or weekly summaries, by harnessing the analytical power of BizML combined with the linguistic capabilities of LLMs/Agents.

  • Notifications: Delivers event-triggered alerts based on quantitative analysis or linguistic cues, ensuring timely updates.

  • Topic Prediction: Identifies and predicts topics of interest, providing proactive insights and content relevance.

These extended capabilities transform the Semantic Brain Platform into a more dynamic system capable of responding to direct inquiries, anticipating needs, and offering sophisticated, automated reporting and notification services.

6 Business Optimization

BizML delivers instant benefits and steers your organization towards an AI-enhanced future.

Whether used alone or in conjunction with Semantic Precision, BizML catalyzes profound changes across your business landscape, integrating smoothly with your current tech, data, and AI setup.

For immediate impact, focus on initiatives that offer quick, measurable returns:

  • Improve the accuracy of models in production settings.

  • Revitalize AI initiatives that have been hindered by issues of accuracy, reliability, or data quality to ensure their successful deployment.

Innovate for Business Optimization:

  • Boost operational excellence, particularly in marketing and supply chain management.

  • Enhance risk management in financial services and supply chain sectors.

For the medium term, pursue strategic transformations where ROI may take longer to realize:

  • AI Transformation: Enable business users, data analysts, and software engineers to independently develop and maintain AI models, reducing reliance on data scientists. Allow business users to use AI tools with minimal oversight, driving AI optimization throughout your organization.

  • Digital Transformation: Help digital marketing, enterprise web applications, and SaaS providers utilize analytics from major platforms like Google Analytics, Google Search Console, and Adobe Analytics. Integrate these analytics with Generative AI for streamlined, optimized results.

Join us on this path, secure immediate benefits, and elevate your enterprise to new heights.

31 views0 comments

Recent Posts

See All


bottom of page