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From Concept to Reality: Product Innovation with AI

Updated: Apr 5

Whether you're a SaaS provider or an enterprise creating applications for customers or employees, leveraging AI can be crucial for securing a competitive edge or ensuring your business's survival.

As Large Language Models (LLMs) become more prevalent in code generation, thereby reducing the cost of adding features, the significance of employing AI to refine user experience (UX) escalates.

Enterprises are currently tackling this challenge by integrating LLM-focused architectures. While LLMs are evolving to be more sophisticated and capable, they often come with drawbacks, such as slower performance, higher costs, and less reliability. Specifically, LLMs struggle with quantitative analysis, and even when they function correctly, they tend to be remarkably inefficient.

Conventional vs. Semantic Brain AI Architecture

Semantic Brain acknowledges the distinct advantages of Traditional AI, Graphs, and LLMs, and has crafted a solution that harnesses the optimal strengths of each technology where it's most beneficial, delivering better, more reliable, faster and cheaper solutions.

The Semantic Brain architecture propels product innovation forward, facilitating the emergence of new capabilities that extend beyond the conventional scope of chatbots, content generation, and data analytics solutions.

Traditional AI & BizML

LLMs frequently depend on APIs and traditional AI to conduct quantitative analysis, as they inherently lack proficiency in this area. In addition, it's crucial to recognize that 70% of traditional AI initiatives do not yield a return on investment, often due to problems with data availability and precision.

Semantic Brain's BizML employs unique feature engineering and selection techniques, consistently enhancing accuracy by 5% to 20% (or diminishing the error rate by 10% to 50%). Moreover, it can generate dependable models using significantly less data.

This approach has been proven effective on more than ten(10) projects with seven(7) customers. Some key successes were:

  1. Ad Optimization: Combining Search and Display Ads yielded up to 25x more conversions per Ad $

  2. Trading: Long and short equity micro portfolio trading simulation resulted in a 2-5x Sharpe Ratio relative to the market(e.g. S&P 500 Sharpe Ratio)

  3. Influencer and Social Media Scoring: Brand Awareness impact prediction error rate declined by 9% to 35%

  4. Hallucination Detection: Hallucination detection error rate declined by 40%

BizML vs. Conventional Process

Ad Optimization Case Study

We utilized NLP (Natural Language Processing), BizML, and traditional AI methods to evaluate and prioritize keywords and negative keywords for Google Search keywords and ad content, in addition to adjusting bid prices. This approach amplified audience acquisition by as much as five times per advertising dollar spent.

Furthermore, BizML and traditional AI were employed to categorize and prioritize potential audiences for display ads, which significantly enhanced conversion rates, achieving up to a fivefold increase per advertising dollar expended. The collective effect of acquisition and display ads resulted in an overall impact of up to 25 times the return per ad dollar spent.

For additional details, on our first customer case study in this space, visit -

BizML, combined with NLP, was employed to evaluate and choose keywords for advertising content, including headlines, descriptions, and landing pages. These keywords are then provided to the person who creates the content. Looking ahead, we plan to incorporate LLMs for content generation and updates under human oversight. Additionally, we intend to utilize Graphs and LLMs alongside BizML to enhance marketing and sales strategies, as well as the user experience for websites and products. Initial experiments in these domains have shown encouraging outcomes.

Trading Simulation Case Study

BizML originated from attempts to predict stock price trends and build optimal portfolios. Initial efforts to create portfolios using regression techniques that would deliver significant returns, like ROI or Sharpe Ratio, proved unsuccessful. The breakthrough came when we redirected our approach towards developing two distinct classification models, specifically designed to predict the magnitude and direction of changes independently, rather than depending on a singular regression model for price forecasting. This strategic shift was instrumental in developing BizML, which aimed to improve the signal-to-noise ratio. Combined with the two classification models, BizML yielded exceptional results.

EOD (end-of-day) historical OHLCV (open, high, low, close, volume) data on equities, ETFs (e.g., S&P 500 and RUSSELL 1000 exchange-traded funds), and VIX(volatility index), along with fundamental and economic data (specifically interest rates), were utilized as inputs. Additional inputs were also derived. Subsequently, BizML was employed to engineer and select features, improving predictions over 5 or 10 trading days.

As the image below illustrates, approximately 70% of the equities predicted to rise did so in the anticipated direction, with the magnitude of change being greater on the upside than the downside.

Over a five-month window, micro portfolios that combined long and short equities yielded a Sharpe Ratio more than five times higher than that of the S&P 500.

Current initiatives in this area involve employing LLMs to gather data from U.S. Securities and Exchange Commission (SEC) filings, such as Forms 10-K, 10-Q, and 8-K, and storing the results from BizML and LLMs in graphs. This approach facilitates advanced and cohesive analytics, combining linguistic and quantitative elements.

Using Graphs

LLM solutions are progressively integrating graphs to bolster the reliability of Retrieval Augmented Generation, a technique commonly employed for handling private or dynamic data. This enhancement is achieved by employing graphs to augment prompts and extract pertinent information.

On the other hand, the Semantic Brain solution opts for a graph-centric strategy where it fits best. Graphs are adept at more accurately modelling specific domains. Merging statistical and graph analytics can further uncover valuable insights. The graph not only includes linguistic content but also houses quantitative data, portions of which are derived from traditional AI and BizML.

Furthermore, LLMs enhance the accessibility of graphs by allowing them to be queried using natural language, eliminating the need for users to learn a new query language.

Use Case Types

LLMs generally facilitate interactions initiated by users through prompts and responses. In contrast, the Semantic Brain architecture, which integrates traditional AI and graphs, enhances Notifications and Topic Prediction, as traditional AI and graphs excel in predictive analytics.

Notifications serve as a highly effective application, enabling proactive user engagement. They can be triggered for various purposes, such as:

  1. Alerting a user to contribute to a workflow initiated by someone else. In this scenario, the notification would specifically tailor relevant data to suit the recipient's needs. For instance, when a decision needs to be made in trading, the risk manager would receive notifications that highlight risk factors and a quantitative assessment explicitly formatted for their review.

  2. Informing the sales team about a prospective customer's activity on the website or social media, indicating a potential revenue opportunity.

  3. Detecting a trading anomaly (in price or volume), pinpointing information likely causing it, and notifying investors who have the affected asset in their portfolio or on their watchlist.


At Semantic Brain, we conceptualize super-intelligence within the enterprise realm as the capability to surpass the productivity achievable with your elite employees and existing systems. We interpret productivity enhancement as a dual function of both quality and quantity enhancements. The integration of language and quantitative analytics is crucial for attaining super-intelligence. We are capable of delivering narrow super-intelligence at both the task and workflow levels:

  1. On the task level, Semantic Brain consistently executes multiple tasks upon receiving a single user prompt, known as a higher-order task.

  2. On the workflow level, Semantic Brain consistently carries out multiple tasks (including higher-order tasks) and orchestrates interactions among several individuals. Notifications are utilized to facilitate coordination with multiple users.

The video below showcases the Semantic Brain executing higher-order tasks, specifically integrating various language and quantitative analysis activities based on a single user input.

While we anticipate that LLMs will persist in evolving, supporting an expanding array of enterprise use cases, and driving product innovation, our confidence is even stronger in our solution's ability to enable a broader spectrum of use cases and enhance productivity more significantly, both in the short and long term.


The applications facilitated by Semantic Brain can be broadly classified into three categories, as outlined below.

Graphs + LLM

This encompasses primarily language-based applications, with our current emphasis on Risk & Compliance, particularly in the realms of Cybersecurity and DevSecOps. We have incorporated essential NIST (National Institute of Standards and Technology) and PCI (Payment Card Industry) documentation, enabling users to engage in:

  1. Question answering

  2. Requirement generation

  3. Sample code generation

Information security frequently impedes product innovation, and our solution is designed to mitigate this issue. When used alongside AI Software Engineering Assistants, our solution has the potential to speed up product innovation greatly.

Traditional AI + Graphs + LLM

These applications merge language and quantitative capabilities, typically employed to address optimization challenges. Examples of their use include optimizing marketing and sales strategies, product and website user experience, as well as financial return and risk management.

This is the core capability of the Semantic Brain Platform, and details are provided throughout this document.

Semantic Shield - AI Security, Safety & Alignment

Semantic Shield is an open-source project created to provide AI Security and DevSecOps for AI-driven and hybrid applications. It's crafted as an open framework, allowing for the integration of both customer and third-party components, ensuring the security of the Semantic Brain Platform.

Further information about Semantic Shield is available at -

Summary & Conclusion

Utilizing AI to catalyze product innovation is essential for SaaS providers and enterprises that develop their applications.

  1. While current LLM-centric methods are gaining strength, they face significant hurdles. LLMs struggle with quantitative analysis and tend to be slower, more costly, and less dependable.

  2. Combining traditional AI with Semantic Brain's BizML technology can enhance accuracy by 5% to 20% (or reduce error rates by 10% to 50%) and require considerably less data.

    1. Although AI/ML specialists might develop more accurate models, BizML empowers domain experts or those with less specialized skills to develop better or comparable models quickly most of the time.

    2. BizML is also equipped with standard integrations for Marketing, Web Analytics, and Finance data sources, further speeding up model development.

  3. Our strategy includes a graph-first approach where it fits best. These graphs maintain both language and quantitative data, facilitating advanced analytics.

  4. Employing traditional AI alongside BizML, graphs, and LLMs as foundational layers allows us to offer superior capabilities compared to LLM-centric solutions. This method also results in quicker applications and lower operational costs.

In summary, our integrated approach combining traditional AI, Semantic Brain's BizML, and graph-first methodologies with LLMs significantly enhances product innovation, offering a versatile, cost-effective solution. This strategy not only overcomes the limitations of LLM-centric models but also accelerates the development of accurate, efficient applications, empowering enterprises and SaaS vendors to harness the full potential of AI for sustained innovation and improved operational efficiency.

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