Use Cases
Use Cases
Use Cases
Case Study : Architecture of Truth (Databases)
Case Study : Architecture of Truth (Databases)
Case Study : Architecture of Truth (Databases)
Base Service: Databases (Design & Architecture, Data Governance, Migration & Modernization)
Base Service: Databases (Design & Architecture, Data Governance, Migration & Modernization)
Base Service: Databases (Design & Architecture, Data Governance, Migration & Modernization)
The Trivial Question vs. The Zyliica Question
The Trivial Question vs. The Zyliica Question
The Trivial Question vs. The Zyliica Question
Client Challenge: "We need to consolidate our sales, marketing, and operations data, which is currently scattered across 12 Excel files and an obsolete SQL server. We are losing data and cannot generate reports."
The Zyliica Question (Question Architecture): "How do we design a Data Warehouse that not only stores data but is optimized to answer the predictive business questions you don't even know yet, while ensuring integrity through Data Governance?"
Client Challenge: "We need to consolidate our sales, marketing, and operations data, which is currently scattered across 12 Excel files and an obsolete SQL server. We are losing data and cannot generate reports."
The Zyliica Question (Question Architecture): "How do we design a Data Warehouse that not only stores data but is optimized to answer the predictive business questions you don't even know yet, while ensuring integrity through Data Governance?"
The Zyliica Solution (Applied Methodology)
The Zyliica Solution (Applied Methodology)
The Zyliica Solution (Applied Methodology)
Foundation: Design of the Entity-Relationship Schema for the new Data Warehouse (Snowflake).
Information Engineering: Implementation of Data Pipelines (ETL) that automate the Transformation and Cleaning of chaotic data from the 12 original sources into a Single Source of Truth.
Governance: Creation of Data Governance Protocols to ensure quality and compliance for future data.
Foundation: Design of the Entity-Relationship Schema for the new Data Warehouse (Snowflake).
Information Engineering: Implementation of Data Pipelines (ETL) that automate the Transformation and Cleaning of chaotic data from the 12 original sources into a Single Source of Truth.
Governance: Creation of Data Governance Protocols to ensure quality and compliance for future data.
Foundation: Design of the Entity-Relationship Schema for the new Data Warehouse (Snowflake).
Information Engineering: Implementation of Data Pipelines (ETL) that automate the Transformation and Cleaning of chaotic data from the 12 original sources into a Single Source of Truth.
Governance: Creation of Data Governance Protocols to ensure quality and compliance for future data.
The Result (The Hidden Story)
The Result (The Hidden Story)
The Result (The Hidden Story)
Trivial Result: Daily reports.
Zyliica Result (Strategic Impact): 5 TB of historical data were migrated and unified with zero loss (Migration & Modernization). The client gained, for the first time, a complete and unified view of the customer lifecycle, allowing Zyliica to implement a Predictive Model (Churn) in the next phase with 95% confidence using clean data.
Trivial Result: Daily reports.
Zyliica Result (Strategic Impact): 5 TB of historical data were migrated and unified with zero loss (Migration & Modernization). The client gained, for the first time, a complete and unified view of the customer lifecycle, allowing Zyliica to implement a Predictive Model (Churn) in the next phase with 95% confidence using clean data.
CASE STUDY: Transformation Engineering (ETL & Transformation)
CASE STUDY: Transformation Engineering (ETL & Transformation)
CASE STUDY: Transformation Engineering (ETL & Transformation)
Base Service: ETL & Transformation (Data Warehouse, Data Lake, Data Marts, Data Cleaning).
Base Service: ETL & Transformation (Data Warehouse, Data Lake, Data Marts, Data Cleaning).
Base Service: ETL & Transformation (Data Warehouse, Data Lake, Data Marts, Data Cleaning).
The Trivial Question vs. The Zyliica Question.
The Trivial Question vs. The Zyliica Question.
The Trivial Question vs. The Zyliica Question.
Client Challenge: "Our marketing (HubSpot), sales (Salesforce), and support (Zendesk) data don't talk to each other. We lose 30% of our time reconciling information".
The Zyliica Question: "How do we design an ETL pipeline that translates and unifies these disparate narratives into a Single Source of Truth, ensuring data quality for cross-platform predictive analysis?".
Client Challenge: "Our marketing (HubSpot), sales (Salesforce), and support (Zendesk) data don't talk to each other. We lose 30% of our time reconciling information".
The Zyliica Question: "How do we design an ETL pipeline that translates and unifies these disparate narratives into a Single Source of Truth, ensuring data quality for cross-platform predictive analysis?".
Client Challenge: "Our marketing (HubSpot), sales (Salesforce), and support (Zendesk) data don't talk to each other. We lose 30% of our time reconciling information".
The Zyliica Question: "How do we design an ETL pipeline that translates and unifies these disparate narratives into a Single Source of Truth, ensuring data quality for cross-platform predictive analysis?".
The Zyliica Solution (Applied Methodology).
The Zyliica Solution (Applied Methodology).
The Zyliica Solution (Applied Methodology)
Foundation: Design of a Data Lake (Historical Archive) to store raw data and a Data Warehouse (Central Library) for structured analysis.
Information Engineering: Implementation of ETL pipelines to automate extraction and cleaning, establishing a unified "Customer Lifetime Value" metric.
Narrative: Creation of specific Data Marts (Study Rooms) for Marketing and Sales, ensuring quick access to clean data without saturating the central system.
Foundation: Design of a Data Lake (Historical Archive) to store raw data and a Data Warehouse (Central Library) for structured analysis.
Information Engineering: Implementation of ETL pipelines to automate extraction and cleaning, establishing a unified "Customer Lifetime Value" metric.
Narrative: Creation of specific Data Marts (Study Rooms) for Marketing and Sales, ensuring quick access to clean data without saturating the central system.
Foundation: Design of a Data Lake (Historical Archive) to store raw data and a Data Warehouse (Central Library) for structured analysis.
Information Engineering: Implementation of ETL pipelines to automate extraction and cleaning, establishing a unified "Customer Lifetime Value" metric.
Narrative: Creation of specific Data Marts (Study Rooms) for Marketing and Sales, ensuring quick access to clean data without saturating the central system.
The Result (The Hidden Story)
The Result (The Hidden Story)
The Result (The Hidden Story)
Trivial Result: Reduction in reporting time.
Zyliica Result: 100% elimination of data silos. The client obtained a Narrative Dashboard showing real acquisition cost vs. lifetime value, leading to a 15% increase in campaign profitability.
Trivial Result: Reduction in reporting time.
Zyliica Result: 100% elimination of data silos. The client obtained a Narrative Dashboard showing real acquisition cost vs. lifetime value, leading to a 15% increase in campaign profitability.
Trivial Result: Reduction in reporting time.
Zyliica Result: 100% elimination of data silos. The client obtained a Narrative Dashboard showing real acquisition cost vs. lifetime value, leading to a 15% increase in campaign profitability.
CASE STUDY: The Human Story (Statistical Analysis)
CASE STUDY: The Human Story (Statistical Analysis)
CASE STUDY: The Human Story (Statistical Analysis)
Base Service: Statistical Analysis (Hypothesis Testing & Validation, Causal & Explanatory Models, Survey Design & Metrics).
Base Service: Statistical Analysis (Hypothesis Testing & Validation, Causal & Explanatory Models, Survey Design & Metrics).
Base Service: Statistical Analysis (Hypothesis Testing & Validation, Causal & Explanatory Models, Survey Design & Metrics).
The Trivial Question vs. The Zyliica Question
The Trivial Question vs. The Zyliica Question
The Trivial Question vs. The Zyliica Question
Client Challenge: "Our latest satisfaction survey indicates that 70% of customers are 'satisfied.' However, 40% of them do not recommend us and are stopping their purchases. We do not understand the real cause of this disconnect".
The Zyliica Question: "What is the hidden causal variable (the 'Human Story') that explains the gap between 'perceived satisfaction' and 'actual purchase intent'? How do we design a Causal and Explanatory Model to validate the business hypotheses that will give us the actionable answer?".
Desafío del Cliente: "Nuestra última encuesta de satisfacción indica que el 70% de los clientes está 'satisfecho'. Pero el 40% de ellos no nos recomienda y está dejando de comprar. No entendemos la causa real de la desconexión."
La Pregunta Zyliica (Arquitectura de la Pregunta): "¿Cuál es la variable causal oculta (el 'Relato Humano') que explica la brecha entre la 'satisfacción percibida' y la 'intención real de compra'? ¿Cómo diseñamos un Modelo Causal y Explicatorio para validar las hipótesis de negocio que nos darán la respuesta accionable?"
Client Challenge: "Our latest satisfaction survey indicates that 70% of customers are 'satisfied.' However, 40% of them do not recommend us and are stopping their purchases. We do not understand the real cause of this disconnect".
The Zyliica Question: "What is the hidden causal variable (the 'Human Story') that explains the gap between 'perceived satisfaction' and 'actual purchase intent'? How do we design a Causal and Explanatory Model to validate the business hypotheses that will give us the actionable answer?".
Client Challenge: "Our latest satisfaction survey indicates that 70% of customers are 'satisfied.' However, 40% of them do not recommend us and are stopping their purchases. We do not understand the real cause of this disconnect".
The Zyliica Question: "What is the hidden causal variable (the 'Human Story') that explains the gap between 'perceived satisfaction' and 'actual purchase intent'? How do we design a Causal and Explanatory Model to validate the business hypotheses that will give us the actionable answer?".
The Zyliica Solution (Applied Methodology)
The Zyliica Solution (Applied Methodology)
The Zyliica Solution (Applied Methodology)
Foundation: Audit of the client's Survey Design & Metrics to eliminate bias and ensure data confidence.
Information Engineering: Application of Causal & Explanatory Models (e.g., Multiple Regression or Path Analysis) on survey data and purchase metrics, using the correct technical stack (e.g., Stata or R).
Narrative: Hypothesis Testing & Validation to statistically confirm that the cause of non-recommendation is a "loss of confidence in post-sale service," rather than the product itself.
Foundation: Audit of the client's Survey Design & Metrics to eliminate bias and ensure data confidence.
Information Engineering: Application of Causal & Explanatory Models (e.g., Multiple Regression or Path Analysis) on survey data and purchase metrics, using the correct technical stack (e.g., Stata or R).
Narrative: Hypothesis Testing & Validation to statistically confirm that the cause of non-recommendation is a "loss of confidence in post-sale service," rather than the product itself.
Foundation: Audit of the client's Survey Design & Metrics to eliminate bias and ensure data confidence.
Information Engineering: Application of Causal & Explanatory Models (e.g., Multiple Regression or Path Analysis) on survey data and purchase metrics, using the correct technical stack (e.g., Stata or R).
Narrative: Hypothesis Testing & Validation to statistically confirm that the cause of non-recommendation is a "loss of confidence in post-sale service," rather than the product itself.
The Result (The Hidden Story)
The Result (The Hidden Story)
The Result (The Hidden Story)
Trivial Result: The conclusion that "price is a problem".
Zyliica Result (Strategic Impact): The Causal Model proved that "satisfaction" was irrelevant. The hidden variable driving retention was the "Technical Service Response Speed." With this truth, the client stopped investing in product quality (which was already high) and reorganized their budget to automate technical service, resulting in a 25% increase in recommendations and a 10% drop in churn rate within six months.
Trivial Result: The conclusion that "price is a problem".
Zyliica Result (Strategic Impact): The Causal Model proved that "satisfaction" was irrelevant. The hidden variable driving retention was the "Technical Service Response Speed." With this truth, the client stopped investing in product quality (which was already high) and reorganized their budget to automate technical service, resulting in a 25% increase in recommendations and a 10% drop in churn rate within six months.
Trivial Result: The conclusion that "price is a problem".
Zyliica Result (Strategic Impact): The Causal Model proved that "satisfaction" was irrelevant. The hidden variable driving retention was the "Technical Service Response Speed." With this truth, the client stopped investing in product quality (which was already high) and reorganized their budget to automate technical service, resulting in a 25% increase in recommendations and a 10% drop in churn rate within six months.
CASE STUDY: The Script of the Future (Data Science)
CASE STUDY: The Script of the Future (Data Science)
CASE STUDY: The Script of the Future (Data Science)
Base Service: Data Science (Predictive Modeling & Forecasting, Voice & Sentiment Analysis, Segmentation & Profiling).
Base Service: Data Science (Predictive Modeling & Forecasting, Voice & Sentiment Analysis, Segmentation & Profiling).
Base Service: Data Science (Predictive Modeling & Forecasting, Voice & Sentiment Analysis, Segmentation & Profiling).
The Trivial Question vs. The Zyliica Question
The Trivial Question vs. The Zyliica Question
The Trivial Question vs. The Zyliica Question
Client Challenge: "We need to know why our app has a high uninstallation rate. We believe it is a bug issue, but we don't know where to focus development resources. We are investing blindly".
The Zyliica Question: "What is the underlying emotion and narrative context (identified by NLP) that precedes uninstallation? How do we build a Predictive Churn Model that gives us an early warning for 80% of users about to leave?".
Client Challenge: "We need to know why our app has a high uninstallation rate. We believe it is a bug issue, but we don't know where to focus development resources. We are investing blindly".
The Zyliica Question: "What is the underlying emotion and narrative context (identified by NLP) that precedes uninstallation? How do we build a Predictive Churn Model that gives us an early warning for 80% of users about to leave?".
Client Challenge: "We need to know why our app has a high uninstallation rate. We believe it is a bug issue, but we don't know where to focus development resources. We are investing blindly".
The Zyliica Question: "What is the underlying emotion and narrative context (identified by NLP) that precedes uninstallation? How do we build a Predictive Churn Model that gives us an early warning for 80% of users about to leave?".
Client Challenge: "We need to know why our app has a high uninstallation rate. We believe it is a bug issue, but we don't know where to focus development resources. We are investing blindly".
The Zyliica Question: "What is the underlying emotion and narrative context (identified by NLP) that precedes uninstallation? How do we build a Predictive Churn Model that gives us an early warning for 80% of users about to leave?".
The Zyliica Solution (Applied Methodology)
The Zyliica Solution (Applied Methodology)
The Zyliica Solution (Applied Methodology)
Foundation: Design of an ETL Pipeline to unify app usage data with comments and reviews (unstructured text data).
Engineering of Truth: Application of Voice & Sentiment Analysis (NLP) to read thousands of comments. It was discovered that the key factor was not bugs, but frustration with an incomprehensible onboarding flow (the "Hidden Story").
Predictive Modeling: Construction of a Predictive Model (Machine Learning) that uses interaction metrics and frustration keywords (NLP) to identify users at risk of churn with over 90% precision.
Foundation: Design of an ETL Pipeline to unify app usage data with comments and reviews (unstructured text data).
Engineering of Truth: Application of Voice & Sentiment Analysis (NLP) to read thousands of comments. It was discovered that the key factor was not bugs, but frustration with an incomprehensible onboarding flow (the "Hidden Story").
Predictive Modeling: Construction of a Predictive Model (Machine Learning) that uses interaction metrics and frustration keywords (NLP) to identify users at risk of churn with over 90% precision.
Foundation: Design of an ETL Pipeline to unify app usage data with comments and reviews (unstructured text data).
Engineering of Truth: Application of Voice & Sentiment Analysis (NLP) to read thousands of comments. It was discovered that the key factor was not bugs, but frustration with an incomprehensible onboarding flow (the "Hidden Story").
Predictive Modeling: Construction of a Predictive Model (Machine Learning) that uses interaction metrics and frustration keywords (NLP) to identify users at risk of churn with over 90% precision.
The Result (The Hidden Story)
The Result (The Hidden Story)
The Result (The Hidden Story)
Trivial Result: Random bugs are fixed.
Zyliica Result (Strategic Impact): The Predictive Model allowed the app to send a "narrative intervention" (a personalized email with direct help) to the top 10% of high-risk users just before they left. This reduced the churn rate by 22% in the first quarter. It was proven that the problem was not "code," but "narrative and experience"—validating the analysis and Zyliica.
Trivial Result: Random bugs are fixed.
Zyliica Result (Strategic Impact): The Predictive Model allowed the app to send a "narrative intervention" (a personalized email with direct help) to the top 10% of high-risk users just before they left. This reduced the churn rate by 22% in the first quarter. It was proven that the problem was not "code," but "narrative and experience"—validating the analysis and Zyliica.
Trivial Result: Random bugs are fixed.
Zyliica Result (Strategic Impact): The Predictive Model allowed the app to send a "narrative intervention" (a personalized email with direct help) to the top 10% of high-risk users just before they left. This reduced the churn rate by 22% in the first quarter. It was proven that the problem was not "code," but "narrative and experience"—validating the analysis and Zyliica.
CASE STUDY: The Narrative Dashboard (Data Visualization)
CASE STUDY: The Narrative Dashboard (Data Visualization)
CASE STUDY: The Narrative Dashboard (Data Visualization)
Base Service: Data Visualization (Corporate BI Mastery, Expert Data Visualization, Embedded Analytics)
Base Service: Data Visualization (Corporate BI Mastery, Expert Data Visualization, Embedded Analytics)
Base Service: Data Visualization (Corporate BI Mastery, Expert Data Visualization, Embedded Analytics)
The Trivial Question vs. The Zyliica Question
The Trivial Question vs. The Zyliica Question
The Trivial Question vs. The Zyliica Question
Client Challenge: "We have 15 different Power BI dashboards for Sales, Marketing, and Finance. They are all accurate, but visually confusing, and no one on the executive team can make a unified decision without wasting an hour in a meeting."
The Zyliica Question (Question Architecture): "How do we filter out visual noise to design a centralized Narrative Dashboard that tells the story of the business's health in five seconds, allowing leadership to make unified decisions with absolute confidence?"
Client Challenge: "We have 15 different Power BI dashboards for Sales, Marketing, and Finance. They are all accurate, but visually confusing, and no one on the executive team can make a unified decision without wasting an hour in a meeting."
The Zyliica Question (Question Architecture): "How do we filter out visual noise to design a centralized Narrative Dashboard that tells the story of the business's health in five seconds, allowing leadership to make unified decisions with absolute confidence?"
Client Challenge: "We have 15 different Power BI dashboards for Sales, Marketing, and Finance. They are all accurate, but visually confusing, and no one on the executive team can make a unified decision without wasting an hour in a meeting."
The Zyliica Question (Question Architecture): "How do we filter out visual noise to design a centralized Narrative Dashboard that tells the story of the business's health in five seconds, allowing leadership to make unified decisions with absolute confidence?"
The Zyliica Solution (Applied Methodology)
The Zyliica Solution (Applied Methodology)
The Zyliica Solution (Applied Methodology)
Foundation: Single Source of Truth (Data Warehouse) audit to ensure data from the 15 original sources is consistent (technical rigor).
Artistry & Precision: Application of Elite Design expertise to eliminate visual noise and create a Narrative Dashboard (Corporate BI Mastery). Only 5 key metrics are designed, each conveyed with the correct chart and strategically placed to narrate the state of the business.
Expert Data Visualization: Development of custom visualizations (using, for example, Python/Plotly) to represent complex relationships (such as Geospatial Heat Maps) that standard BI tools cannot generate.
Foundation: Single Source of Truth (Data Warehouse) audit to ensure data from the 15 original sources is consistent (technical rigor).
Artistry & Precision: Application of Elite Design expertise to eliminate visual noise and create a Narrative Dashboard (Corporate BI Mastery). Only 5 key metrics are designed, each conveyed with the correct chart and strategically placed to narrate the state of the business.
Expert Data Visualization: Development of custom visualizations (using, for example, Python/Plotly) to represent complex relationships (such as Geospatial Heat Maps) that standard BI tools cannot generate.
Foundation: Single Source of Truth (Data Warehouse) audit to ensure data from the 15 original sources is consistent (technical rigor).
Artistry & Precision: Application of Elite Design expertise to eliminate visual noise and create a Narrative Dashboard (Corporate BI Mastery). Only 5 key metrics are designed, each conveyed with the correct chart and strategically placed to narrate the state of the business.
Expert Data Visualization: Development of custom visualizations (using, for example, Python/Plotly) to represent complex relationships (such as Geospatial Heat Maps) that standard BI tools cannot generate.
The Result (The Hidden Story)
The Result (The Hidden Story)
The Result (The Hidden Story)
Trivial Result: A prettier dashboard.
Zyliica Result (Strategic Impact): 15 dashboards were consolidated into a single, intuitive Narrative Dashboard. This reduced executive report preparation time by 60%. Immediate visual clarity (the "Visual Response") allowed management to identify an operational inefficiency that was hidden in the fragmented data, resulting in cost savings of 8% in the following quarter.
Trivial Result: A prettier dashboard.
Zyliica Result (Strategic Impact): 15 dashboards were consolidated into a single, intuitive Narrative Dashboard. This reduced executive report preparation time by 60%. Immediate visual clarity (the "Visual Response") allowed management to identify an operational inefficiency that was hidden in the fragmented data, resulting in cost savings of 8% in the following quarter.
Trivial Result: A prettier dashboard.
Zyliica Result (Strategic Impact): 15 dashboards were consolidated into a single, intuitive Narrative Dashboard. This reduced executive report preparation time by 60%. Immediate visual clarity (the "Visual Response") allowed management to identify an operational inefficiency that was hidden in the fragmented data, resulting in cost savings of 8% in the following quarter.
USE CASE: The Trust Diagnosis (Advanced Risk Intelligence & Forensics)
USE CASE: The Trust Diagnosis (Advanced Risk Intelligence & Forensics)
USE CASE: The Trust Diagnosis (Advanced Risk Intelligence & Forensics)
Base Service: Advanced Risk Intelligence & Forensics (Forensic Data Services, Actuarial Risk Modeling, Probative Data Structuring, Damages Quantification)
Base Service: Advanced Risk Intelligence & Forensics (Forensic Data Services, Actuarial Risk Modeling, Probative Data Structuring, Damages Quantification)
Base Service: Advanced Risk Intelligence & Forensics (Forensic Data Services, Actuarial Risk Modeling, Probative Data Structuring, Damages Quantification)
The Trivial Question vs. The Zyliica Question
The Trivial Question vs. The Zyliica Question
The Trivial Question vs. The Zyliica Question
Client Challenge: "A division of our company is facing a potential lawsuit for an unjustified financial loss over the last three years. We need to quantify the actual damage and establish whether the problem was an operational error or systematic fraud, but the data is chaotic and legally inaccessible."
The Zyliica Question (Question Architecture): "How do we apply Forensic Data Services to reconstruct the digital chain of custody (Probative Data Structuring) of the transactions, and what Actuarial Risk Model can we build to quantify, with legal certainty, the attribution and the real magnitude of the damage?"
Client Challenge: "A division of our company is facing a potential lawsuit for an unjustified financial loss over the last three years. We need to quantify the actual damage and establish whether the problem was an operational error or systematic fraud, but the data is chaotic and legally inaccessible."
The Zyliica Question (Question Architecture): "How do we apply Forensic Data Services to reconstruct the digital chain of custody (Probative Data Structuring) of the transactions, and what Actuarial Risk Model can we build to quantify, with legal certainty, the attribution and the real magnitude of the damage?"
Client Challenge: "A division of our company is facing a potential lawsuit for an unjustified financial loss over the last three years. We need to quantify the actual damage and establish whether the problem was an operational error or systematic fraud, but the data is chaotic and legally inaccessible."
The Zyliica Question (Question Architecture): "How do we apply Forensic Data Services to reconstruct the digital chain of custody (Probative Data Structuring) of the transactions, and what Actuarial Risk Model can we build to quantify, with legal certainty, the attribution and the real magnitude of the damage?"
The Zyliica Solution (Applied Methodology)
The Zyliica Solution (Applied Methodology)
The Zyliica Solution (Applied Methodology)
Foundation: Application of Forensic Data Services to audit and reconstruct transactions, ensuring that Probative Data Structuring meets legal evidence standards (e-discovery).
Information Engineering: Design of an Actuarial Risk Model that uses validated historical data to simulate what-if scenarios and quantify attribution and Damages Quantification with statistical rigor.
Artistry & Precision: Creation of custom Graph Visualizations (Neo4j) to visually illustrate hidden connections between people, transactions, and time, revealing fraud patterns.
Foundation: Application of Forensic Data Services to audit and reconstruct transactions, ensuring that Probative Data Structuring meets legal evidence standards (e-discovery).
Information Engineering: Design of an Actuarial Risk Model that uses validated historical data to simulate what-if scenarios and quantify attribution and Damages Quantification with statistical rigor.
Artistry & Precision: Creation of custom Graph Visualizations (Neo4j) to visually illustrate hidden connections between people, transactions, and time, revealing fraud patterns.
Foundation: Application of Forensic Data Services to audit and reconstruct transactions, ensuring that Probative Data Structuring meets legal evidence standards (e-discovery).
Information Engineering: Design of an Actuarial Risk Model that uses validated historical data to simulate what-if scenarios and quantify attribution and Damages Quantification with statistical rigor.
Artistry & Precision: Creation of custom Graph Visualizations (Neo4j) to visually illustrate hidden connections between people, transactions, and time, revealing fraud patterns.
The Result (The Hidden Story)
The Result (The Hidden Story)
The Result (The Hidden Story)
Trivial Result: An accounting report of the loss.
Zyliica Result (Strategic Impact): Forensic Data revealed that 80% of the loss was not an error, but systematic fraud operated by a network of four individuals (information that was previously invisible). The Actuarial Model reduced legal risk by quantifying the actual damage in a precise figure, allowing the client to present irrefutable and admissible digital evidence in court. This transformed a legal risk into an asset recovery case based on data certainty.
Trivial Result: An accounting report of the loss.
Zyliica Result (Strategic Impact): Forensic Data revealed that 80% of the loss was not an error, but systematic fraud operated by a network of four individuals (information that was previously invisible). The Actuarial Model reduced legal risk by quantifying the actual damage in a precise figure, allowing the client to present irrefutable and admissible digital evidence in court. This transformed a legal risk into an asset recovery case based on data certainty.
Trivial Result: An accounting report of the loss.
Zyliica Result (Strategic Impact): Forensic Data revealed that 80% of the loss was not an error, but systematic fraud operated by a network of four individuals (information that was previously invisible). The Actuarial Model reduced legal risk by quantifying the actual damage in a precise figure, allowing the client to present irrefutable and admissible digital evidence in court. This transformed a legal risk into an asset recovery case based on data certainty.
CASE STUDY 2: The Script Laboratory (Data Science Storytelling)
CASE STUDY: The Script Laboratory (Data Science Storytelling)
CASE STUDY: The Script Laboratory (Data Science Storytelling)
Servicio Base: Data Science Storytelling (Relevant Question Design, Model Audit Protocol, Champion/Challenger Testing, Model Auditing & Validation)
Servicio Base: Data Science Storytelling (Relevant Question Design, Model Audit Protocol, Champion/Challenger Testing, Model Auditing & Validation)
Servicio Base: Data Science Storytelling (Relevant Question Design, Model Audit Protocol, Champion/Challenger Testing, Model Auditing & Validation)
The Trivial Question vs. The Zyliica Question
The Trivial Question vs. The Zyliica Question
The Trivial Question vs. The Zyliica Question
Client Challenge: "Our digital advertising campaign is expensive and gets clicks, but conversions on the landing page are low. We don't know if the problem is the copy, the design, or if we are targeting the wrong audience."
The Zyliica Question (Relevant Question Design - The Script): "Which of the two value proposition narratives (Story A: Focus on Savings vs. Story B: Focus on Exclusivity) resolves the deepest pain of our ideal customer? How do we design a Champion/Challenger experiment that doesn't test colors, but the intrinsic strength of the narrative script?"
Client Challenge: "Our digital advertising campaign is expensive and gets clicks, but conversions on the landing page are low. We don't know if the problem is the copy, the design, or if we are targeting the wrong audience."
The Zyliica Question (Relevant Question Design - The Script): "Which of the two value proposition narratives (Story A: Focus on Savings vs. Story B: Focus on Exclusivity) resolves the deepest pain of our ideal customer? How do we design a Champion/Challenger experiment that doesn't test colors, but the intrinsic strength of the narrative script?"
Client Challenge: "Our digital advertising campaign is expensive and gets clicks, but conversions on the landing page are low. We don't know if the problem is the copy, the design, or if we are targeting the wrong audience."
The Zyliica Question (Relevant Question Design - The Script): "Which of the two value proposition narratives (Story A: Focus on Savings vs. Story B: Focus on Exclusivity) resolves the deepest pain of our ideal customer? How do we design a Champion/Challenger experiment that doesn't test colors, but the intrinsic strength of the narrative script?"
The Zyliica Solution (Applied Methodology)
The Zyliica Solution (Applied Methodology)
The Zyliica Solution (Applied Methodology)
Foundation: Zyliica applies the Model Audit Protocol to verify that the current audience segmentation is reliable and not biased.
Question Architecture: The Champion/Challenger Testing experiment (The Audience Test) is designed. Two identical landing pages in design are developed (eliminating visual variables), but with opposing narratives (Story A vs. Story B) that appeal to different psychological motivations.
Strategic Laboratory: The experiment is launched and monitored. Zyliica analyzes the data to see which of the two scripts generates more engagement and conversion, delivering an undeniable truth about the customer's mind.
Foundation: Zyliica applies the Model Audit Protocol to verify that the current audience segmentation is reliable and not biased.
Question Architecture: The Champion/Challenger Testing experiment (The Audience Test) is designed. Two identical landing pages in design are developed (eliminating visual variables), but with opposing narratives (Story A vs. Story B) that appeal to different psychological motivations.
Strategic Laboratory: The experiment is launched and monitored. Zyliica analyzes the data to see which of the two scripts generates more engagement and conversion, delivering an undeniable truth about the customer's mind.
Foundation: Zyliica applies the Model Audit Protocol to verify that the current audience segmentation is reliable and not biased.
Question Architecture: The Champion/Challenger Testing experiment (The Audience Test) is designed. Two identical landing pages in design are developed (eliminating visual variables), but with opposing narratives (Story A vs. Story B) that appeal to different psychological motivations.
Strategic Laboratory: The experiment is launched and monitored. Zyliica analyzes the data to see which of the two scripts generates more engagement and conversion, delivering an undeniable truth about the customer's mind.
The Result (The Hidden Story)
The Result (The Hidden Story)
The Result (The Hidden Story)
Trivial Result: Conclusion that the landing page needs more buttons.
Zyliica Result (Strategic Impact): The Champion/Challenger experiment revealed that Story B (Exclusivity) outperformed Story A (Savings) by a 45% conversion rate. The client obtained the "Strategic Script" validated by data. This allowed them to discard the old narrative, reorganize their copy strategy across all social networks, and increase their Return on Investment (ROI) in advertising in the next cycle.
Trivial Result: Conclusion that the landing page needs more buttons.
Zyliica Result (Strategic Impact): The Champion/Challenger experiment revealed that Story B (Exclusivity) outperformed Story A (Savings) by a 45% conversion rate. The client obtained the "Strategic Script" validated by data. This allowed them to discard the old narrative, reorganize their copy strategy across all social networks, and increase their Return on Investment (ROI) in advertising in the next cycle.
Trivial Result: Conclusion that the landing page needs more buttons.
Zyliica Result (Strategic Impact): The Champion/Challenger experiment revealed that Story B (Exclusivity) outperformed Story A (Savings) by a 45% conversion rate. The client obtained the "Strategic Script" validated by data. This allowed them to discard the old narrative, reorganize their copy strategy across all social networks, and increase their Return on Investment (ROI) in advertising in the next cycle.
CASE STUDY: Interactive Story Engineering (Software Development)
CASE STUDY: Interactive Story Engineering (Software Development)
CASE STUDY: Interactive Story Engineering (Software Development)
Servicio Base: Software Development (PWAs, Mobile Apps, Desktop Applications, Microservices & Deployment)
Servicio Base: Software Development (PWAs, Mobile Apps, Desktop Applications, Microservices & Deployment)
Servicio Base: Software Development (PWAs, Mobile Apps, Desktop Applications, Microservices & Deployment)
The Trivial Question vs. The Zyliica Question
The Trivial Question vs. The Zyliica Question
The Trivial Question vs. The Zyliica Question
Client Challenge: "We need an internal management application (dashboard) accessible from mobile and web, but development times for traditional agencies are 12 months and require a budget that would put our initial capital at risk."
The Zyliica Question (Question Architecture): "How do we design a Microservices Architecture that allows us to build the Interactive Story (frontend) on an agile stack (Flutterflow/Bubble) to validate the product in record time and ensure scalability that justifies future investment?"
Client Challenge: "We need an internal management application (dashboard) accessible from mobile and web, but development times for traditional agencies are 12 months and require a budget that would put our initial capital at risk."
The Zyliica Question (Question Architecture): "How do we design a Microservices Architecture that allows us to build the Interactive Story (frontend) on an agile stack (Flutterflow/Bubble) to validate the product in record time and ensure scalability that justifies future investment?"
Client Challenge: "We need an internal management application (dashboard) accessible from mobile and web, but development times for traditional agencies are 12 months and require a budget that would put our initial capital at risk."
The Zyliica Question (Question Architecture): "How do we design a Microservices Architecture that allows us to build the Interactive Story (frontend) on an agile stack (Flutterflow/Bubble) to validate the product in record time and ensure scalability that justifies future investment?"
The Zyliica Solution (Applied Methodology)
The Zyliica Solution (Applied Methodology)
The Zyliica Solution (Applied Methodology)
Foundation: Design of Microservices & Deployment architecture, separating core business logic (data backend) from user experience (frontend).
Artistry & Precision: Simultaneous development of the application as a Progressive Web App (PWA) and Mobile App (iOS/Android) using the Low-Code stack (Flutterflow/Bubble). This allows implementing elite design and rapidly testing user experience.
Truth Engineering: Microservices architecture is used to ensure that, although the frontend is built quickly, the heart of the application (data and logic) is hosted in a robust system ready for scalability.
Foundation: Design of Microservices & Deployment architecture, separating core business logic (data backend) from user experience (frontend).
Artistry & Precision: Simultaneous development of the application as a Progressive Web App (PWA) and Mobile App (iOS/Android) using the Low-Code stack (Flutterflow/Bubble). This allows implementing elite design and rapidly testing user experience.
Truth Engineering: Microservices architecture is used to ensure that, although the frontend is built quickly, the heart of the application (data and logic) is hosted in a robust system ready for scalability.
Foundation: Design of Microservices & Deployment architecture, separating core business logic (data backend) from user experience (frontend).
Artistry & Precision: Simultaneous development of the application as a Progressive Web App (PWA) and Mobile App (iOS/Android) using the Low-Code stack (Flutterflow/Bubble). This allows implementing elite design and rapidly testing user experience.
Truth Engineering: Microservices architecture is used to ensure that, although the frontend is built quickly, the heart of the application (data and logic) is hosted in a robust system ready for scalability.
The Result (The Hidden Story)
The Result (The Hidden Story)
The Result (The Hidden Story)
Trivial Result: Launch of a functional MVP after one year.
Zyliica Result (Strategic Impact): The application was designed, built, and deployed in three months, representing a 75% reduction in development time. Using Microservices allowed the client to launch on multiple platforms (Web, iOS, Android) from a single code base, reducing operating maintenance costs by 40%. Most importantly, the application validated the business model before capital ran out, ensuring the next investment round was for a proven and scalable product.
Trivial Result: Launch of a functional MVP after one year.
Zyliica Result (Strategic Impact): The application was designed, built, and deployed in three months, representing a 75% reduction in development time. Using Microservices allowed the client to launch on multiple platforms (Web, iOS, Android) from a single code base, reducing operating maintenance costs by 40%. Most importantly, the application validated the business model before capital ran out, ensuring the next investment round was for a proven and scalable product.
Trivial Result: Launch of a functional MVP after one year.
Zyliica Result (Strategic Impact): The application was designed, built, and deployed in three months, representing a 75% reduction in development time. Using Microservices allowed the client to launch on multiple platforms (Web, iOS, Android) from a single code base, reducing operating maintenance costs by 40%. Most importantly, the application validated the business model before capital ran out, ensuring the next investment round was for a proven and scalable product.
© 2025 All Rights Reserved
We Find The Truth You Don't Know You're Missing
© 2025 All Rights Reserved
We Find The Truth You Don't Know You're Missing
© 2025 All Rights Reserved
We Find The Truth You Don't Know You're Missing