FalconDive is an AI-native BI platform built for scalable, real-time decision-making.
At its core, FalconAI powers every layer through intelligent agents that validate, transform, analyze, and explain data—ensuring seamless flow from ingestion to insight.
This layer enables seamless integration with structured, semi-structured, and unstructured data sources. It supports batch ingestion, real-time streams, and third-party connectors through APIs. Its primary responsibility is to reliably and flexibly capture all relevant data across systems.
At this junction, FalconAI activates the Data Governance Agent to evaluate incoming data for schema adherence, structural consistency, and semantic alignment—ensuring that data quality and compatibility checks are conducted before it enters the processing pipeline.
This layer handles raw data storage and scalable transformation using AWS-native tools:
AWS S3 Data Lake stores ingested data securely and flexibly in its raw format.
AWS Glue handles ETL transformations including data cleaning, mapping, and enrichment.
Amazon Redshift (Transient) enables high-performance temporary transformations for analytical readiness.
FalconAI operates extensively within this layer. The Data Governance Agent continues to monitor and enforce standards, validating that transformed datasets maintain consistency with historical structures, business logic, and reporting requirements.
In parallel, FalconAI’s transformation monitoring agents identify anomalies such as:
Unexpected nulls or surges in volume,
Breakdowns in scheduled jobs or schema drift,
Skewed distributions across key segments.
The system can trigger intelligent alerts and generate automated recommendations for remediation—whether that’s a schema adjustment, ETL logic improvement, or operational alert to data engineering teams.
The Value Layer is where FalconAI delivers its core intelligence—transforming prepared data into decisions, forecasts, and system-wide optimization.
Amazon Athena enables serverless querying of curated data directly from S3.
Amazon SageMaker powers machine learning models for predictive analytics such as churn risk, lifetime value forecasting, and bonus optimization.
FalconAI agents in this layer:
Detect real-time anomalies in performance KPIs,
Surface the root causes behind metric movements,
Dynamically recalculate segments based on behavior and engagement,
Recommend next-best-actions through prescriptive logic.
The Data Governance Agent also extends into this layer, ensuring that outputs from modeling and insights remain consistent with expected business definitions and that operational decisioning is grounded in validated data.
In addition, FalconAI operates across the connection between this layer and the next—performing final checks and optimization before insights are pushed into decision workflows, dashboards, or downstream systems.
The final layer focuses on delivering actionable insights and supporting operational decisions through visualization tools. Key features include:
Insights and Reports: Processed and analyzed data is presented in the form of dashboards and reports, enabling stakeholders to track key metrics and performance indicators.
Visualization Tools: Visualization tools like Power BI, Tableau, or Amazon QuickSight can be integrated for detailed and interactive reporting.
Operational Systems and Processes: Data is made accessible to downstream operational systems and processes, supporting workflows such as CLCM, customer segmentation, bonus optimization, and financial reporting and many more.
In addition to traditional BI capabilities, FalconDive integrates agentic AI features that elevate how users interact with data:
FalconAI Chat: A dedicated agent within the FalconAI system, this chat allows users to query data using natural language and receive contextual, actionable answers. It supports use cases such as explaining metric movements, retrieving top-level KPIs, or identifying outliers.
FalconOmnis: The orchestration layer within FalconAI that enables agents to work together in real time—monitoring trends, detecting anomalies, and surfacing explanations. It facilitates coordinated agent-to-agent interaction that mirrors a real analytical team operating across your data.
This integration of conversational AI and agentic collaboration ensures that business users receive not just data, but answers, context, and proactive guidance—when and where they need it most.
FalconAI agents also proactively generate alerts and notifications based on business-defined thresholds, AI-detected risks, or unexpected shifts in key performance metrics. These alerts are surfaced contextually within dashboards or delivered to relevant stakeholders via integrated channels such as email, Slack, or in-app messages.
Each day, FalconOmnis monitors anomalies detected across all datasets, maintaining a live view of what changed, why it changed, and how it was addressed. Business users can view a summary of unresolved anomalies from the previous day, alongside agent-generated explanations and recommended next steps—enabling quick triage, decision-making, or escalation where necessary.
This architecture provides several critical advantages:
The S3 Data Lake and AWS Glue allow organizations to handle growing data volumes with ease.
Support for diverse data sources, including relational, streaming, and cloud-based platforms.
S3’s pay-as-you-go model and transient Redshift usage minimize operational costs.
Tools like Kafka and Athena enable real-time data ingestion and analysis.
SageMaker and Redshift provide capabilities for predictive modeling and advanced data science applications.
CloudWatch ensures continuous monitoring, logging, and error resolution.
Built for Scale, Designed for Simplicity.
Drawing from 100+ years of collective iGaming expertise, our journey mirrors industry evolution. Falcon Dive streamlines time-to-market, ensuring cost-efficiency.
© 2024 All rights reserved Falcondive.
