SpendSignal Architecture
SpendSignal's backend has an AI-native BI platform built for scalable, real-time decision-making. At its core, SpendSignal AI powers every layer through intelligent agents that validate, transform, analyze, and explain data, ensuring seamless flow from ingestion to insight.
Data Sources
AWS S3 Data Lake
Transformation Layer
Value Layer
Analytics
Source Data Layer
Seamless integration with structured, semi-structured, and unstructured data sources. Supports batch ingestion, real-time streams, and API connectors.
SpendSignal AI Integration
At this junction, SpendSignal AI 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.
Storage & Ingestion
Scalable transformation and raw storage using AWS-native tools like S3, Glue, and Redshift.
AWS S3 Data Lake
Secure raw format storage
AWS Glue
ETL transformations & cleaning
Amazon Redshift
High-performance analytics
Anomaly Detection & Governance
SpendSignal AI monitors for:
- Unexpected nulls or surges in volume
- Breakdowns in scheduled jobs or schema drift
- Skewed distributions across key segments
Value Layer
Where core intelligence is delivered—transforming prepared data into decisions, forecasts, and optimization.
Intelligent Optimization
Powered by Amazon Athena and SageMaker, SpendSignal AI agents in this layer:
- Detect real-time anomalies in performance KPIs
- Surface root causes behind movements
- Dynamically recalculate segments
- Recommend next-best-actions
Agentic AI Capabilities
Elevating how users interact with data beyond traditional BI.
SpendSignal AI Chat
A dedicated agent that allows users to query data using natural language. It supports use cases such as explaining metric movements, retrieving top-level KPIs, or identifying outliers contextually.
SpendSignal Omnis
The orchestration layer enabling agents to work together in real time. It facilitates coordinated agent-to-agent interaction that mirrors a real analytical team operating across your data.
BI Query Processing Workflow
A modern, AI-powered approach transforming natural language into actionable insights without SQL knowledge.
The Challenge
Traditional BI requires SQL knowledge or reliance on technical teams, creating bottlenecks and delaying decisions.
The Solution
An AI-orchestrated workflow using LangChain, LLMs, and intelligent executors to democratize data access.
The Result
70% reduction in time-to-insight and 3x increase in data-driven decisions across the organization.