SigmaArc SPECTRA™

Turn Customer Insights into Lifecycle Execution.

Oct 28, 2025

SPECTRA™ is SigmaArc’s proprietary execution intelligence system — designed to help enterprises operationalize lifecycle value through intelligent orchestration. It turns fragmented data, latent insights, and disjointed workflows into coordinated execution across the customer journey.

This document outlines the high-level architecture of SPECTRA™ — what it is, how it works, and where it applies. Core IP mechanisms, orchestration logic, and decision flows are confidential and not disclosed here.

System Architecture: Overview

SPECTRA operates through three modular layers — each powering a stage of enterprise execution:

1. Perception Layer

The Perception layer continuously ingests and contextualizes structured and unstructured data across internal and external systems. It establishes the foundational awareness required for dynamic decision-making.

Key functions:

• Aggregate signals from CRMs, data lakes, third-party sources, and operational tools

• Normalize and transform data into a usable state for modeling and logic

• Extract patterns and surface relevant events, entities, and intents from large volumes of activity and interaction data

Key components:

• Data Ingestion Engine – Connects to platforms like Snowflake, Salesforce, and APIs

• Signal Extraction Framework – Applies NLP and event-detection models

• Unstructured Storage – Supports documents, emails, chat transcripts, knowledge stores

• Example tools: Apache Kafka, Airbyte, Elasticsearch, Hugging Face, Azure Cognitive Services

2. Cognition Layer

The Cognition layer is the intelligence core of SPECTRA. It interprets signals, applies business and contextual logic, and determines the most relevant and timely action to take.

Key functions:

• Model lifecycle context (e.g., customer stage, risk profile, product state)

• Prioritize signals based on urgency, impact, or business objectives

• Select and route decisions through configurable logic paths or LLM-assisted reasoning

Key components:

• Contextual Modeling Engine – Builds enterprise-specific knowledge graphs

• Decision Engine – Applies structured rules, logic trees, and model outputs

• Orchestration Middleware – Manages flow control, fallback logic, and state transitions

• Example tools: Neo4j, OpenRules, LangChain, Temporal, Camunda, Airflow

3. Transformation Layer

Executes prioritized actions through workflows, prompts, and system integrations.

Key components:

• Workflow Engine – Drives action sequences across teams or systems

• Interfaces & Integration Adapters – Connects to CRM, ERP, Ops tools

• Agent Execution Framework – Supports prompt-based or scripted automation

• Example tools: Workato, Zapier, Boomi, CrewAI, Rasa, Datadog

Infrastructure & Cloud Architecture

SPECTRA is natively designed for major cloud platforms — AWS, Azure, and GCP — and is deployable in cloud-native, hybrid, or on-prem configurations.

Delivery & Deployment

SPECTRA is currently offered through structured, modular engagements. Each engagement is scoped carefully based on specific lifecycle zones and enterprise context. Areas of initial focus may include:

• Onboarding acceleration

• Intelligent product cross-sell

• Risk triage and agent handoff

• Churn prediction and retention flow

While the system has been architected for adaptability and integration, SPECTRA is an evolving platform and delivery experience. As such, we approach each deployment collaboratively and with care — aligning closely with your team to tailor the design, expectations, and pace.

To explore a pilot, request a technical briefing, or discuss implementation: contact@sigmaarc.com
www.sigmaarc.com