Pre-Funnel Intelligence
The sensory cortex of the Sentient Enterprise
Nimbus treats the collective conversation with AI as a real-time market simulation. Capture market intelligence at massive scale, verify it with dual analysis, and produce trusted insights that drive decisions and governed action.
Market Signal Analysis
Capture what your market is asking and believing across thousands of AI interactions - before it appears in surveys, search, or sales data.
Early Market Signals
Detect emerging themes, unmet needs, and competitive shifts from AI query patterns - minutes after the market moves, not months later.
Dual Analysis Core
Verify truth and uncertainty with parallel content + behavioral forensics - RAG fact-checking, consistency, bias, drift, authority, and knowledge gaps.
Real-Time Market Simulation
Treat aggregate AI conversations as a continuous simulation - track sentiment, volatility, and scenarios with feedback loops into prompt planning.
Five-Phase Architecture
The Five Phases of Perception
An intelligent pipeline - Context → Ingestion → Dual Analysis → Insight synthesis → Activation & feedback - built for auditability, governance, and continuous learning.
Context & Dynamic Prompting
Intelligently generates thousands of market-relevant queries using your business context
Data Ingestion & Orchestration
Captures and orchestrates the flow of raw intelligence through the system
The Dual Analysis Core
Parallel processing of content meaning and behavioral patterns
Insight Storage & Analytics
Structured intelligence storage optimized for real-time analytics
Activation & Continuous Feedback
Intelligent agents that act on validated signals and learn from outcomes
Context & Dynamic Prompting
Intelligently generates thousands of market-relevant queries using your business context
Truth Verification
Dual analysis, not blind trust
Nimbus does not blindly trust LLM outputs. Every signal is validated via content forensics and behavioral forensics - producing trustworthiness scores, hallucination flags, and routing high-impact signals to humans-in-the-loop before activation.
What is being said
Extracts and structures semantic meaning from individual AI responses
Named Entity Recognition
Identifies key business entities: products, brands, features, and people
Aspect-Based Sentiment Analysis
Assigns specific sentiment to each identified entity or aspect
Theme Extraction
Discovers emergent themes using unsupervised topic modeling
RAG Fact-Checking
Validates claims against internal knowledge base to detect hallucinations
How it is being said
Analyzes patterns across thousands of responses to derive behavioral metrics
Consistency Scoring
Measures market consensus vs. fragmented narratives
Authority Scoring
Rewards specificity and quantifiable data over uncertain language
Narrative Bias Detection
Quantifies sentiment bias between entities in competitive contexts
Knowledge Gap Identification
Systematically identifies true knowledge gaps for strategic opportunities
Insight-Driven Workflows
From insight to action
Market intelligence powers competitive analysis, regulatory foresight, supply chain detection, and demand forecasting - triggering governed actions in Jira, Slack, and Salesforce with human oversight when impact is high.
Competitive Intelligence Monitoring
Detect competitor moves and narrative shifts before they impact pipeline
Continuous market monitoring surfaces prioritized insights with confidence scores and clear decision trails
Earlier threat detection, faster enablement, and clearer competitive positioning
Supply Chain Signal Detection
Surface supplier, region, and material risks before disruption hits production
Early market signals correlated with supplier data and procurement records
Faster mitigation and more resilient sourcing decisions
Regulatory Foresight
Track emerging regulation and compliance risk across markets and regions
Truth verification (RAG + behavioral forensics) flags uncertainty and contradictions early
Faster policy readiness and fewer last-minute compliance surprises