Nimbus

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.

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01 / 04

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.

Market Signal Analysis
01 / 05

Context & Dynamic Prompting

Graph RAG + LLM Orchestration
Prompts Generated2,847
ready
02 / 05

Data Ingestion & Orchestration

Dagster + Cloud Storage
Responses Captured156K
ready
03 / 05

The Dual Analysis Core

NLP + Behavioral Analysis
Analysis Accuracy91%
ready
04 / 05

Signal Storage & Transformation

Enterprise Data Warehouse
Signal Records2.4M
ready
05 / 05

Activation & Continuous Feedback

Agentic Framework + RL
Actions Triggered1,247
ready
Phase 01 Details
Context & Dynamic Prompting

Intelligently generates thousands of market-relevant queries using business context

Tech Stack: Graph RAG + LLM Orchestration
Live Metrics
Prompts Generated2,847
Context Accuracy94%
Query Success Rate89%
02 / 04

Early Market Signals

Detect emerging themes, unmet needs, and competitive shifts from AI query patterns - minutes after the market moves, not months later.

Early Market Signals
Current Signal
Click "Capture Signal" to begin
Analyze market intelligence in real-time
Ready
Signal History
00
No Signals Processed
History will appear here...
Archive
03 / 04

Dual Analysis Core

Verify truth and uncertainty with parallel content + behavioral forensics - RAG fact-checking, consistency, bias, drift, authority, and knowledge gaps.

Dual Analysis Core
Sample Response
QuantumScape’s solid-state battery pilot production is expected to start in 2024, with energy density claims above 1000 Wh/kg. Some sources suggest a Tesla partnership could accelerate commercialization, but timelines vary across models and independent verification is limited. Compared to Toyota’s stated roadmap, QuantumScape appears closer to pilot scale - though key manufacturing milestones remain uncertain.
Ready
Content Steps
04
Named Entity Recognition
0.3s
Aspect-Based Sentiment
0.8s
Theme Extraction
1.2s
RAG Fact-Checking
0.9s
Pipeline
Analysis Results
Entities
QuantumScape...company
solid-state ...technolo
pilot produc...timeline
Themes
Solid-State Battery Timeline92%
EV OEM Partnerships84%
Results
Content Analysis Detail
Sentiment Analysis
QuantumScape timeline0.61
Partnership speculation0.48
Toyota comparison0.52
Fact Verification
QuantumScape solid-state focusverified
Pilot production starts 2024disputed
Tesla partnership claimuncertain
04 / 04

Real-Time Market Simulation

Treat aggregate AI conversations as a continuous simulation - track sentiment, volatility, and scenarios with feedback loops into prompt planning.

Real-Time Market Simulation
Live Metrics
156.8K
Conversations
8.2K
Participants
2.8K
Signals
89%
Accuracy
Market Sentiment67%
Volatility Index43%
Live
Market Scenarios
03
EV Battery Demand Surge
Simulating market response to breakthrough in solid-state ba...
2.8K
Parts
342
Signals
78%
Sentiment
AI Regulation Impact
Market dynamics around new AI governance frameworks...
1.9K
Parts
156
Signals
45%
Sentiment
Supply Chain Disruption
Semiconductor shortage ripple effects across industries...
3.4K
Parts
289
Signals
32%
Sentiment
Scenarios
Scenario 01 Analysis
EV Battery Demand Surge

Simulating market response to breakthrough in solid-state battery technology

Volatility: medium
Key Insights
QuantumScape mentions up 340%
Tesla partnership speculation rising
Traditional automaker concern detected

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.

Architecture Overview
Phase 01
Active

Context & Dynamic Prompting

Intelligently generates thousands of market-relevant queries using your business context

Technology Stack
Graph RAG + LLM Orchestration
Phase 02
Ready

Data Ingestion & Orchestration

Captures and orchestrates the flow of raw intelligence through the system

Technology Stack
Dagster + Cloud Storage
Phase 03
Ready

The Dual Analysis Core

Parallel processing of content meaning and behavioral patterns

Technology Stack
NLP + Behavioral Analysis
Phase 04
Ready

Insight Storage & Analytics

Structured intelligence storage optimized for real-time analytics

Technology Stack
Enterprise Data Warehouse
Phase 05
Ready

Activation & Continuous Feedback

Intelligent agents that act on validated signals and learn from outcomes

Technology Stack
Agentic Framework + RL
Current Phase 01 Focus

Context & Dynamic Prompting

Intelligently generates thousands of market-relevant queries using your business context

Implementation Details
Technology StackGraph RAG + LLM Orchestration
Current StatusActive

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.

Content Analysis Pipeline

What is being said

Extracts and structures semantic meaning from individual AI responses

Processing Techniques
Tech 01

Named Entity Recognition

Identifies key business entities: products, brands, features, and people

Tech 02

Aspect-Based Sentiment Analysis

Assigns specific sentiment to each identified entity or aspect

Tech 03

Theme Extraction

Discovers emergent themes using unsupervised topic modeling

Tech 04

RAG Fact-Checking

Validates claims against internal knowledge base to detect hallucinations

Live Metrics
Entities Extracted
847.0K
Business Entities
Sentiment Accuracy
94%
Analysis Precision
Themes Identified
2.3K
Market Themes
Facts Verified
156.0K
Claims Validated
Sentiment Distribution
Positive 67%
Neutral 28%
Negative 5%
Behavioral Analysis Pipeline

How it is being said

Analyzes patterns across thousands of responses to derive behavioral metrics

Processing Techniques
Tech 01

Consistency Scoring

Measures market consensus vs. fragmented narratives

Tech 02

Authority Scoring

Rewards specificity and quantifiable data over uncertain language

Tech 03

Narrative Bias Detection

Quantifies sentiment bias between entities in competitive contexts

Tech 04

Knowledge Gap Identification

Systematically identifies true knowledge gaps for strategic opportunities

Live Metrics
Consensus Score
87%
Market Agreement
Authority Rating
92%
Data Specificity
Bias Detected
23
Narrative Patterns
Knowledge Gaps
156
Strategic Opportunities
Behavioral Insights
Market Consensus
Strong Consensus45%
Moderate Consensus38%
Weak Consensus17%
Narrative Bias
Positive Bias34%
Neutral Bias52%
Negative Bias14%

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.

Intelligence in Action
Use Cases
Case 01
Enterprise

Competitive Intelligence Monitoring

Challenge

Detect competitor moves and narrative shifts before they impact pipeline

Solution

Continuous market monitoring surfaces prioritized insights with confidence scores and clear escalation paths

Outcome

Earlier threat detection, faster enablement, and clearer competitive positioning

Key Results
Threat Detection Speed10x Faster
Market Coverage95%
Signal Trust92%
Case 02
Manufacturing

Supply Chain Signal Detection

Challenge

Surface supplier, region, and material risks before disruption hits production

Solution

Early market signals correlated with supplier data, BOM data, and procurement history

Outcome

Faster mitigation and more resilient sourcing decisions

Key Results
Detection Lead Time+3–6 weeks
False Positives-38%
Coverage90%+
Case 03
Financial Services

Regulatory Foresight

Challenge

Track emerging regulation and compliance risk across markets and regions

Solution

Truth verification (RAG + behavioral forensics) flags uncertainty and contradictions early

Outcome

Faster policy readiness and fewer last-minute compliance surprises

Key Results
Response Time<1 day
Signal Trust90%+
Risk Prevention78%
Case 01 Impact Visualization
Before Implementation
Baseline Performance
Limited market visibility
After Implementation
Enhanced Performance
Comprehensive intelligence
Implementation Timeline
Planning
Week 1-2
Results
Week 3-8
Week 9-12
Week 9-12