How It Works

From Breaking News to
Investment Opportunity
in Seconds

MarketRipple operates a multi-stage AI intelligence pipeline — continuously ingesting market events, running them through analysis and classification, tracing ripple effects across the market graph, and surfacing scored opportunities — all within seconds of any market-moving event in India or globally.

9-Stage PipelineReal-time ProcessingExplainable AI

The Intelligence Pipeline

9 Steps from News to Opportunity

Every market event flows through each stage of the pipeline — producing richer, more structured intelligence at each step.

STEP 01< 15 secondsIngestion latency

Breaking News Ingestion

500+ sources. Real-time. Always on.

MarketRipple's data pipeline monitors over 500 sources across global and Indian financial media — including NSE/BSE corporate filings, RBI press releases, SEBI circulars, Ministry of Finance notifications, PIB announcements, Reuters, Bloomberg feeds, and premium Indian financial news services.

NSE/BSE corporate disclosures and exchange filings
RBI press releases and MPC meeting minutes
SEBI circulars and regulatory announcements
Global macro feeds: Fed, ECB, commodity markets
Indian financial news: ET, Mint, Business Standard

STEP 02< 8 secondsAnalysis latency

AI Analysis

LLMs extract signal from noise.

Large Language Models trained on Indian financial data analyse each ingested item. The AI extracts key entities (companies, sectors, commodities, currencies), assesses market relevance, generates a human-readable summary, and assigns a confidence score based on source credibility, event novelty, and impact precedent.

Named entity recognition for stocks, sectors, and commodities
Impact magnitude estimation (High / Medium / Low / Negligible)
Source credibility scoring and cross-reference verification
Duplicate detection and event deduplication across sources
Confidence score assignment with supporting evidence

STEP 03StructuredEvent format

Market Event Classification

Every event gets a structured identity.

The raw news item is transformed into a structured Market Event with classification metadata. MarketRipple classifies events by type, tags affected sectors and companies, estimates impact duration, and assigns an initial impact score — making every event queryable and comparable.

Event type: Monetary / Fiscal / Geopolitical / Earnings / Regulatory / Corporate
Sector tagging: primary and secondary sector effects
Company tagging: direct and indirect company exposure
Impact duration: Short-term (< 1 week) / Medium / Long-term (> 6 months)
Geographic scope: India-specific / Global with India exposure

STEP 04700+Relationship types

Ripple Engine Processing

Cause-effect chains across 700+ relationship types.

MarketRipple's proprietary Ripple Engine is the core differentiator. It traces cause-effect relationships from every market event across seven dependency types, mapping how one event creates cascading effects across the Indian market ecosystem — with confidence scores on every relationship edge.

Commodity chain dependencies (crude oil → paint, aviation, logistics)
Currency effects (INR/USD movement → IT exports, import-heavy sectors)
Sector contagion (banking stress → NBFC, real estate, consumer credit)
Policy responses (rate hike → bond yields → insurance, pension funds)
Corporate earnings impact (raw material price → margin compression)
Global capital flows (FII risk-off → mid/small cap, emerging market premium)
Market sentiment shifts (event narrative → sector rotation triggers)

STEP 05Live GraphIntelligence network

Market Dependency Graph

The invisible connections made visible.

A force-directed knowledge graph visualises how one event creates cascading effects across markets, sectors, and companies. Each node is a market entity (sector, company, commodity, currency). Each edge is a typed dependency with direction, magnitude, and confidence score — forming a queryable intelligence graph.

Nodes: companies, sectors, commodities, currencies, policy instruments
Edges: 7 dependency types with direction and magnitude
Confidence scores on every graph relationship
Historical validation against 3+ years of Indian market data
Real-time updates as new events are processed

STEP 063 layersExposure depth

Company Identification

Specific stocks. Specific reasons.

AI identifies specific BSE/NSE-listed companies affected by each event, categorised by exposure type. Direct exposure means a company's revenues or costs are immediately impacted. Indirect means sector spillover. Thematic means long-term structural change to the business model or competitive position.

Direct exposure: earnings, margins, revenue directly affected
Indirect exposure: sector spillover, input cost changes, demand shifts
Thematic exposure: long-term structural change to business model
Promoter / institutional holding context for price reaction prediction
F&O activity and derivative positioning as confirmation signals

STEP 075–15 eventsPer story

Story Synthesis

Multi-event narratives become investment themes.

The AI synthesises multiple related events into coherent investment narratives — Stories. A Story connects 5–15 related events into a chronological theme (e.g., India's EV transition, NBFC credit cycle, IT sector re-rating) with a timeline, affected companies, opportunity score, and risk assessment.

Automatic event clustering by theme and sector coherence
Timeline construction with key milestone events
Compound opportunity scoring across the event cluster
Risk factors extracted from contrarian evidence in the cluster
Time horizon estimation: Short / Medium / Long-term thesis

STEP 080–100Opportunity score

Opportunity Radar Scoring

0–100. Evidence-based. Explained.

The Opportunity Radar scoring algorithm evaluates the investment opportunity strength of every market event and story on a 0–100 scale. The score combines event impact magnitude, AI confidence, sector momentum, volume of corroborating events, and historical precedent from similar past events.

Event impact weight: primary contributor to raw score
AI confidence multiplier: penalises uncertain or low-evidence events
Sector momentum factor: aligned with or against sector trend
Corroboration bonus: score increases with multiple confirming events
Historical precedent: similar events' actual outcome backtesting

STEP 09< 3 secQuery response

AI Search Interface

Ask anything. Get sourced answers.

Users can query the entire MarketRipple intelligence graph in natural language via the AI Search interface. Queries like ‘Which IT companies are exposed to US recession risk?’ or ‘What sectors benefit if crude oil falls to $70?’ return instant, sourced answers — grounded in MarketRipple's knowledge graph, not hallucination.

Natural language query over the full intelligence graph
Company screening: 'Which stocks benefit from rupee depreciation?'
Event analysis: 'How did markets react to last RBI rate hike?'
Sector queries: 'What are the key risks to Indian IT sector in 2025?'
All answers include source events and evidence citations

Technology

What Powers the Pipeline

Three technology layers working together to deliver real-time market intelligence at institutional quality.

15-min

Intelligence refresh cycle

AI Models

Domain-adapted Large Language Models fine-tuned on Indian financial data — SEBI filings, RBI reports, earnings transcripts, and 3+ years of BSE/NSE market events. Combined with retrieval-augmented generation (RAG) to ground every output in real market data.

500+

Sources monitored

Data Pipeline

Real-time ingestion from 500+ financial data sources with deduplication, entity normalisation, and quality scoring. The pipeline handles structured data (exchange filings, regulatory databases) and unstructured text (news, press releases, social signals) in a unified processing architecture.

700+

Market relationship types

Graph Engine

A dynamic market knowledge graph with 700+ typed relationship edges covering Indian market dependencies. The graph is updated in real-time as events are processed, enabling fast graph traversal queries for ripple analysis, sector contagion mapping, and company exposure scoring.

Pipeline Summary

From Raw News to Structured Opportunity

News

AI Analysis

Classification

Ripple Engine

Graph Build

Company Tagging

Story Synthesis

Opportunity Score

AI Search Ready

Total pipeline latency: under 60 seconds from source publication to fully enriched, scored, and searchable intelligence.

Go Deeper

Understand How MarketRipple Thinks

Explore the AI reasoning models, evidence standards, and confidence calibration that power every output on MarketRipple.

How MarketRipple Thinks See Live Events

From Breaking News to
Investment Opportunity
in Seconds

9-Stage PipelineReal-time ProcessingExplainable AI

9 Steps from News to Opportunity

Every market event flows through each stage of the pipeline — producing richer, more structured intelligence at each step.

STEP 01< 15 secondsIngestion latency

Breaking News Ingestion

500+ sources. Real-time. Always on.

NSE/BSE corporate disclosures and exchange filings
RBI press releases and MPC meeting minutes
SEBI circulars and regulatory announcements
Global macro feeds: Fed, ECB, commodity markets
Indian financial news: ET, Mint, Business Standard

STEP 02< 8 secondsAnalysis latency

AI Analysis

LLMs extract signal from noise.

Named entity recognition for stocks, sectors, and commodities
Impact magnitude estimation (High / Medium / Low / Negligible)
Source credibility scoring and cross-reference verification
Duplicate detection and event deduplication across sources
Confidence score assignment with supporting evidence

STEP 03StructuredEvent format

Market Event Classification

Every event gets a structured identity.

Event type: Monetary / Fiscal / Geopolitical / Earnings / Regulatory / Corporate
Sector tagging: primary and secondary sector effects
Company tagging: direct and indirect company exposure
Impact duration: Short-term (< 1 week) / Medium / Long-term (> 6 months)
Geographic scope: India-specific / Global with India exposure

STEP 04700+Relationship types

Ripple Engine Processing

Cause-effect chains across 700+ relationship types.

Commodity chain dependencies (crude oil → paint, aviation, logistics)
Currency effects (INR/USD movement → IT exports, import-heavy sectors)
Sector contagion (banking stress → NBFC, real estate, consumer credit)
Policy responses (rate hike → bond yields → insurance, pension funds)
Corporate earnings impact (raw material price → margin compression)
Global capital flows (FII risk-off → mid/small cap, emerging market premium)
Market sentiment shifts (event narrative → sector rotation triggers)

STEP 05Live GraphIntelligence network

Market Dependency Graph

The invisible connections made visible.

Nodes: companies, sectors, commodities, currencies, policy instruments
Edges: 7 dependency types with direction and magnitude
Confidence scores on every graph relationship
Historical validation against 3+ years of Indian market data
Real-time updates as new events are processed

STEP 063 layersExposure depth

Company Identification

Specific stocks. Specific reasons.

Direct exposure: earnings, margins, revenue directly affected
Indirect exposure: sector spillover, input cost changes, demand shifts
Thematic exposure: long-term structural change to business model
Promoter / institutional holding context for price reaction prediction
F&O activity and derivative positioning as confirmation signals

STEP 075–15 eventsPer story

Story Synthesis

Multi-event narratives become investment themes.

Automatic event clustering by theme and sector coherence
Timeline construction with key milestone events
Compound opportunity scoring across the event cluster
Risk factors extracted from contrarian evidence in the cluster
Time horizon estimation: Short / Medium / Long-term thesis

STEP 080–100Opportunity score

Opportunity Radar Scoring

0–100. Evidence-based. Explained.

Event impact weight: primary contributor to raw score
AI confidence multiplier: penalises uncertain or low-evidence events
Sector momentum factor: aligned with or against sector trend
Corroboration bonus: score increases with multiple confirming events
Historical precedent: similar events' actual outcome backtesting

STEP 09< 3 secQuery response

AI Search Interface

Ask anything. Get sourced answers.

Natural language query over the full intelligence graph
Company screening: 'Which stocks benefit from rupee depreciation?'
Event analysis: 'How did markets react to last RBI rate hike?'
Sector queries: 'What are the key risks to Indian IT sector in 2025?'
All answers include source events and evidence citations

What Powers the Pipeline

Three technology layers working together to deliver real-time market intelligence at institutional quality.

15-min

Intelligence refresh cycle

AI Models

500+

Sources monitored

Data Pipeline

700+

Market relationship types

Watchlist

From Breaking News toInvestment Opportunityin Seconds

9 Steps from News to Opportunity

Breaking News Ingestion

AI Analysis

Market Event Classification

Ripple Engine Processing

Market Dependency Graph

Company Identification

Story Synthesis

Opportunity Radar Scoring

AI Search Interface

What Powers the Pipeline

AI Models

Data Pipeline

Graph Engine

From Raw News to Structured Opportunity

Understand How MarketRipple Thinks

From Breaking News toInvestment Opportunityin Seconds

9 Steps from News to Opportunity

Breaking News Ingestion

AI Analysis

Market Event Classification

Ripple Engine Processing

Market Dependency Graph

Company Identification

Story Synthesis

Opportunity Radar Scoring

AI Search Interface

What Powers the Pipeline

AI Models

Data Pipeline

Graph Engine

From Raw News to Structured Opportunity

Understand How MarketRipple Thinks

From Breaking News to
Investment Opportunity
in Seconds

From Breaking News to
Investment Opportunity
in Seconds