Current Member of NVIDIA Inception Program
Accelerating innovation in Digital Twin technology with NVIDIA's cutting-edge AI and GPU infrastructure
Incubated by NASSCOM CoE - IoT & AI
Selected as an early-stage startup by NASSCOM Center of Excellence for IoT & AI, driving innovation in Digital Twin technology across India
FROM STATIC DIGITAL TWINS TO AI-DRIVEN CAUSAL DECISION SYSTEMS
Tattva Twins goes beyond simulation — we build AI-driven Causal Decision Systems that understand why things happen, predict outcomes, and autonomously prescribe actions to keep your operations ahead of failure.
Why Traditional Digital Twins Fall Short
Most digital twins and predictive systems detect what is happening, but cannot explain why—or autonomously decide what to do about it. This intelligence gap costs enterprises billions in preventable failures.
Correlation Without Causation
Most digital twins detect patterns and anomalies but cannot explain why things happen. They alert you to problems without revealing root causes, leaving your team guessing at solutions.
No Causal Reasoning
Traditional models lack the intelligence to understand cause-and-effect relationships. They cannot predict how interventions will impact outcomes or prescribe optimal actions to prevent failures.
Reactive, Not Prescriptive
Static dashboards and alerts force humans to interpret data and decide what to do. Without autonomous prescriptive intelligence, opportunities to prevent failures are missed and decision latency costs millions.
CAUSAL DIGITAL TWINS AS A SERVICE (DTaaS)
Tattva Twins delivers an AI-driven Causal Digital Twin-as-a-Service platform that goes beyond simulation to understand why things happen, predict outcomes, and autonomously prescribe optimal actions—keeping you ahead of failure.
Causal-Aware Digital Replicas
AI-PoweredAI-driven digital twins that understand not just what is happening, but why—modeling cause-and-effect relationships to predict outcomes and prescribe optimal actions.
Intelligent Data Fusion
Causal EngineAdvanced AI algorithms process live IoT streams to identify causal patterns, enabling proactive decision-making that prevents failures before they occur.
Autonomous Decision Engine
PrescriptiveEnterprise-grade AI that doesn't just simulate scenarios—it autonomously prescribes optimal actions, continuously learning and adapting to maximize performance.
Causal Digital Thread
IntelligentSeamless AI-driven communication that maps causal relationships across your entire operation, turning fragmented data into unified, actionable intelligence.
How It Works
A four-stage causal intelligence pipeline that transforms raw operational data into autonomous, explainable decisions—deployed in weeks, not months.
Ingest & Unify Data
Connect IoT sensors, SCADA systems, ERP, and operational data sources into a unified causal data fabric. Our vendor-neutral connectors normalize real-time and historical streams into a structured causal graph.
Build Causal Knowledge Graph
Our AI engine automatically constructs a causal knowledge graph of your physical assets—mapping cause-and-effect relationships between sensors, processes, and outcomes, not just correlations.
Reason, Predict & Explain
The causal AI engine runs continuous what-if simulations, predicts failure modes before they occur, and explains the root cause behind every anomaly—so your team always knows why, not just what.
Autonomously Prescribe & Act
Go beyond alerts. The system autonomously prescribes optimal interventions—maintenance schedules, process adjustments, resource allocation—and triggers actions via APIs or operator-approved workflows.
Step 01
Ingest & Unify Data
Connect IoT sensors, SCADA systems, ERP, and operational data sources into a unified causal data fabric. Our vendor-neutral connectors normalize real-time and historical streams into a structured causal graph.
Beyond Digital Twins: Causal Intelligence
Traditional digital twins show you what is happening. Tattva Twins tells you why—and what to do about it.
Causal AI, Not Just Correlation
While others detect patterns, we model cause-and-effect relationships. Understand why failures happen, not just when—enabling true root cause analysis and preventive action.
Autonomous Prescriptive Decisions
Go beyond alerts and dashboards. Our AI autonomously prescribes optimal interventions—maintenance schedules, process adjustments, resource allocation—and can trigger actions automatically.
Explainable AI by Design
Every prediction and recommendation comes with a clear causal explanation. Your team knows exactly why the system recommends an action, building trust and enabling informed decision-making.
Causal Knowledge Graph Foundation
Built on a dynamic causal knowledge graph that continuously learns and updates relationships between assets, processes, and outcomes—getting smarter as your operations evolve.
Causal Intelligence, Measurable Impact
Real outcomes delivered by our causal AI engine — preventing failures, explaining anomalies, and autonomously prescribing optimal decisions across industrial operations.
Root Cause Accuracy
Causal AI correctly identifies the true root cause of failures and anomalies
Failures Prevented
Of predicted failure events autonomously avoided before they occur
Reduction in Downtime
Average improvement delivered through causal predictive maintenance
Faster Decision-Making
Autonomous prescriptive actions replace hours of manual analysis
Energy Optimization
Causal interventions reduce energy waste across industrial facilities
Causal AI Market by 2030
Global TAM for AI-driven industrial intelligence (Grand View Research)
Why Now is the Perfect Time
The convergence of real-time data infrastructure, causal AI breakthroughs, and outcome-focused enterprise buyers creates the ideal conditions for Causal Digital Twins.
Real-Time Causal Data Fabric
5G and edge computing enable continuous, high-frequency data streams. Our causal AI engine transforms this raw data into a living knowledge graph—not just periodic replicas, but a reasoning system.
Causal AI Breakthrough
Recent advances in causal inference and large language models now enable true cause-and-effect modeling. We move beyond correlation to understand why things happen—and prescribe optimal actions.
Outcome-Focused Buyers
Ops, ESG, and risk leaders now fund solutions based on measurable outcomes: prevented failures, optimized energy, reduced downtime. They need prescriptive intelligence, not just dashboards.
Intelligent SaaS Era
Enterprises now expect AI-driven, continuously learning software that autonomously improves operations. The shift from passive monitoring to active prescription is the new standard.
Causal AI Across Industries
Strategic expansion across high-value sectors where understanding why things happen—and autonomously prescribing actions—delivers transformative outcomes.
Initial Market Entry
Real Estate
Causal AI understands why HVAC systems fail, autonomously prescribes energy interventions, and prevents equipment downtime before tenants notice.
Hospitality
Root cause analysis of guest complaints, predictive maintenance that prevents service disruptions, and autonomous resource allocation during peak demand.
Manufacturing
Causal models identify which upstream process variations cause downstream quality defects—enabling prescriptive adjustments that prevent scrap and rework.
Industrial Expansion
Heritage
Causal understanding of environmental factors affecting artifact preservation, with autonomous climate control prescriptions that prevent degradation.
Data Centers
Causal AI maps cooling inefficiencies to specific workload patterns, autonomously prescribes workload redistribution to prevent thermal failures.
Energy
Causal models explain why demand spikes occur, predict grid stress before it happens, and autonomously prescribe load balancing interventions.
Broader Applications
Smart Cities
Causal understanding of traffic congestion root causes, autonomous signal timing prescriptions, and predictive infrastructure maintenance that prevents failures.
Utilities
Causal models identify why water pressure drops occur, predict pipe failures before they burst, and autonomously prescribe network reconfigurations.
Aerospace
Causal analysis of flight data to understand why components degrade, with autonomous maintenance prescriptions that prevent in-flight failures.
Invest in Causal Intelligence
From a single-asset causal AI pilot to an enterprise-wide autonomous decision platform — scale your causal intelligence as your operations grow.
Starter
Causal AI Pilot
Validate causal AI on a single asset — prove ROI before scaling across your operations.
- 1 causal digital twin (single asset)
- Real-time IoT data ingestion
- Causal knowledge graph (single asset)
- Predictive failure alerts with root cause
- Monitoring & visualization dashboards
- Monthly causal model refresh
- Standard onboarding (2–4 weeks)
- Email support
Professional
Causal Intelligence at Scale
Deploy causal AI across facilities, factories, and multi-asset portfolios (5–15 assets).
- Multi-asset causal digital twins
- Cross-asset causal knowledge graph
- Root cause analysis across processes
- Prescriptive AI recommendations
- What-if causal scenario simulation
- Role-based explainability dashboards
- Quarterly causal model retraining
- SLA-backed support
Enterprise
Autonomous Decision Platform
Full-scale autonomous causal decision system for large enterprises and mission-critical infrastructure.
- Unlimited assets & causal models
- Enterprise-wide causal knowledge graph
- Autonomous prescriptive action engine
- API-driven action triggers & workflows
- Explainable AI audit trails
- ERP / SCADA / MES deep integration
- Multi-tenant secure deployment
- Dedicated Customer Success Manager
- 99.9%+ uptime SLA
- On-prem / hybrid deployment options
Optional Add-Ons
"We start with a causal AI pilot on one asset, prove measurable ROI, then expand into an enterprise-wide autonomous decision platform — turning every asset into an intelligent, self-managing system."
Building the Future of Causal AI
Visionary leaders combining enterprise technology expertise with a mission to transform industrial operations through autonomous causal intelligence.
Board of Advisors
World-class industry leaders, scientists, and investors guiding Tattva Twins' mission to deliver causal AI intelligence at industrial scale.
Seasoned enterprise innovation leader and founder of Ideapoke, with deep expertise in open innovation, technology scouting, and building data-driven platforms that connect global ecosystems for accelerated R&D and product development.
Entrepreneur and finance professional with strong grounding in investment strategy, startup ecosystems, and capital markets, backed by academic rigor from Jamnalal Bajaj Institute of Management Studies and hands-on experience in building and scaling financial platforms.
Management professional with a strong foundation in business strategy and analytics, complemented by hands-on experience in manufacturing environments, driving operational efficiency and data-led decision-making.
Interested in joining our advisory network? Get in touch →
Orchestrating Causal Intelligence Across Every Layer
Orchestrating Causal Digital Twins across shopfloor systems, MES, and cloud ecosystems to enable real-time industrial decision intelligence.
Industrial & IoT Hardware
IoT Sensors & Edge Devices
Manufacturing Execution Systems
Cloud & Industrial IoT Platforms
Digital Twin & Simulation
Data & AI Platforms
Enterprise Systems & Integration
Integrated with the world's leading industrial platforms — out of the box, with 200+ protocols supported.
Causal AI Insights
Explore the science of cause-and-effect, autonomous decision systems, and how Causal Digital Twins are transforming industrial operations.
From Reactive to Prescriptive: How Causal AI Transforms Manufacturing
Discover how leading manufacturers are deploying Causal Digital Twins that don't just monitor equipment—they understand why failures happen, predict outcomes, and autonomously prescribe optimal interventions to prevent downtime before it occurs.
Causal Digital Twins vs Static Digital Twins
Static digital twins monitor. Causal digital twins simulate and reason. Explore use cases, real-world examples, and the future of industrial AI.
Building Causal Knowledge Graphs for Industrial Assets
Learn how our AI engine automatically maps cause-and-effect relationships between sensors, processes, and outcomes—turning raw data into actionable intelligence.
From Predictive to Prescriptive: A Causal AI Success Story
How a leading manufacturer reduced downtime by 40% using Causal Digital Twins that not only predict failures but autonomously prescribe optimal maintenance interventions.
Explainable AI: Why Trust Requires Understanding
Explore how causal reasoning makes AI decisions transparent and auditable—every prediction comes with a clear explanation of the underlying cause-and-effect chain.
Autonomous Decision Systems in Industrial Operations
See how prescriptive AI moves beyond alerts to autonomously trigger actions—reducing decision latency from hours to seconds and preventing failures before they occur.
Platform Update: Explainability Dashboard & Causal Insights
Introducing our enhanced explainability dashboard—now every AI prediction includes a visual causal chain showing exactly why the system recommends each action.
Root Cause Quality Control with Causal AI
How causal reasoning identifies which upstream process variations actually cause downstream quality defects—enabling prescriptive adjustments that prevent scrap and rework.
Master Causal AI
Get exclusive insights on causal inference, autonomous decision systems, and prescriptive AI delivered to your inbox.
The Tattva Twins Podcast
Dive deep into Digital Twin technology, industry insights, and expert conversations. Subscribe and stay ahead of the curve.
Causal AI Questions
Find quick answers about how our Causal Digital Twin platform works, from causal knowledge graphs to autonomous prescriptive actions.
Contact SupportA Causal Digital Twin goes far beyond mirroring physical assets. While traditional digital twins show you what is happening, our Causal AI engine understands why things happen—modeling cause-and-effect relationships between sensors, processes, and outcomes. This enables true root cause analysis, explainable predictions, and autonomous prescriptive actions that prevent failures before they occur.
Our platform builds a dynamic causal knowledge graph that maps how changes in one part of your system affect others. The AI engine continuously learns these relationships from your operational data, runs what-if simulations to predict outcomes, and explains the reasoning behind every prediction. When it detects an anomaly, it doesn't just alert you—it tells you the root cause and prescribes the optimal intervention.
With Tattva Twins, deployment typically takes 2–4 weeks for a single asset pilot, compared to 6–12 months with traditional solutions. Our automated causal discovery algorithms, pre-built industry templates, and no-code configuration tools dramatically accelerate time-to-value. The causal knowledge graph auto-generates from your data, requiring minimal manual configuration.
We support a wide range of industrial assets where understanding causality delivers maximum value: manufacturing equipment, HVAC systems, data centers, energy infrastructure, supply chain logistics, and smart buildings. Our causal AI is industry-agnostic and has been deployed across manufacturing, energy, logistics, healthcare facilities, and commercial real estate—anywhere complex systems need explainable, autonomous decision-making.
No. Tattva Twins is designed to integrate with your existing infrastructure. We support 200+ industrial protocols and can connect to your current sensors, PLCs, SCADA systems, and enterprise software (ERP, CMMS, etc.). Our causal AI layer sits on top of your existing investments, transforming raw data into actionable intelligence without requiring hardware changes.
We offer flexible subscription-based pricing that scales with your causal intelligence needs. Our Starter plan (₹4–7L/year per asset) lets you validate causal AI on a single asset. Professional plans (₹18–30L/year) deploy causal intelligence across multi-asset portfolios. Enterprise solutions (₹75L–2Cr+/year) unlock full autonomous decision capabilities. Paid POCs (₹3–6L) are 100% adjustable against subscriptions.
Our customers typically see 25–40% reduction in unplanned downtime through causal predictive maintenance, 15–25% energy savings via prescriptive optimization, and 20–30% improvement in asset lifespan. The key differentiator: our causal AI prevents failures rather than just predicting them, and autonomously prescribes optimal actions—delivering measurable ROI within 6–12 months.
Absolutely. We implement enterprise-grade security including end-to-end encryption, SOC 2 Type II compliance, role-based access controls, and regular security audits. For sensitive deployments, we offer on-premise and hybrid cloud options. Your causal knowledge graphs and operational data remain yours—we never share or monetize customer data, and our explainable AI provides full audit trails of every decision.
Three fundamental differentiators: First, our Causal AI engine goes beyond correlation to model true cause-and-effect, enabling root cause analysis and explainable predictions. Second, we deliver autonomous prescriptive actions—not just alerts—triggering interventions via APIs or workflows. Third, our rapid deployment model delivers a living, reasoning causal intelligence system in weeks, not months or years.