INDEX
Understanding the Problem (Slides 3-7)
Slide 3: minto.ai™ – Uncovering the Real-Time Truth
Slide 4: spiderAI™ – Operational Intelligence Platform
Slide 5: The Context Gap costing 15-25% of operating expenses
Slide 6: Real losses in your plant today
Slide 7: Why current solutions fail
The spiderAI Solution (Slides 8-10)
Slide 8: The Platform - spiderAI architecture
Slide 9: From losses to workflows
Slide 10: Example workflow: Predictive Maintenance
The Technology Behind It (Slides 11-14)
Slide 11: Six radial capabilities
Slide 12: How data becomes prevented losses
Slide 13: Contextual intelligence at every phase
Slide 14: Right intelligence for the right person
Workflows & Value (Slides 15-18)
Slide 15: Multiple workflows that prevent losses
Slide 16: Proven results from real implementations
Slide 17: What "solved" actually looks like
Slide 18: ROI framework
Why We Win (Slide 19)
Slide 19: Competitive advantages
Getting Started (Slides 20-25)
Slide 20: Next steps
Slide 21: Implementation phases
Slide 22: Value demonstration & scale decision
Slide 23: Our commitment
Slide 24: Let's close your context gap
Slide 25: What you'll get from a discovery session
About Us & Contact (Slide 26-27)
Slide 26: About Us
Slide 27: Contact information
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minto.ai™ – Uncovering the Real-Time Truth Inside Industrial Operations
minto.ai™ is an industrial intelligence company dedicated to uncovering the real-time truth inside industrial operations. Our journey began with a commitment to combating entropy by developing accurate, reliable, and intelligent tools that help industries keep the world running efficiently and on time.
2017
Company founded with extensive plant visits and market research across manufacturing sectors
2018-2019
Pioneered Motor Current Signature Analysis (MCSA); launched iHz™ IoT device; first PoC at Patil Group with 40-machine deployment
2019
Top 2 Innovative Startups in India by BIRLASOFT (CK Birla Group)
2020
Launched spidersense™ – web-based equipment health monitoring platform
2023-2024
Integrated Generative AI capabilities, evolved into spiderAI™; SAIL-Bhilai project; launched minto.ai™ 2.0
Our Mission: To build accurate and trustworthy technology systems that enable industrial businesses to manage their operations at peak performance, efficiency, and availability year after year.
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spiderAI™ – Operational Intelligence Platform for Contextual Intelligence
spiderAI™ is an operational intelligence platform that delivers contextual intelligence for industrial operations. We don't just provide AI – we map and execute the actual workflows your teams use every day.
What Makes spiderAI Different
Every industrial operation is an expedition through a shifting landscape. Things are constantly changing:
- Equipment degrades
- Processes drift
- Demand fluctuates
- Failures cascade
At every step, every person on the team needs contextual intelligence to make the right decision for THEIR role.
The Critical Gap
Your plant has data everywhere – sensors, SCADA, historians, CMMS, MES. But data sitting in systems doesn't help the operator decide what to do NOW.
The gap isn't data availability or even analytics or AI – the gap is CONTEXTUAL INTELLIGENCE.
What We Deliver
We deliver intelligence that is FORMED by:
- Connecting DATA across domains with INTELLIGENCE layers
- Understanding the shifting landscape in operations
- Delivering the right insight to the right person
- At the right step in their workflow
- Only then the user can take RIGHT Decisions - which lead to the operational excellence.
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The Context Gap is Costing You 15-25% of Your Operating Expenses
$3.7 Trillion
Industrial Operations Globally Lose ~$3.7 Trillion Annually to Missing Contextual Intelligence
Organizations have invested heavily in IoT and data infrastructure. Annual spending exceeds $800 billion globally and will surpass $1 trillion by 2026. Yet McKinsey research indicates that 70% of Industry 4.0 initiatives fail to achieve their stated objectives.
Unplanned Downtime
5-10% of OpEx
Equipment fails without warning; reactive fire-fighting mode
Quality Defects
3-5% of OpEx
The "hidden factory" – rework, scrap, customer returns
Energy Waste
2-4% of OpEx
20-50% of energy inputs wasted through suboptimal processes
Yield Loss
2-4% of OpEx
Material waste, process inefficiencies
Repeat Failures
2-3% of OpEx
20% of assets cause 80% of problems – same issues recurring
The gap isn't data it's context. Your teams have data. What they don't have is intelligence FORMED from that data, delivered at the right time, to the right person, for the decision THEY need to make.
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The Shocking Reality – What Your Plant is Losing Today
Losses Across Multiple Dimensions in Continuous Process Plants
Mixing/Compounding
Common Losses: Temperature variation, batch inconsistency
Impact: Quality defects, rework
Extrusion
Common Losses: Die wear, profile drift
Impact: Scrap, rework, downtime
Curing/Vulcanization
Common Losses: Temperature/pressure variation, cycle time drift
Impact: Quality rejects, energy waste
Rotating Equipment
Common Losses: Bearing failures, alignment issues, seal leaks
Impact: Unplanned stops, cascading damage
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Why Current Solutions Don't Solve the Context Gap
The Common Failure: All these approaches make data ACCESSIBLE. None of them understand the WORKFLOW structure – the phases, tasks, and actions that operations teams actually execute.
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spiderAI™ – The Operational Intelligence Platform
spiderWEB: The Architecture for Contextual Intelligence
Critical Distinction:
spiderAI™ is the PLATFORM – the operational intelligence system of LLM driven AI agents which orchestrates workflows, agents, and capabilities.
spiderWEB is the ARCHITECTURE – the deliberate design for how AI agents collaborate to form and deliver contextual intelligence.
1. spiderAI™ Maps Use Cases into Workflows
spiderAI transforms complex operational challenges into structured workflows, guiding AI agents through distinct phases to achieve specific outcomes.
Maps use cases as WORKFLOWS with clear phases
Defining operational challenges as structured workflows with distinct stages.
Has WORKFLOW AGENTS that orchestrate each phase
Dedicated agents manage the overall progression and coordination within a workflow.
Has SKILL AGENTS that execute specific tasks within phases
Specialized agents perform detailed actions and analyses for individual workflow steps.
2. How spiderAI™ Spawns the Cognitive Layer using spiderWEB Architecture
The spiderWEB architecture is designed to connect diverse data domains and apply various intelligence types through a network of AI agents. These agents dynamically form cognitive layers throughout the workflow.
The cognitive layer in spiderAI™ is dynamic, emerging on the fly during workflow execution. Workflow Agents activate phases (e.g., Triage, Diagnose), invoking specialized Skill Agents. Both agent types leverage six radial capabilities (Knowledge, Reasoning, Algorithms, Understanding, Adaptation, Memory) across various data domains (Equipment, Operational, Process, Maintenance History, Sensor, Fault History). This synergy creates the cognitive layer—a "thinking web" that perceives, reasons, and acts. The resulting contextual intelligence is precisely tailored to each user's position and role within the workflow.
Connecting Data Domains (Spiral Layers):
Layer 1: Asset/Equipment Data
Layer 2: Operational Data
Layer 3: Process Data
Layer 4: Maintenance History
Layer 5: Sensor Data
Layer 6: Fault History
Applying Intelligence Types (Radial Layers):
A. Knowledge (domain expertise)
B. Reasoning (logical inference)
C. Algorithms (pattern recognition)
D. Understanding (semantic comprehension)
E. Adaptation (learning from outcomes)
F. Memory (contextual recall)
3. The Result: Contextual Intelligence Delivered
Delivers contextual intelligence to every user at every step in the workflow!
Ensuring relevant, real-time coherent information is available for critical decisions to navigate through the workflow
Contextual Intelligence on spiderAI™ :
Workflow Execution on spiderAI™ in AI driven mode
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From Losses to Workflows
Our Systematic Approach to Operational Excellence
Step 1: Understand the Losses
Every operation has losses – downtime, quality defects, energy waste, yield loss, safety incidents. We start by understanding WHAT is being lost and WHERE.
Step 2: Identify the Use Case Being Pursued
Operations teams are already pursuing use cases to prevent these losses. They may be doing predictive maintenance, yield optimization, energy optimization, or quality control. We identify what they're ALREADY trying to do.
Step 3: Map It as a Workflow
The use case becomes a WORKFLOW – an end-to-end process with clear phases, tasks, and actions. This is the structure that spiderWEB operates within.
Workflow Hierarchy
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Workflow Example: Predictive Maintenance
INSPECT
Tasks: Scan signals, Validate data integrity, Detect anomalies, Filter irrelevant equipment
What Gets Done: Identify which assets need attention
TRIAGE
Tasks: Classify issue (Health/Operations/Process), Rank risk, Correlate symptoms, Check historical context
What Gets Done: Prioritize what needs immediate action vs. monitoring
DIAGNOSE
Tasks: Compare normal vs abnormal states, Identify fault mechanisms, Eliminate competing hypotheses, Estimate failure progression
What Gets Done: Determine root cause and severity
PLANNING
Tasks: Recommend actions, Prioritize work orders, Assess urgency vs. impact, Schedule intervention
What Gets Done: Create actionable maintenance plan
Two Types of Agents Execute This Workflow
WORKFLOW AGENTS (Phase-Level)
- Orchestrate the journey through phases
- Know what phase they're in and what comes next
- Examples: Inspect Agent, Triage Agent, Diagnose Agent, Planning Agent
SKILL AGENTS (Task-Level)
- Execute specific, bounded tasks within phases
- Are reusable across different workflows
- Examples: Signal Scanner, Risk Ranker, Fault Identifier, Action Recommender
Key Insight: These same Skill Agents (Risk Ranker, Fault Identifier, etc.) can be used across OTHER workflows like Quality Optimization or Energy Optimization. The architecture is extensible.
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The Six Radial Capabilities – How Agents Think
Dynamically Spawning Contextual Intelligence
Knowledge
Domain expertise encoded – Understanding of industrial operations, equipment types, failure modes, best practices
Reasoning
Logical inference & deduction – Connecting causes to effects, building chains of logic, drawing conclusions
Algorithms
Pattern recognition & ML – Detecting anomalies, identifying signatures, predicting trajectories
Understanding
Semantic comprehension – Interpreting what signals MEAN in context, not just what they are
Adaptation
Learning from outcomes – Improving with each case, learning what works, adjusting approaches
Memory
Contextual recall – Remembering relevant history, similar situations, what happened before
How the Cognitive Layer is Dynamically Spawned
The cognitive layer in spiderAI™ is spawned dynamically during workflow execution. It's not a static component but emerges on the fly. When a workflow runs, a Workflow Agent (Phase Agent) activates a phase (like Triage or Diagnose), which invokes relevant Skill Agents (like Issue Classifier or Risk Ranker). Both Phase Agents AND Skill Agents then draw on the six radial capabilities (Knowledge, Reasoning, Algorithms, Understanding, Adaptation, Memory) as needed, applying these cognitive capabilities on the data domains (Equipment, Operational, Process, Maintenance History, Sensor, Fault History). The combination of agents using these capabilities on the data domains spawns the cognitive layer – essentially a "thinking web" that represents how the system perceives, reasons, and acts. What emerges depends on where a particular user is in the workflow, providing them with contextual intelligence specific to their position and role.
Critical Insight: Radial layers don't DO anything on their own. They are capabilities that agents USE. The cognitive layer is SPAWNED DYNAMICALLY during workflow execution.
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How SpiderAI Turns Data into Prevented Losses
A micro-case study!
The Reality Today
Your vibration analyst flags Pump P-104 trending upward. Your maintenance system shows the pump handles secondary cooling. Your operations team knows a shutdown is scheduled in 2 weeks.
Four systems, four signals, four silos—no one sees the connection.
The Gap
You have data everywhere. What you don't have is context—the intelligence that connects vibration patterns to failure predictions to operational schedules, and tells you what to do about it.
SpiderAI's Answer
The spiderWEB architecture dynamically connects these four data domains through 6 cognitive capabilities ( radial layers) then delivers integrated, cross-domain intelligence through structured workflows.
It doesn't just report metrics. It thinks across systems.
How the cognitive layer thinks:
✅ "Fault Detection: Bearing outer race defect developing (Sensor #5 × Algorithms #C). RUL Prediction: 3-4 weeks to critical failure (C-Algorithms × F-Memory—similar past cases). Process Context (#3): Pump handles secondary cooling loop—backup pump P-105 available. Operational Context (#2): Plant shutdown scheduled in 2 weeks for 4-day maintenance window. Recommendation: DEFER repair to planned shutdown. Risk acceptable (backup available + 3-4 week window). Saves $8K in emergency shutdown costs. Monitor daily until shutdown." (Uses 5×3×2×4×6 across B-Reasoning, A-Knowledge, C-Algorithms, F-Memory)
Vibration Data
Sensor #5 showing increasing trend on Pump P-104
Historical Data
Past bearing failure patterns and outcomes
Asset Data
Secondary cooling loop; backup pump P-105 available
Operations Data
Plant shutdown in 2 weeks for 4-day maintenance
↓
Through it's spiderWEB orchestration , spiderAI connects these signals
↓
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Contextual Intelligence at Every Phase
Real Example: M107 Conveyor Belt
INSPECT Phase
What SpiderAI Does: Scans 50 machines, filters by risk & availability, prioritizes by degradation trajectory
Intelligence Delivered: "M107 flagged: Risk score 27-28 (near critical threshold of 30), idle_pct 3.97 σ above baseline—CRITICAL anomaly confirmed, not false positive"
TRIAGE Phase
What SpiderAI Does: Classifies issue domain, detects cross-domain coupling, identifies feedback loops
Intelligence Delivered: "Classification: HO (Health + Operations). Feedback loop detected: Excess idle → thermal cycling → cage stress → degradation → more downtime. Loop type: POSITIVE (self-reinforcing). Status: ACTIVE. Risk: HIGH"
DIAGNOSE Phase
What SpiderAI Does: 7-layer reasoning: Symptom → Pattern → Fault correlation → Mechanism → Root cause → Confidence → Action
Intelligence Delivered: "ROOT CAUSE: Bearing cage damage (92% match). MECHANISM: BSF+FTF pattern = cage fault, BPFO/BPFI decreasing rules out raceway. CONTRIBUTING FACTOR: Thermal cycling from excess idle. 6322 bearing model: known cage failure mode. Confidence: 92% (HIGH)"
PLAN Phase
What SpiderAI Does: Checks constraints, verifies resources, recommends optimal window
Intelligence Delivered: "URGENCY: CRITICAL. M107 isolatable without plant impact ✓ Parallel conveyor available ✓ Window: Today 14:00-18:00. Parts: 6322 bearings (DE/NDE) in stock ✓ Lubricant available ✓ Post-repair: MCSA baseline to verify BSF/FTF eliminated"
The Difference: A sensor based analytics solution tells you "high idle time" and "rising kurtosis" (or any variable -two disconnected numbers). spiderAI™ tells you "these are connected through a self-reinforcing feedback loop, the root cause is bearing cage damage from thermal cycling, we're 92% confident, here's exactly what to do and when"
Tagline: Context isn't more data it's knowing that THIS operational pattern is causing THIS failure mechanism, with THIS confidence, and HERE's the optimal action window.
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The Right Intelligence for the Right Person
Each Role Gets What THEY Need to Decide
Operator
Triage intelligence: "This needs escalation, not immediate action"
Technician
Diagnostic intelligence: "Inner race fault, lubrication-related"
Planner
Scheduling intelligence: "Can wait until planned shutdown in 7 days"
Manager
Business impact intelligence: "Low risk if addressed in window; high risk if ignored"
Each person gets contextual intelligence for THEIR role
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Workflows That Prevent Your Losses
Beyond Predictive Maintenance – The Right Workflow for YOUR Loss Profile
spiderAI addresses ALL major loss categories through purpose-built workflows. The actual value from each workflow depends on YOUR plant's specific loss profile.
For Continuous Process Plants (like Rubber/Tyre Manufacturing)
Mixing/Compounding
Workflows: Quality Optimization, Energy Optimization
Why: Batch consistency critical; heating energy-intensive
Extrusion
Workflows: Predictive Maintenance, Quality Optimization
Why: Die wear affects quality; extruder downtime stops line
Curing/Vulcanization
Workflows: Energy Optimization, Quality Optimization
Why: Temperature-critical process; energy-intensive
Rotating Equipment
Workflows: Predictive Maintenance, Root Cause Analysis
Why: Continuous operation; repeat failures common
The Extensibility Advantage: Skill Agents are REUSABLE across workflows. A Risk Ranker skill works in Predictive Maintenance Triage AND Quality Optimization Detect. Once deployed, the platform compounds in value as you add workflows.
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Proven at One of the World's Largest Steel Plants
SAIL Sinter Plant III – Production Results, Not Lab Results
The Situation
- $33M annual losses from equipment failures
- External analysts took 5-7 days for diagnosis with 60-70% accuracy
- Route-based monitoring covered 1,000 machines on 10-day cycles – often missed
- Excel trackers built manually to correlate data
- They had the data. They had experts. But they couldn't execute the workflow with consistent contextual intelligence 24/7.
SpiderAI Results
95% Diagnostic Accuracy
Workflow agents + skill agents using radial capabilities (Previously: 60-70%)
10 Minutes to Diagnosis
Structured workflow vs. ad-hoc investigation (Previously: 5-7 days)
9 Pre-Failure Detections
Continuous execution of Inspect → Triage phases (Previously: Rare)
100% Post-Mortem Accuracy
Full Diagnose workflow with all capabilities (5 out of 5 cases)
$6.2M Annual Value
Right workflow → right intelligence → right actions
What This Proves: The spiderWEB architecture – workflows orchestrated by agents using radial capabilities – delivered what human experts couldn't at scale: consistent, accurate, contextual intelligence 24/7.
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What "Solved" Actually Looks Like
Observable Behavioural Changes at SAIL SP-III
Before spiderAI™
- Route-based monitoring on 10-day cycles, often missed
- External vibration analysts with 2-3 day site visit + 3-4 day report turnaround
- Excel trackers manually correlating CBMS data
- Engineering time spent building workarounds
- Fire-fighting mode – reacting to failures
After spiderAI™
- Continuous monitoring with real-time triage
- 10-minute diagnosis replacing 5-7 day external analysis
- Automated correlation across data domains
- Engineering time redirected to improvements
- Proactive mode – preventing failures
Observable Evidence
Excel trackers abandoned
spiderAI provides the context they were building manually
External analyst contract scope reduced
Internal capability now exceeds external expertise
Route-based monitoring supplemented with continuous
Real-time intelligence is more valuable
Shift handover includes spiderAI triage report
Integrated into daily operations
First-time fix rate improved
Accurate diagnosis = right repair first time
The Pattern: When contextual intelligence works, you see behavioral change. Teams don't just use the tool – they change how they work because the tool delivers what they actually need.
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Value Framework: How We Think About ROI
Actual Value Depends on YOUR Loss Profile – Here's the Framework
Sources: Siemens True Cost of Downtime 2024, ASQ COPQ Research, McKinsey, DOE, industry studies
Illustrative Value Framework
The table below shows how value COULD be calculated for a hypothetical plant. Your actual numbers will be different based on your specific loss profile.
The KPI Cascade – Where SpiderAI Creates Impact for
Example use-case: Predictive Maintenance
Root Cause
Diagnostic Accuracy, Diagnosis Speed – SpiderAI operates here: 95% accuracy, 10-minute diagnosis
Leading Indicators
PM Compliance, First-Time Fix Rate – Improve when diagnostics improve
Lagging Indicators
MTBF, MTTR, Breakdown Count – Will follow leading indicators
Outcomes
Equipment Uptime, Capacity Utilization – Will follow lagging indicators
Financial
Operating Margin, Cost per Unit – Will follow outcomes
Key Insight: spiderAI™ addresses the ROOT CAUSE (diagnostic uncertainty). We don't promise to directly improve your MTBF – we promise to improve diagnostic accuracy and speed. When diagnostics improve, downstream KPIs follow naturally.
Our Commitment: We will work with you in Discovery to identify YOUR specific losses and build a business case based on YOUR numbers, not generic industry averages.
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Why spiderAI™ Wins
Workflow-Native Architecture vs. Generic Approaches
The spiderAI™ Differentiation
Structure
Generic: Ad-hoc AI assistance
SpiderAI: Workflow-mapped with phases, tasks, actions
AI Agents
Generic: Generic agents that "help"
SpiderAI: Workflow Agents (phase orchestration) + Skill Agents (task execution)
Capabilities
Generic: Whatever the LLM can do
SpiderAI: Six radial capabilities: Knowledge, Reasoning, Algorithms, Understanding, Adaptation, Memory
Intelligence
Generic: Retrieved on demand
SpiderAI: FORMED dynamically as cognitive layer during workflow execution
Extensibility
Generic: Build from scratch each time
SpiderAI: Skill Agents reusable across workflows; radial capabilities constant
Why This Matters: Generic AI agents don't know what phase they're in. They can't orchestrate a workflow. They provide assistance, not intelligence. SpiderAI is WORKFLOW-NATIVE – we map the actual Inspect → Triage → Diagnose → Plan phases teams use, then orchestrate agents within that structure.
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From YOUR Losses to YOUR Workflows
We don't start with "deploy sensors" – we start with understanding YOUR losses. Our systematic discovery and deployment approach ensures we're solving your highest-impact challenges with workflows that match how your teams actually work.
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Implementation Phases: Discovery Through Deployment
Phase 1: Discovery
Understanding what is being lost and where in your operations is our foundation. We identify workflows you're already trying to execute and map them with precision.
Phase 2: Configuration & Deployment
We configure Workflow Agents and Skill Agents tailored to your operations, connecting to your existing data systems and training your team on execution modes.
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Value Demonstration & Scale Decision
Phase 3: Value Demonstration
Real operational results drive our approach. We run workflows with your team, track KPIs at root cause, leading, and lagging levels, and optimize based on feedback to create a compelling business case for expansion.
Phase 4: Scale Decision
Based on demonstrated value from the pilot, you decide the path forward. Our approach ensures you have quantified results before making expansion commitments.
Expand Workflows
Add Quality, Energy, or Yield workflows to your operations
Expand Coverage
Scale to additional equipment and operational areas
Expand Locations
Deploy to other plant locations across your enterprise
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Our Commitment to You
We don't prescribe a solution before understanding your problem. The Discovery phase ensures we're solving YOUR highest-impact losses with workflows that match how YOUR teams actually work.
Discovery-Driven
No predetermined solutions – we start with understanding your unique operational challenges and losses
Workflow-Focused
We map to how your teams actually work, not how we think they should work
Value-Proven
Demonstrate measurable results before asking for scale commitments
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Let's Close Your Context Gap
spiderAI™ delivers Contextual Intelligence for Industrial Operations, powered by minto.ai™. Schedule a Discovery Session to understand YOUR operational losses and identify the highest-impact workflows for YOUR plant.
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What You'll Get from a Discovery Session
Loss Map
Identify where YOUR plant is losing value across operations
Priority Use Cases
Ranked by impact and readiness for implementation
Workflow Mapping
Preliminary mapping for your top use case
Pilot Scope
Proposed timeline and deployment plan
No Obligation
No predetermined solution, just insights
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minto.ai™ - Industry Validation & Ecosystem
Past Experience across Real Industrial Environments & Major Industrial Sectors
Steel Manufacturing
Blooming Mill, Hot Strip Mill, Sinter Plant
Foundry Operations
Insert casting (multiple sites)
Process Manufacturing
PVC pipe manufacturing
Heavy Industry
Continuous process plants
Major Industrial Clients we work with-
SAIL
Steel Authority of India Limited – Multiple plants including Bhilai, Bokaro
TATA Steel
TATA Steel Long Products Limited
HIL
Hyderabad Industries Limited
Patil Group
Multiple sites
What Our Customers Say
Real Results from Real Industrial Operations
Patil Group
"Before minto.ai's solution, we had 83 hours of production loss over three months, resulting in approximately 57,000 lost inserts. After implementing spidersense™, minto.ai's condition monitoring platform, 90% of this downtime was eliminated. Our maintenance engineers now have better awareness of machines and failure mechanisms, with some months seeing no downtime at all."
— Manager, Automation | Patil Group.
Key Result: 90% downtime elimination
SAIL Bokaro
"After installing Minto Ai's system, we quickly identified an issue in the intermediate gearbox. Their system has been helpful in diagnosing the stalling motor and the gearbox shaft problem."
— Maintenance Engineer | SAIL Bokaro.
Key Result: Rapid issue identification in critical gearbox
TATA Steel Long Products Limited
"Kudos to the team at Minto Ai for identifying the problem accurately. Their timely detection helped us replace the mandrel assembly, preventing further damage caused by the smaller pulley key and keyway getting crushed."
— Deputy Divisional Head | TATA Steel Long Products Limited.
Key Result: Prevented cascading equipment damage
The Pattern Across All Testimonials
- ✓ Accurate problem identification
- ✓ Timely detection before catastrophic failure
- ✓ Measurable losses reduction
- ✓ Enhanced operations team awareness
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Contact Us
spiderAI™- Contextual Intelligence for Industrial Operations
From minto.ai™
Website: minto.ai
Email: spiderai@minto.ai
Phone: +91-8501903007
"We're dedicated to uncovering the real-time truth inside industrial operations - combating entropy (the natural decline of order if not acted upon) with technology & intelligence , so that the world runs on time, efficiently, and sustainably"
Visit Our Office
Minto Softech Private Limited
Come see our office filled with Plants, Books, Charts, Diecast Toy Cars and the Aroma of fresh Coffee!
4th Floor, Plot no. 114, Street No. 03, floor no. 04, Rai Durgam, Prashanth Hills Gachi Bowli,
Serilingampally, Hyderabad,
Telangana - 500008.
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spiderAI™