NQ Platform - Nextqore

Platform Features

Data Quality

Component Library

Notification

Visualization & Dashboards

Scalability

Flexible Deployment Options

Security

Magnifying glass with bar chart for data analysis

Data Quality

AI models are only as reliable as the data they consume. Nextqore’s AI Pre-Processor maintains
data quality by applying configurable validation and verification checks at the point of ingestion — before data
reaches any AI system. When a check fails, the issue is flagged in real time, giving your team the opportunity
to investigate and correct it at the source. The result is AI that reasons from data you can trust, not data you
hope is right.

Three connected cubes representing data sharing

Component Library

Pre-built integration components for sensors, IT systems and business logic — requiring minimal configuration regardless of OEM differences. Includes business rules and reference data that govern how ingested data is validated and contextualized before reaching an AI model

IT Connectors

Flipkart   

HereMaps   

Walmart   

Amazon   

Google

Flexport   

Clover POS   

Meta   

MYX   

Mobile As A Sensor

LIDAR Scans   

Drone Videos

High-Resolution Videos

Mechanical Sensors ( External )

Vibration   

Force   

Pressure   

Strain   

Torque   

Tilt   

Mechanical Sensors ( Embedded )

Internal Temperature   

Load Sensors   

Accelerometer   

RPM Sensors   

Gyroscope   

Proximity Sensors

Environmental Sensors

Temperature   

Humidity

Air Quality
( PM 2.5, PM 10 )

Solar Irradiance

Barometric Pressure

Wind Speed & Direction   

Noise

Rainfall

Business Logic

Top Selling Products   

Listing new products

Pricing updates

Orders

Notification

Notification Alerts are triggered based on defined data events and quality conditions — reaching the right teams at the moment action is needed. Three configurable event types:

1.Pipeline disruption — data source failure or transfer interruption

2. Quality threshold breach — data parameter outside defined boundaries 3.Derived parameter exceedance — computed metrics crossing operational thresholds. Delivered via SMS, email or in-app on Android and iOS.

Cube surrounded by curved digital dashboards and data charts

Visualization & Dashboards

Real-time process status, KPI trends and AI output analytics presented through configurable, dashboards. Accessible across desktop, mobile and handheld via the Nextqore app on Android and iOS.

Cloud and arrow above server stacks showing data growth

Scalability

Hyperscalable cloud architecture adapts to data volume without infrastructure reconfiguration. Pay-as-you-go pricing aligns cost to actual usage — scaling up at peak demand and down during quieter periods with no stranded infrastructure cost.

Flexible Deployment Options

Supports single-instance deployments for small teams through to multi-region enterprise setups — operating as a standalone layer with no changes to existing systems. Options include Nextqore Cloud, Dedicated Cloud and Customer-Specific Cloud instances

Security

Data security enforced from source to destination via SSH tunnel, HTTPS and SFTP protocols. Data at rest encrypted by default. Access control and audit logging ensure enterprise data governance compliance

AnySource Data Combiner

Enterprise data exists in silos across IT systems, cloud platforms, field devices, documents, videos and email. The AnySource Data Combiner connects to all of them — enforcing schema validation, business rule compliance and source attribution at every step. The output is a unified, auditable dataset ready for AI preprocessing

Integrate Data from varied sources seamlessly

Blue icon of a cloud with stacked server racks inside, representing cloud computing or cloud storage services.

Blue icon of a microchip with circuit lines extending outward, representing computing or processor technology.

IT Applications

Captures operational and transactional data from IT applications such as CRM ERP Inventory Management, Order entry etc.. for seamless integration.

Cloud Storage

Aggregates and processes data stored across cloud platforms for scalable analysis.

Field Devices

Combines data form embedded, external, and add-on sensors to provide real-time operational insights.

Documents

Analyze unstructured data from diverse document types for enhanced context and relevance.

Videos

Processes LiDAR scans and drone footage to extract actionable visual and spatial data.

Email

Corporate email carries significant operational intelligence — customer communications, supplier confirmations, approval workflows and exception notifications.

Every Source. One AI-Ready Pipeline

Enterprise data sits across IT systems, cloud platforms, field devices, documents, videos and email. The AnySource Data Combiner connects to all of them — enforcing quality, eliminating silos and delivering a single, auditable dataset ready for contextualization and AI deployment

Enhance collected Data by referencing with relevant Business information for meaningful analysis.
This flexibility empowers businesses to

Ingest All Sources

Structure and Validate

Improve Model Accuracy

Data Context Builder

The Combiner output captures what happened. The Data Context Builder adds why it happened — applying operational conditions, environmental signals, correlations, mathematical derivations and Semantics to produce a deterministic, context-rich dataset. AI models operating on contextualized data demonstrate 2–4× higher deployment success rates than those on uncontextualized inputs

Making Data Work for Smart Outcomes

Conditions

Correlation

Computation

OBS

Add Context to Operating conditions

Understanding the conditions under which data is generated is crucial for insightful analysis.  This allows businesses to go beyond simple performance reporting and explain the “why” behind the values.  Whether influenced by environmental factors or internal/external conditions, our solution provides the context needed to derive meaningful analysis and strategic insights.

Influencing factors such as

Weather Context

Seasonality Context

Traffic Context

Efficient Data Structuring for Enhanced Insights

Refining and structuring datasets within the pipeline ensures that businesses have access to the right information for analysis and decision-making.  This streamlined process minimizes the time required for data preparation, allowing teams to focus on extracting meaningful insights.  By efficiently correlating and organizing data, businesses can improve reporting accuracy and optimize their decision-making processes.

Perform Logical and Mathematical Outcomes on the Data to Enhance Use of data

01. Produce derivative parameters by conditionally filtering and/or mathematically combining sourced data
02. Aggregate data as per the Business/Operational needs with automatic scheduling

Optimizing Data for Business Intelligence

Derived Outcomes

Time Series Aggregation

Define Meaning and Relationships Across Data to Enable AI Understanding

Data alone does not understand your business. Ontology Based Semantics (OBS) gives your data a business
vocabulary — defining what each data point means, how it relates to other data, and where it sits in the context
of your operations. Instead of processing rows and columns, AI models built on OBS reason with business
concepts: customers, assets, events, relationships. The result is AI that does not just identify patterns — it
understands what those patterns mean for your business and supports decisions accordingly.

Extend the Platform. No New Data Layer Required.

Four capabilities activated directly within the Nextqore platform — analytics, machine learning, visualisation, and notifications — each operating on data that is already validated, contextualised, and AI-ready.

Analytics

Ask anything. Get the answer in seconds.

Type a question in plain English and receive an instant answer…

A built-in Business Glossary understands company-specific terminology…

Data stays fully secure — the AI receives only a structural map…

Outcomes

Business users get instant answers without depending on analysts
Decisions made on live data, not last week’s report
Zero technical skills required

Machine Learning

Confident predictions built on data the business can trust and explain.

Structured, validated data eliminates the preparation work that typically consumes 70–80% of a data scientist’s time. Only records meeting defined quality thresholds enter the training pipeline.

Semantic Bindings version-lock feature definitions across training and inference — eliminating silent model degradation when data drifts. Predictions are stored back into the platform and immediately queryable through NextqoreAI Analytics.

Full lineage traces every prediction back to its source — explainable to stakeholders and regulators without additional effort.

Outcomes

Data preparation time cut from weeks to days
Models trained on richer, higher-quality inputs
Every prediction is explainable and traceable

Visualization

Live dashboards authored by the team, shared across the business.

Build and publish dashboards using familiar drag-and-drop tools, powered by AWS QuickSight. No dependency on a technical team — business users author and own dashboards directly.

Role-based access ensures the right people always see the right data. Dashboards refresh automatically as new records arrive. Data assets are searchable using business terms, not technical field names.

Outcomes

Business teams own their dashboards without IT involvement
Always-current data — no manual refresh or scheduled export
Governed access across every team and role

Notification

The right alert. The right person. The moment it happens.

Configure event conditions using threshold-based and conditional logic — no code required. A Notification Agent monitors data continuously and fires the moment a condition is met.

Alerts are delivered instantly through SMS, Email, WhatsApp, or Slack — to the right person, through the right channel. Notifications are not scheduled — they fire the moment a threshold is crossed.

Outcomes

Teams act the moment an event occurs — not after the next scheduled check
No manual monitoring — the agent watches continuously
Configured once, runs automatically

The AI Data Preprocessor

Data Quality

Component Library

Notification

Visualization & Dashboards

Scalability

Flexible Deployment Options

Security

Data Quality

Component Library

Notification

Visualization & Dashboards

Scalability

Flexible Deployment Options

Security

Integrate Data from varied sources seamlessly

IT Applications

Cloud Storage

Field Devices

Documents

Videos

Email

Every Source. One AI-Ready Pipeline

Add Context to Operating conditions

Efficient Data Structuring for Enhanced Insights

Perform Logical and Mathematical Outcomes on the Data to Enhance Use of data

Define Meaning and Relationships Across Data to Enable AI Understanding

Get AI Right. Start Here.