Sold by: Timeseer.AIÂ
Deployed on AWS
Timeseer.AI is an OT/IoT data software company established to address the data quality challenges of OT/IoT data. This data is at the heart of all data driven solutions in the industry, whether is it reporting, AI/ML, or digital twin solutions.
Overview
Timeseer.AI is an OT/IoT data software company established to address the data quality challenges of OT/IoT data. This data is at the heart of all data driven solutions in the industry, whether is it reporting, AI/ML, or digital twin solutions in the industry. With more than 80 human-years of relevant time-series data experience and over 100+ sensor data quality checks, Timeseer.AI understands the complexity to turn raw OT/IoT data into high quality, reliable data. Our software does the heavy lifting and empowers data teams to detect, analyze, and fix data quality issues before these significantly impact operations and critical business decisions. Our customers span across all industries with 1 common type of data: sensor data. From Oil and Gas to manufacturing, utilities, pharmaceutical manufacturing, chemical manufacturing, automotive if you have sensor data, Timeseer can enable your teams to accelerate. As a partner of AWS, our software seamlessly integrates into your data architecture and safeguards your data quality. We enable data teams to focus on their added value: generate insights into data and reduce their time on data cleaning.
Highlights
- Reduction of data science work by > 80%.
- Automatically validated and augmented data leading to better output from models, ML, AI, digital twins.
- Scalability, ability to rapidly deploy at scale across assets, enabling full potential of OT data applications.
Details
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Delivery method
Supported services
Delivery option
Container Image
Latest version
Operating system
Linux
Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Scale | Scale Tier - 4,000 Tokens included. Cap at 15000 tokens (For more tokens please contact us for Private Offers ,niels@timeseer.ai) | $49,000.00 |
Enterprise | Enterprise Tier - 7,000 tokens included. (For more tokens please contact us for Private Offers ,niels@timeseer.ai) | $99,000.00 |
Vendor refund policy
We do not currently support refunds.
Legal
Vendor terms and conditions
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Delivery details
Container Image
Supported services: Learn moreÂ
- Amazon EKS
Container image
Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.
Version release notes
The "Series" tab in data services has been improved. The series reliability score is now shown. Series can be filtered by severity or KPI rule.
Data service notifications are now sent only once for each specific issue.
Flows can now send notifications when they fail.
The drop values flow block now supports dropping data points where the quality flag is not good.
The split and segment blocks now support a duration.ignoreSegmentStart option to exclude the start of a segment. This allows skipping startup periods where behavior is known to be erratic for example.
Modules that check limits gained a tolerance parameter to allow slight excursions.
A warning is now shown when displaying charts with out of order timestamps. Out of order timestamps are known to cause weird plotting artifacts and should be corrected by using the fix timestamp issues block.
Timeseer will now wait for data service calculations to finish at startup. This means the user interface can take longer to become available, but there is no further waiting while first loading the dashboard.
Additional details
Usage instructions
A data volume should be attached to /usr/src/app/db
Timeseer listens on port 8080.
Timeseer runs using UID 1001 inside the container by default.
Include:
securityContext: fsGroup: 1001in the Kubernetes Deployment spec to ensure the data volume has correct write permissions.
Running as other UIDs is supported.
To run in Docker, first pull the container image.
Create a volume:
docker volume create timeseerThen start Timeseer:
docker run --rm -v timeseer:/usr/src/app/db -p 8080:8080 <AWS MP Container image URL>The full Timeseer Administrator Guide is available at http://localhost:8080/help/admin once Timeseer has been started.
Support
Vendor support
Technical : Keep your Timeseer technology running:
- Access to our knowledge base and documentation.
- Timeseer upgrades.
- Direct access to the support team through the Timeseer application, email and/or phone.
- Provides full support on submitted requests, bug fixes and providing circumventions whenever it is possible.
Advisory Support : Our customer advisor's guide you to success :
- Monthly status check in with your dedicated customer advisor to review product challenges, user adoption and user requirements.
- Quarterly Business Review (QBR) to review the captured value and discuss future goals.
- Bi-yearly product advisory board to share product roadmap & vision
- Escalation path through your dedicated customer advisor
Functional : Additional expert services for adaption and impact :
- Installation, set-up and integration of Timeseer into your IT architecture.
- Timeseer user training to become self-sustainable in using the technology at scale.
- Timeseer admin training to manage your Timeseer environment.
- Timeseer coaching to assist you in implementing use cases.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Data Quality Validation
Comprehensive sensor data quality checks with over 100 validation mechanisms for OT/IoT data
Time-Series Data Processing
Advanced processing capabilities for handling complex time-series sensor data across multiple industrial domains
Data Integration Framework
Seamless integration with existing data architectures and AWS ecosystem for efficient data management
Automated Data Transformation
Automated detection, analysis, and remediation of data quality issues with minimal manual intervention
Scalable Data Architecture
Highly scalable platform capable of rapid deployment across diverse industrial assets and sensor networks
Machine Learning Anomaly Detection
Automated machine learning engines for detecting data quality issues across databases without user supervision
Data Stream Scalability
Capable of processing and analyzing millions of time series data streams with rapid machine learning model generation
Multi-System Integration
Supports comprehensive API interactions and integration with existing notification and operational technology systems
Data Quality Metrics
Provides advanced metrics like Data Quality Index for comprehensive data quality assessment and visualization
Industrial Data Focus
Specialized platform for analyzing data from automation equipment, Industrial IoT, laboratory, and environmental measurement systems
Data Fabric Architecture
Unified platform that connects data across multiple clouds and on-premises environments without data movement
Universal Query Engine
AutoSQL technology enabling comprehensive data querying across disparate data sources
Data Governance Framework
Automated policy enforcement and real-time data discovery and classification capabilities
AI Model Management
ModelOps system for synchronizing application and model pipelines with drift and bias reduction
Data Integration Capabilities
Powerful automated integration tools for designing, developing, and running data transformation and movement jobs
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