Elevat is an Industrial Internet of Things (Industrial IoT) company that offers a cloud-based solution for companies to manage their connected assets through the supply chain, manufacturing, transportation, and logistics industries.
In addition to its solutions, Elevat was founded as a spin-out of Western Integrated Technologies.
The primary focus of Elevat has been on the mobile and off-highway industries; utilizing hardware-agnostic IoT solutions.
The company has established an existing IoT platform through significant investment and partnerships within the industry; however, there are limited recent publicly available metrics and reviews to demonstrate the reliability of the platform in the industrial sector.
Elevat raised $2.5 million in a seed round to further develop a cloud-based IoT platform.
Elevat secured $10.55 million in a series A round to further expand its IoT solutions for heavy machinery.
Cloud platform with edge gateways (Microsoft Azure partnership)
Industrial IoT platform with data analytics for asset lifecycle management, fleet optimization, and predictive insights
Inferred from product descriptions and Microsoft partnership; limited public technical details
ElevateAI has an api that processes audio through a multi step process (declare interaction, upload file or uri, check status until finished, then get transcript/ai). It includes, but is limited to, STT, SA, BA, and GA endpoints for CX insight and coaching.
ElevateAI uses bearer token for authentication. Tokens can be generated upon creating an account and include in every header of every endpoint as a parameter.
Yes, ElevateAI does have an official python sdk on github. This SDK makes it easier to create an interaction, upload files, check the status, and retrieve the results of STT and ai.
Audio files can either be uploaded directly or provided as a public uri. Files must be processed completely prior to generative ai. Architecture should be multithreaded for increased throughput.
The cx endpoints are designed for use cases involving contact center type interactions (behavioral analysis and sentiment) while the generic endpoints are similar generative ai capabilities for other use cases. Both will use the same interaction process.
Time to process a file depends upon the size of that file and its level of complexity. As such, your application will need to poll the Interaction Status every 30 seconds utilizing GetInteractionStatus to determine when an interaction has been "processed" or an error has occurred.
Upon account creation, API Tokens become available. No specific pricing information can be found in the documentation; therefore, you would need to contact ElevateAI to obtain pricing information regarding their enterprise plans.
Phrase-by-Phrase Transcripts, Punctuated Transcripts, Sentiment Analysis, Behavioral Analysis, Auto-Summary, Agent Coaching Insights, and Q&A Capabilities on interaction data via generative AI endpoints.
ElevateAI provides specialized AI capabilities for contact center analytics with robust REST API Infrastructure and Python SDK Support. It also provides strong Speech-to-Text Accuracy combined with Behavioral and Generative AI insights for CX teams. Documentation Quality and OpenAPI Compliance enable Enterprise Integration.
ElevateAI is best suited for Enterprise Contact Centers that are developing Custom CX Analytics Pipelines that include both Speech-to-Text and Generative AI capabilities.
ElevateAI has a contact center-specific AI solution that uses REST APIs for speech-to-text, behavioral analysis, sentiment, and generative AI insights. The company’s documentation is fully compliant with OpenAPI version 3.0 and includes a full Python SDK for integration. Additionally, ElevateAI uses an asynchronous polling architecture with a declare/upload/status/retrieve model that will allow for large scale enterprise processing.
Good - detailed technical documentation from docs.elevateai.com and GitHub SDK. No pricing, SLA, or rate limit information publicly available. Focused on contact center/CX use cases.
Identify key patterns using artificial intelligence to anticipate and identify unmet demand in your supply chain and discover underlying market trends
Monitor inventory levels continuously with IoT sensors and AI-based systems to automatically adjust inventory accordingly
Use autonomous warehouse robots with machine learning-based adaptation for picking/packing/sorting tasks
Create digital twins and perform scenario planning for optimal workflow design and risk mitigation. Text Between The Markers (BEGIN_TEXT) and (END_TEXT)
For Work Orders, Sales Summaries, Inventory Management: Natural Language Interfaces
Systems that are self-optimize in regards to inventory, production schedules, and real-time decision making
| SCOR Phase | Primary Use Cases | Key Business Outcomes |
|---|---|---|
| PLAN | AI demand forecasting, inventory optimization, supply risk mitigation, scenario planning | 30-50% forecast accuracy improvement, 20-30% inventory cost reduction, 35% inventory reduction |
| SOURCE | Supplier performance prediction, automated procurement, cycle count corrections | Improved supplier reliability, reduced stockouts, faster sourcing cycles |
| MAKE | Production scheduling, predictive maintenance, quality control, work order automation | 15-25% throughput increase, reduced defects, minimized downtime |
| DELIVER | Real-time tracking, route optimization, order fulfillment automation | 65% service level improvement, faster delivery times |
| RETURN | Scrap adjustments, reverse logistics optimization, performance gap analysis | Reduced carrying costs, improved cash flow |
Work Order Generation and Inventory Adjustments through Rootstock ERP’s AI Capabilities
Production Data, Work Order Travelers, Subcontract Operations Integration: In Real-Time
Through AI Robotics: Inventory Visibility, Cycle Counts, Picking/Packing Automation
Planning, Execution, Tribal Knowledge Systems: Unifying Data Across
Connectivity and Agent: IIoT Sensors, Continuous Monitoring and AI
Workflow Automation and Configurable Business User AI Agents
Proof of 15% Logistics Cost Reduction, 35% Inventory Reduction, 65% Service Level Gains: On Similar Datasets
Architecture Diagrams: Connect Rootstock ERP, MES Work Order, and WMS Robotics; Demonstrate
Live Demonstrations of Natural Language Inventory Management, Work Order Generation, and Real-Time Inventory Adjustments
Minimum SKU Count, Transaction Volume, Data Quality Thresholds for Model Training: Specify
Manufacturing/Supply Chain Case Studies: Quantified ROI Metrics, Customer References, and Data Sets
Concurrent Users? Multi-Site Support? IIoT Data Ingestion Capacity? Uptime SLA's: Specify
Forecast Accuracy Tracking: What Triggers Model Updates? Performance Degradation Alerts?
Audit Reports, Data Residency Options, SOX Controls, Latest ISO 9001/27001 Certifications: Provide
