Elevat

  • What it is:Elevat is a Seattle-based Industrial IoT platform that connects off-highway machines for management, tracking, remote upgrades, and AI-driven repair assistance to speed equipment repairs.
  • Rating:72/100Good
  • Expert's conclusion: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.
Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Elevat and What Does It Do?

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.

Active
📍Seattle, WA
📅Founded 2015
🏢Private
TARGET SEGMENTS
Industrial ManufacturingTransportation & LogisticsMobile & Off-Highway EquipmentHeavy Machinery

What Are Elevat's Key Business Metrics?

📊
$13.05M
Total Funding
📊
2
Funding Rounds
📊
$10.55M Series A (2020)
Latest Funding
💵
<$5M
Annual Revenue
🏢
11-50
Employees
📊
Top IoT Emerging Company of the Year (2021)
Awards

How Credible and Trustworthy Is Elevat?

72/100
Good

Elevat's out-of-the-box, configurable, and customized solutions utilize edge gateways to connect factory equipment, trucks, and other assets to provide real-time data visualization and optimization.

Product Maturity80/100
Company Stability75/100
Security & Compliance70/100
User Reviews65/100
Transparency70/100
Support Quality75/100
Spin-out from Western Integrated TechnologiesMicrosoft Azure IoT Edge partnershipTop IoT Emerging Company award (2021)

What is the history of Elevat and its key milestones?

2015

Company Founded

In addition to its solutions, Elevat was founded as a spin-out of Western Integrated Technologies.

2017

Seed Funding

The primary focus of Elevat has been on the mobile and off-highway industries; utilizing hardware-agnostic IoT solutions.

2020

Series A Funding

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.

2021

IoT Award

Elevat raised $2.5 million in a seed round to further develop a cloud-based IoT platform.

2023

Microsoft Partnership

Elevat secured $10.55 million in a series A round to further expand its IoT solutions for heavy machinery.

Who Are the Key Executives Behind Elevat?

William HillCEO & Co-founder
Elevat earned the top award for Emerging Company of the Year in IoT by Compass Intelligence.
Adam LivesayCo-founder
Elevat announced a technical partnership with Microsoft to provide Azure IoT Edge solutions for heavy machinery.

What Are the Key Features of Elevat?

Edge Gateways
I am a co-founder of Elevat, and I have responsibility for the overall direction of the company; including developing the IoT platform since the inception of the company.
Cloud Dashboard
I am a co-founder of Elevat, which was founded in 2015. As part of the spin-out from Western Integrated Technologies, I also contributed to the product strategy of the company.
Hardware Agnostic
Hardware gateways are easily installable, allowing users to connect factory equipment, trucks, and assets to the cloud to collect data in real-time.
Customizable Reporting
Users can visualize key data points from the connected assets using a user-friendly interface; and view them on either a smartphone or laptop.
Out-of-the-Box Solutions
Elevat works with any gateway or connected device to allow for flexibility across different types of industrial equipment.
Configurable Solutions
Elevat provides customizable reports and data visualization options for each user using built-in widgets.
Custom Development
Customized integration to your company's needs, asset tracking, fleet management services.

What Technology Stack and Infrastructure Does Elevat Use?

Infrastructure

Cloud platform with edge gateways (Microsoft Azure partnership)

Technologies

IoTCloud ComputingEdge Computing

Integrations

IT Business SystemsAzure IoT EdgeMachine Sensors

AI/ML Capabilities

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

What Are the Best Use Cases for Elevat?

Heavy Machinery Manufacturers
Real time monitoring of equipment; optimal scheduling for maintenance and improved uptime through edge gateway IOT data.
Fleet Operators
Tracking of trucks and mobile assets by using location, usage, and performance metrics.
Industrial Plant Managers
Displaying of data from various types of factory machinery with custom reportable widgets.
Service Technicians
Remote diagnostics and access to asset information for quicker fixes and less down time through smartphone app.
NOT FORConsumer IoT Startups
Unsuitable - Heavy industry grade equipment and large scale enterprise deployments.
NOT FORHigh-Frequency Trading Systems
N/A - Industrial IOT is timed at millisecond levels where sub second data required for financial applications.

What APIs and Integrations Does Elevat Support?

API Type
REST API conforming to OpenAPI 3.0 specification
Authentication
Bearer token (API token) authentication required for all endpoints
Webhooks
No webhook support mentioned in documentation
SDKs
Official Python SDK available on GitHub for speech-to-text API
Documentation
Comprehensive documentation at docs.elevateai.com covering tutorials, API endpoints, and generative AI features
Sandbox
No sandbox or testing environment mentioned; production API token provided immediately upon account creation
SLA
No SLA or uptime guarantees publicly documented
Rate Limits
No rate limits specified in public documentation
Use Cases
Speech-to-text (ASR), behavioral analysis, sentiment analysis, generative AI for CX insights, auto-summaries, agent coaching, Q&A on interaction data

What Are Common Questions About Elevat?

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.

Is Elevat Worth It?

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.

Recommended For

  • Contact Centers that require Speech Analytics and Agent Coaching.
  • Enterprises with large amounts of call data that wish to utilize AI insights.
  • CX Operations Teams that have experience working with REST API Workflows and Polling Patterns.
  • CX Operations Teams that want integrated ASR + GenAI capabilities.

!
Use With Caution

  • CX Operation Teams that want real-time processing — utilizes an Async Polling Model.
  • Developers who expect Webhooks — Polling-Based Architecture Only.
  • Small Teams that do not have API Integration Expertise Required.

Not Recommended For

  • Real-Time Conversational AI Applications.
  • Teams that want to take advantage of an extensive Pre-Built SDK Ecosystem.
  • Budget-Constrained SMBs without Dedicated Developers.
  • Use Cases Beyond Voice/Contact Center Analytics.
Expert's Conclusion

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.

Best For
Contact Centers that require Speech Analytics and Agent Coaching.Enterprises with large amounts of call data that wish to utilize AI insights.CX Operations Teams that have experience working with REST API Workflows and Polling Patterns.

What do expert reviews and research say about Elevat?

Key Findings

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.

Data Quality

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.

Risk Factors

!
This type of architecture may not be suitable for applications requiring real-time responses.
!
While there are many SDKs available today, they are mostly limited to Python; as such, users who wish to integrate other programming languages into their workflows may find this to be a limitation.
!
There are no webhooks or push notifications currently supported by ElevateAI.
!
Due to its contact center specific design, the solutions offered by ElevateAI may not be applicable to all types of applications.
Last updated: February 2026

What Are the Best Alternatives to Elevat?

  • Deepgram: Deepgram provides both real-time and batch speech-to-text API with the ability to detect sentiment and topics. Additionally, Deepgram offers lower latency using streaming models versus ElevateAI’s polling-based architecture. Deepgram would be best suited for those applications where real-time, sub-second transcription is required. (deepgram.com)
  • AssemblyAI: AssemblyAI offers a universal speech AI platform providing transcription, summarization, and sentiment analysis capabilities. As compared to ElevateAI, AssemblyAI has more endpoints available as well as supports webhooks. AssemblyAI would be ideal for a wide variety of use cases involving audio beyond what can be accomplished with contact center applications. (assemblyai.com)
  • Gladia: Gladia is a multilingual speech-to-text platform that provides real-time transcription and analysis on over 100 languages while ElevateAI focuses on CX. Therefore, Gladia would be best-suited for global enterprises with a need to analyze and understand multiple language call data. (gladia.io)
  • Symbl.ai: Symbl.ai is a conversation intelligence platform offering real-time API and webhooks as well as analytics capabilities. In addition to the basic transcription provided by ElevateAI, Symbl.ai also provides additional conversational AI features, including action items and follow-up conversations, which would be beneficial to sales and service teams beyond basic transcription. (symbl.ai)
  • Rev.ai: Rev.ai is an automated speech recognition (ASR) provider that provides high accuracy transcription in addition to speaker diarization and custom vocabulary options. Similar to ElevateAI, Rev.ai has an asynchronous polling architecture but is generally priced less than ElevateAI for customers requiring simple transcription functionality. (rev.ai)

AI-Driven Supply Chain Performance Improvements

15 %
Logistics Costs Reduction
35 %
Inventory Levels Reduction
65 %
Service Levels Improvement
30-50 %
Forecast Error Reduction
20-30 %
Inventory Carrying Cost Reduction
15-25 %
Production Throughput Increase

Supply Chain Manufacturing KPI Benchmarks

On-Time Delivery Performance
95%+
Perfect Order Rate (POR)
90%+
Inventory Turnover Ratio
5-10 cycles
Production Cycle Time
Minimized by SKU
Cash-to-Cash Cycle Time
Minimized by product
Service Levels
65%+ improvement target

AI & Machine Learning Capabilities

Demand Forecasting

Identify key patterns using artificial intelligence to anticipate and identify unmet demand in your supply chain and discover underlying market trends

Real-Time Inventory Tracking

Monitor inventory levels continuously with IoT sensors and AI-based systems to automatically adjust inventory accordingly

AI-Powered Robotics

Use autonomous warehouse robots with machine learning-based adaptation for picking/packing/sorting tasks

Predictive Analytics

Create digital twins and perform scenario planning for optimal workflow design and risk mitigation. Text Between The Markers (BEGIN_TEXT) and (END_TEXT)

Generative AI Agents

For Work Orders, Sales Summaries, Inventory Management: Natural Language Interfaces

Reinforcement Learning

Systems that are self-optimize in regards to inventory, production schedules, and real-time decision making

AI Use Cases Across SCOR Supply Chain Framework

SCOR PhasePrimary Use CasesKey Business Outcomes
PLANAI demand forecasting, inventory optimization, supply risk mitigation, scenario planning30-50% forecast accuracy improvement, 20-30% inventory cost reduction, 35% inventory reduction
SOURCESupplier performance prediction, automated procurement, cycle count correctionsImproved supplier reliability, reduced stockouts, faster sourcing cycles
MAKEProduction scheduling, predictive maintenance, quality control, work order automation15-25% throughput increase, reduced defects, minimized downtime
DELIVERReal-time tracking, route optimization, order fulfillment automation65% service level improvement, faster delivery times
RETURNScrap adjustments, reverse logistics optimization, performance gap analysisReduced carrying costs, improved cash flow

Enterprise System Integration & Data Unification

ERP System Integration

Work Order Generation and Inventory Adjustments through Rootstock ERP’s AI Capabilities

Manufacturing Execution System (MES)

Production Data, Work Order Travelers, Subcontract Operations Integration: In Real-Time

Warehouse Management System (WMS)

Through AI Robotics: Inventory Visibility, Cycle Counts, Picking/Packing Automation

Enterprise Knowledge Graph (EKG)

Planning, Execution, Tribal Knowledge Systems: Unifying Data Across

Real-Time Data Streams

Connectivity and Agent: IIoT Sensors, Continuous Monitoring and AI

No-Code/Low-Code Integration

Workflow Automation and Configurable Business User AI Agents

Supply Chain Security & Compliance Requirements

Data Encryption (at rest & in transit)Required for real-time IIoT and cloud ERP data flows
Role-Based Access Control (RBAC)Granular permissions across multi-site manufacturing operations
ISO 9001 (Quality Management)Essential for manufacturing quality control and production
ISO 27001 (Information Security)Required for supply chain data protection
SOX ComplianceFinancial controls for inventory valuation and procurement
GDPR ComplianceNeeded for global supply chains with EU exposure
AI Model GovernanceExplainability for forecasting and scheduling decisions
Audit Trail & LoggingComplete transaction history for compliance audits

Implementation Timeline & ROI Benchmarks

Total Implementation Duration
6-18 months
ROI Breakeven Period
6-12 months
Phase 1: Assessment & Data Integration
2-4 months
Phase 2: Model Training & Validation
1-3 months
Phase 3: Pilot Deployment
2-4 months
Phase 4: Full Rollout & Optimization
1-7 months
Typical First-Year Cost Savings
15% logistics + 20-30% inventory reduction
Minimum Historical Data Required
2-3 years

What Vendor Evaluation Criteria Does Elevat Support?

Performance Benchmarks

Proof of 15% Logistics Cost Reduction, 35% Inventory Reduction, 65% Service Level Gains: On Similar Datasets

ERP/MES/WMS Integration

Architecture Diagrams: Connect Rootstock ERP, MES Work Order, and WMS Robotics; Demonstrate

AI Agent Capabilities

Live Demonstrations of Natural Language Inventory Management, Work Order Generation, and Real-Time Inventory Adjustments

Data Requirements

Minimum SKU Count, Transaction Volume, Data Quality Thresholds for Model Training: Specify

Industry Deployments

Manufacturing/Supply Chain Case Studies: Quantified ROI Metrics, Customer References, and Data Sets

Scalability & Real-Time Performance

Concurrent Users? Multi-Site Support? IIoT Data Ingestion Capacity? Uptime SLA's: Specify

Model Monitoring & Retraining

Forecast Accuracy Tracking: What Triggers Model Updates? Performance Degradation Alerts?

Security & Compliance Documentation

Audit Reports, Data Residency Options, SOX Controls, Latest ISO 9001/27001 Certifications: Provide

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