List of Amazon Transcribe Customers
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Since 2010, our global team of researchers has been studying Amazon Transcribe customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Amazon Transcribe for Speech Recognition AI from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Amazon Transcribe for Speech Recognition AI include: NJ Transit, a United States based Transportation organisation with 12500 employees and revenues of $1.10 billion, Octopus Energy, a United Kingdom based Utilities organisation with 100 employees and revenues of $10.0 million and many others.
Contact us if you need a completed and verified list of companies using Amazon Transcribe, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Amazon Transcribe customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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NJ Transit | Transportation | 12500 | $1.1B | United States | Amazon Web Services (AWS) | Amazon Transcribe | Speech Recognition AI | 2023 | n/a |
In 2023, NJ TRANSIT implemented Amazon Transcribe, in the category. The work was provisioned using Terraform to define AWS resources and transcription pipelines, with an architecture designed for scalability and cost-efficiency.
Cloud Engineer Interns developed PowerShell scripts and AWS Lambda functions to automate operational tasks and orchestrate transcription jobs against Amazon Transcribe, creating serverless processing pipelines for ingestion, job control, and result handling. Configuration emphasized reusable Terraform modules and explicit IAM role definitions to standardize deployments and reduce manual configuration across environments.
Project portfolio management was maintained through project management software, ensuring data integrity and secure handling of project information and providing governance oversight for deployment activities. The implementation activity occurred on-site in Newark New Jersey during the internship period and primarily impacted cloud engineering and operations functions. Integrations were executed within the AWS ecosystem, with Lambda functions and automation scripts bridging orchestration workflows and the Amazon Transcribe service.
The recorded implementation outcomes stated in project notes included ensured scalability and cost-efficiency, enhanced operational transparency and efficiency, and secure handling of project data. Amazon Transcribe was instrumented as the core speech-to-text engine within a Terraform-provisioned, AWS-centric architecture.
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Octopus Energy | Utilities | 100 | $10M | United Kingdom | Amazon Web Services (AWS) | Amazon Transcribe | Speech Recognition AI | 2018 | n/a |
In 2018, Octopus Energy implemented Amazon Transcribe. The deployment used Amazon Transcribe in the Apps Category .
Implementation centered on speech to text transcription workflows common to automatic speech recognition, with configuration for both batch and real-time streaming transcription. Amazon Transcribe was used to produce time aligned transcripts, apply speaker diarization and timestamps, enable punctuation and formatting, and incorporate custom vocabulary and vocabulary filtering to address industry specific terms.
The Octopus Energy case study and demo focused on customer voice channels and internal audio analysis use cases, highlighting how to improve ASR accuracy through custom vocabularies, sample audio tuning, and noise handling settings. Operational scope discussed in the demo emphasized ingesting call and event audio into transcription pipelines and validating transcript quality for downstream analytics and operational review.
Governance and rollout discussion documented during the engagement recommended establishing transcript quality validation processes, version control for custom vocabularies, and a phased pilot to validate accuracy before broader adoption. Next steps captured in the case study included iterative tuning of Amazon Transcribe configurations, formalizing automated transcript quality checks, and operationalizing transcripts for analytics and operational workflows.
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