Straive

straive.com

Straive combines technology delivery with operational services rather than operating solely as an advisory firm or software vendor. Its technical scope includes LLM customization, enterprise AI integration, AWS-based development, data infrastructure, analytics, and legacy-platform modernization. Separate offerings cover customer-experience consulting, reusable AI toolkits, and human-delivered support operations. Buyers should specify whether an engagement covers advice, implementation, production operations, or a combination, rather than treating every Straive offering as technology consulting. ^[1] ^[2] ^[3] ^[4] ^[5] ^[6]

Services and delivery

Custom AI development and deployment

Implementation. Develops custom AI solutions using prompt engineering and LLM fine-tuning, then integrates them into existing systems and workflows. Supporting deliverables include AI infrastructure, data foundations, and monitoring integration. ^[1]

AI monitoring and retraining

Managed services. Provides continuing oversight of deployed AI through expert review, exception handling, and model retraining. Support hours, escalation responsibilities, and service levels require contractual definition. ^[2]

Legacy-platform modernization

Implementation. Integrates legacy technologies into AI-powered platforms and redesigns workflows and interfaces. This is a distinct modernization offering, not evidence that every outsourced operations engagement includes software engineering. ^[2]

AWS AI and generative-AI implementation

Implementation. Builds and operationalizes AI and machine-learning solutions using Amazon SageMaker, and develops enterprise generative-AI solutions using Amazon Bedrock. ^[3]

AWS architecture and infrastructure design

Advisory. Designs AWS architecture and infrastructure for enterprise workloads. Buyers should establish the boundary between architecture design, infrastructure build, and subsequent operations. ^[3]

AWS data migration and analytics

Implementation. Delivers data migration and modernization, with real-time analytics using Amazon Kinesis and Amazon Redshift. These data services do not inherently include machine-learning development. ^[3]

Data architecture and engineering

Implementation. Builds data infrastructure and processing foundations, with architecture, engineering, and data-quality controls supporting collection, storage, processing, and analysis. ^[7]

BI and advanced analytics

Implementation. Delivers BI reporting, visualization, predictive and prescriptive modeling, and knowledge graphs. The specific modeling scope determines whether an engagement includes machine learning. ^[8]

Data and AI system testing

Managed services. Provides pre-production testing and continuing performance monitoring for AI and data systems. Buyers should define evaluation criteria and the systems covered. ^[8]

Speech and LLM call analytics

Implementation. Implemented audio transcription, LLM-based intent and theme analysis, call classification, and dashboards for a sports-club service center. ^[9]

Computer-vision implementation

Implementation. Implements image recognition for asset identification, including object recognition, image classification, enrichment, and delivery into data pipelines. ^[10]

Content-repository migration and maintenance

Implementation. Migrates publishing content into a Single Source Repository, integrates information-provider content, and provides product maintenance. ^[11]

Customer-experience advisory

Advisory. Provides CX maturity assessments, journey mapping, strategy design, and implementation roadmaps. These business-experience deliverables are separate from technical platform implementation. ^[4]

Agentic AI toolkits

Software product. Offers DocExplore, DocSearch, DataChat, LLM Foundry, and App Maker for document analysis, conversational access, reusable model components, and prompt-driven application creation. Standalone commercial terms are unverified. ^[5]

Royalty-support operations

Non-technical service. Runs business-process support covering identity verification, statement and contract analysis, resolution, escalation, record updates, and quality controls. ^[6]

Company details

Industries and client fit

Documented use cases include enterprise AI for a media and streaming company, repository migration for a publisher, call analytics for a US sports club, and image-based asset identification for a transportation-technology provider. These examples demonstrate specific delivery contexts rather than universal sector coverage. ^[12] ^[11] ^[9] ^[10]

Track record

For an unnamed media and streaming enterprise, Straive reports implementing an integrated AI platform with content analysis, document intelligence, a governance portal, a secure experimentation sandbox, and agentic workflows. It reports approximately 72% less content-review time and 60–70% faster document-analysis workflows. ^[12]

In the sports-club call-center case, Straive reports $1.1 million in incremental revenue and a 42% reduction in call abandonment following transcription, classification, and dashboard implementation. These are case-specific, company-reported results, not independently verified forecasts. ^[9]

Scope and limitations

This profile uses only the supplied research notes; downloadable attachments were not used to establish additional capabilities.
Selected AI case studies describe clients without naming them, limiting reference verification. Reported outcomes remain Straive’s claims.
Toolkit licensing, ownership, hosting, and standalone purchase terms are not established by the supplied evidence.
Support hours, service levels, production responsibilities, and detailed computer-vision evaluation results require confirmation.

Questions to ask

Who owns model development, integration, production release, and ongoing operations?
Which comparable customer references and evaluation results can be disclosed?
What cloud, model, data-residency, and proprietary-tool dependencies apply?
Who owns custom code, prompts, evaluation datasets, and fine-tuned model artifacts?
What monitoring, retraining, escalation, support SLAs, and exit assistance are included?

Overview