CloudFactory provides an enterprise AI platform and AI consulting, combining automation with a managed human-in-the-loop workforce to create high-quality AI datasets, train and align models, and validate/correct inference so AI can be trusted in production.
CloudFactory is an AI consulting and AI platform provider focused on helping organizations develop, deploy, and operate trusted AI in production. The company combines AI-powered technology, automation, and a managed human-in-the-loop workforce to turn messy, unstructured, or incomplete data into high-quality datasets; to fine-tune and validate models; and to provide inference evaluation, validation, and error handling where reliability matters. Founded in 2010 in Kathmandu, Nepal, CloudFactory was built on the belief that talent is equally distributed, but opportunity is not. Over time it developed a repeatable approach to hiring, training, and managing a global AI workforce and delivering accountable, high-quality outcomes for customers. CloudFactory states it has helped over 700 clients build high-quality datasets and high-performing models and launch AI solutions. Following its acquisition of Hasty (an inference-centric AI data platform), CloudFactory positions itself as expanding beyond data labeling to support the full AI lifecycle, including inference-centric operations and continuous improvement through human-in-the-loop feedback. The platform is organized into modular engines—Data Engine, Training Engine, Inference Engine, and AI Engine—paired with enterprise AI consulting services (advisory, discovery, design & build) to move AI from idea/pilot to scalable production with governance, monitoring, and oversight.
HC score
verified business cases
Consulting services spanning advisory/strategy, discovery (blueprint), and design & build to translate AI vision into scalable, secure, and compliant AI implementations.
AI strategy
Discovery workshops
Solution design
Operate AI in production with continuous monitoring, observability, risk/incident response, infrastructure optimization, and governance enforcement across cloud or on-prem environments.
Model monitoring
Observability
Risk management
Enterprise-ready platform that integrates data preparation, model lifecycle management, inference oversight, governance, and scalable deployment for AI applications.
Lifecycle management
Inference oversight
Governance
Data collection, curation, and annotation services to deliver AI-ready datasets with diversity, metadata enrichment, and AI-assisted pre-labeling.
Data collection
Data curation
Data annotation
Continuous inference validation, error handling, and evaluation to maintain trust, auditability, and performance in production AI predictions.
Inference validation
Error handling
Inference evaluation
Model training support including prompt engineering, supervised fine-tuning, red teaming, and reinforcement learning from expert feedback to improve safety and alignment for high-stakes AI.
Prompt engineering
Fine tuning
Red teaming
Direct
LineVision and CloudFactory are working together to help utilities reach a clean energy future, faster. We appreciate having a resource to focus on calibrating our data and making sure it’s accurate. Our partnership has allowed us to accelerate that work.
VP, AI & Computer Vision
CloudFactory has been working with us from the beginning to understand how we build a scalable model, how we become faster and more efficient, and how we get better at increasing the support we give our clients.
Company Founder
The space that I’m in is going through an evolution. If you don’t have AI capabilities, you’ll be left behind. But I can’t focus on the product if I’m swamped doing people management. CloudFactory takes that burden off of us.
VP, AI & Computer Vision
With a trained team, you get something you simply can't with crowdsourcing—accountability. In retrospect, this has had a huge impact for us, because the biggest limiting factor on the performance of the models is actually the quality of the labels, and how precise the definitions are.
A healthcare-focused initiative sought to improve patient care while strengthening financial performance. The customer needed to better understand nurse experiences to address operational and staffing challenges. These issues had impacted both care delivery and financial outcomes. The customer learned from nurses and applied AI-powered insights to identify improvement opportunities. The initiative implemented operational and staffing changes based on those insights. The approach aligned workforce decisions with patient care and financial objectives. The initiative delivered measurable improvements in financial performance and workforce stability. EBITDA increased by 137%. Nurse turnover decreased by 44%. These results supported stronger operations and improved ability to maintain patient care quality.
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MV Transportation
MV Transportation faced significant driver turnover and needed measurable improvements across the operator lifecycle. The organization required a way to strengthen retention while maintaining a strong focus on safety. It needed results that could be tracked and sustained over time. MV Transportation implemented an AI-driven approach to optimize driver retention and safety. The solution was applied across the operator lifecycle to identify opportunities to improve outcomes. This approach helped convert turnover challenges into a more structured, data-informed strategy. MV Transportation achieved a 50% improvement in driver retention. The initiative also reduced safety incidents, though no specific incident reduction figure was provided. These outcomes helped turn turnover into a competitive advantage.
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Financial Services Company
A financial services company faced slow document transcription that took days to complete. They needed to speed up turnaround without sacrificing quality. The existing process could not meet the required pace and reliability. They implemented a managed workforce to accelerate document processing and verification. This approach increased the capacity to transcribe documents quickly while maintaining quality controls. Verification steps were used to ensure outputs met the required standards. Turnaround time improved from days to just minutes. The work was completed with 99% accuracy. Faster processing enabled the company to handle document transcription more efficiently while maintaining high quality.
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Medical AI Company
A medical AI company needed to rapidly create labeled training data to stay ahead of the curve. They faced pressure to build a sizable dataset within a fixed timeline. They needed labeled data quickly to support their medical AI efforts. They scaled image annotation production to increase labeling capacity. They used this scaled approach to create the training dataset within the required timeframe. They implemented a process focused on producing labeled images efficiently. They labeled 24,000 images in 6 months. They built a sizable dataset on a fixed timeline. They met the timeline needed to support their training-data goals.
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Bizly
Bizly needed more capacity to broaden coverage of its event planning platform. The existing setup was not sufficient to support the required data operations at the needed pace. This limited how quickly the platform could expand its venue listings. Bizly implemented a dedicated workforce to support its data operations. This added the operational capacity required to handle the work involved in expanding platform coverage. The approach focused on accelerating expansion by scaling execution through dedicated support. With the additional capacity in place, Bizly accelerated its expansion efforts. The platform scaled its coverage to over 18,000 venues. This outcome reflected the increased ability to execute data operations at scale.
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Tractor Zoom
Tractor Zoom sought to scale its agtech marketplace operations through better data enrichment. The company faced a need to improve data enrichment capacity to support growing marketplace demands. Without expanded enrichment capability, higher marketplace throughput was harder to sustain. Tractor Zoom implemented an expanded data enrichment capability to improve its marketplace operations. The enrichment work was used to better support listings and overall marketplace throughput. This approach increased the company’s ability to handle greater operational volume. With the expanded data enrichment capability in place, Tractor Zoom supported higher marketplace throughput. The company grew from advertising $2B in assets annually to $5B. This growth reflected the impact of improved data enrichment on marketplace scale.
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Allvision
Allvision needed to execute an ambitious smart city go-to-market plan on an accelerated timeline. They faced significant computer-vision data needs while working against a compressed schedule. They required a way to move faster without compromising their go-to-market objectives. Allvision implemented a flexible partnership to support their computer-vision data needs. The engagement was structured to adapt to changing requirements and timelines. This approach helped Allvision accelerate execution of their smart city go-to-market plan. Allvision sped up execution of their smart city go-to-market plan. They achieved their smart city GTM mission 8x faster. The partnership enabled the pace required to meet the accelerated timeline.
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Ocuvera
Ocuvera aimed to advance its human-centric healthcare mission by reducing unassisted bed exits. Unassisted bed exits posed a challenge to patient safety outcomes. The organization needed a way to accelerate progress toward its mission. Ocuvera implemented a supported workflow to accelerate progress toward reducing unassisted bed exits. The approach focused on enabling the team to move faster toward its patient safety goals. The workflow provided support to advance the initiative. The engagement resulted in an 89% reduction in unassisted bed exits. This reduction improved patient safety outcomes. The results helped Ocuvera progress toward its human-centric healthcare mission.
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Heavy Equipment Rental
A heavy equipment rental client needed to detect more damage in its fleet while accelerating processing. Existing methods required significant labor and slowed turnaround. The client also needed to reduce the burden on its workforce without sacrificing detection quality. The client implemented custom AI with human-in-the-loop workflows. These workflows combined automated detection with human review to improve accuracy and speed. The approach streamlined how damage was identified and processed. The implementation delivered measurable improvements across identification, turnaround time, and labor costs. The client identified 3.5x more damage than before. Turnaround became 66% faster, and workforce costs were reduced by more than 50%.
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Full service creative production company helping brands maximise the impact of their marketing content
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Empowering US startups with unrivaled access to global engineering talent, seamless hiring, and improved retention.
Human Cloud Verification ensures that the listed end customer is verified. It's used across kudos, customers, and business cases, and performed by Human Cloud. Think about it like a background check.
An independent global marketing consultancy delivering outsized growth.
Human Cloud Verification ensures that the listed end customer is verified. It's used across kudos, customers, and business cases, and performed by Human Cloud. Think about it like a background check.
