ATRI develops, trains, optimizes and deploys AI and machine-learning systems across cloud, edge and private infrastructure — and builds the applications and products that put that intelligence to work.
Our capabilities span model engineering, computer vision, video analytics, language and speech, generative AI, edge AI, MLOps, private AI, cloud AI and agentic systems.
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Model architecture/development • training • evaluation • inference • optimization • domain-specific models • low-resource/localized-language models
Computer vision • object detection • classification • segmentation • tracking • video analytics • event/anomaly detection • predictive analytics derived from video and physical-world data
Language understanding/generation • speech recognition • keyword spotting (KWS) • Whisper • SLMs • localized-language models • encoder architectures • GRUs
GPU/accelerator inference • local AI processing • model optimization • quantization • latency/memory/compute optimization • edge/cloud partitioning
Cloud training/inference • CSP-native AI services • private model deployment • on-premises/private-cloud AI • GPU infrastructure • model serving • enterprise integration
Specialized AI agents • RAG • contextual architectures • domain-specific knowledge • agent orchestration • multi-agent systems • privately deployed agents • human/AI collaboration • workflow/action integration
Finance • supply chain • manufacturing • healthcare • compliance • education and training • physical-world/video intelligence
Problem · data · objectives · model/deployment constraints
Architecture · model development · data preparation · training
Accuracy · behavior · robustness · performance · application suitability
Quantization · compute/memory optimization · latency · hardware-aware optimization
Cloud · private infrastructure · edge · GPU/accelerator · embedded platforms
Inference · monitoring · model/version management · retraining · continued improvement
ATRI builds and supports the infrastructure and operational workflows required to move models from development into reliable use — end-to-end MLOps, not just deployment.
Training pipelines • experiment/model management • model registries • deployment pipelines • model versioning • inference infrastructure • model serving • monitoring • retraining • scaling • GPU infrastructure • cloud/edge deployment • CI/CD for ML
ATRI works with the AI/ML infrastructure, managed services and model ecosystems of leading cloud-service providers for model training, deployment, inference and application integration.
Managed training/deployment • computer vision • speech/language services • foundation-model services • managed inference • GPU infrastructure • MLOps
ATRI deploys and optimizes AI models on edge-compute platforms so inference can occur close to cameras, sensors, equipment and physical systems.
NVIDIA Jetson • Blackwell-based NVIDIA platforms • Qualcomm • AMD • STM32 • Raspberry Pi • GPU/accelerator inference • quantization • local inference • performance optimization • computer vision/video analytics at the edge
Data captured at the source — imaging, audio or physical-world sensor data.
NVIDIA Jetson, Blackwell-based platforms, Qualcomm, AMD, STM32 or Raspberry Pi running close to the data.
A quantized, optimized model sized to the compute, memory and latency budget of the platform.
Local inference producing a result without a round trip to the cloud.
The inference result becomes a decision, event or anomaly flag.
Results are reported, aggregated or acted on through a connected system, cloud service or application.
ATRI designs and deploys private AI environments for organizations requiring greater control over models, data, infrastructure and inference.
Private/local model deployment • on-premises/private-cloud AI • GPU infrastructure • model serving • inference optimization • quantization • model lifecycle management • application/API integration
ATRI designs agentic architectures in which specialized AI agents operate with defined roles, domain knowledge, contextual information and access to appropriate tools and workflows. Agents can operate individually or collaborate within multi-agent systems supporting analysis, decision-making and execution alongside human teams.
ATRI experience includes specialized agents, multi-agent orchestration, RAG, contextual architectures, domain-specific knowledge, context retrieval, private/local agents, enterprise/organizational agent systems and workflow integration.
ATRI has deployed multi-agent architectures in healthcare where different agents represent specialized knowledge domains and use contextual/RAG architectures as part of a broader application — supporting analysis and information organization, not autonomous diagnosis or treatment.
ATRI also has internal experience with privately deployed organizational agent environments aligned to distinct functional roles.
ATRI's AI capability does not stop at the model. We engineer applications in which AI is part of the fundamental product architecture and workflow.
Depending on the application, ATRI can develop a model, use and optimize an open model, integrate a commercial/frontier model, use a CSP-native AI service, or combine approaches. Architecture decisions weigh accuracy, latency, compute, memory, power, privacy, data, security, connectivity, cost and deployment environment.
The model is one part of the product architecture — not the product itself.
ATRI can engineer the intelligence, the infrastructure that runs it and the software or physical product that puts it to work.
ATRI's AI & ML teams can engage when a customer needs to:
From model development and MLOps to edge inference, private AI and agentic applications, ATRI brings together the models, infrastructure, software and product engineering required to put intelligence to work.
Talk to ATRI →