DCconnect builds AI models that predict infrastructure failures, detect thermal anomalies via computer vision, and optimise capacity before problems occur. Purpose-built for enterprise data centre operators.
Enterprise data centres generate millions of telemetry signals per hour. Without AI models trained on this data, critical failures, thermal runaway events, and capacity crunches arrive without warning.
Average unplanned data centre downtime costs $9,000 per minute. Operators relying on manual monitoring and threshold alerts consistently miss the early signals that machine learning models detect hours in advance.
Thermal cameras produce continuous high-resolution streams that human operators cannot monitor at scale. Computer vision models trained on infrastructure imagery identify hotspots, airflow failures, and equipment degradation in real time.
Expansion decisions are made weeks too late or months too early. Data science models trained on historical utilisation, growth patterns, and workload telemetry predict capacity thresholds with 91% accuracy.
Generic monitoring tools cannot learn from the specific infrastructure patterns of a given operator's environment. DCconnect trains models on your proprietary telemetry, building a data moat that general-purpose tools cannot replicate.
DCconnect is purpose-built to answer these challenges.
DCconnect DataOS and DCconnect VisionEdge are developed in parallel to cover the full spectrum of data centre AI: predictive analytics from telemetry data and real-time visual intelligence from camera infrastructure.
An AI data platform for infrastructure telemetry. DataOS ingests millions of sensor readings per hour from servers, power units, cooling systems, and network equipment, trains predictive failure models on operator-specific data, and delivers actionable intelligence via API. Models are built with CUDA-accelerated training pipelines, compiled with TensorRT, and served through Triton Inference Server at sub-400ms latency.
A computer vision application for physical infrastructure monitoring. VisionEdge processes live thermal camera and CCTV feeds from data centre floors, applying deep learning models to detect hotspots, identify equipment anomalies, and flag airflow disruptions in real time. The platform connects to existing camera infrastructure without hardware changes.
DCconnect DataOS training pipelines are implemented as CUDA kernels, enabling massively parallel processing of telemetry data ingestion, feature engineering, and model training directly on GPU hardware. CUDA handles time-series feature extraction, anomaly scoring, and pattern recognition at throughputs CPU frameworks cannot match.
CUDA 12.xcuBLASRAPIDS cuDFBefore production deployment, all DCconnect models pass through TensorRT compilation. Graph optimisation, operator fusion, and INT8/FP16 precision calibration reduce inference latency 6x to 12x on identical hardware. Every failure prediction model and vision model is TensorRT-compiled before serving.
TensorRT 8.6INT8 QuantisationFP16 PrecisionProduction inference across both platforms runs on Triton Inference Server. Triton handles concurrent model execution, dynamic batching, and model repository management. The DataOS predictive engine and VisionEdge thermal models run simultaneously on shared GPU resources, each serving with P95 latency guarantees.
Triton 2.xDynamic BatchinggRPC + REST# DCconnect DataOS — failure prediction pipeline import tensorrt as trt import tritonclient.grpc as triton # TensorRT-compiled failure prediction model builder = trt.Builder(logger) config.set_flag(trt.BuilderFlag.FP16) engine = builder.build_serialized_network( network, config ) # Triton serving — sub-400ms P95 latency client = triton.InferenceServerClient( "dcconnect-triton:8001" ) result = client.infer( model_name="failure_predict_v2", inputs=telemetry_batch, # Returns: confidence, time_to_failure, rack_id )
Hyperscale and enterprise data centre operators use DCconnect DataOS to predict hardware failures 6 to 48 hours in advance and VisionEdge to monitor thermal conditions across thousands of racks simultaneously.
Colo operators serving multiple tenants use DCconnect to provide SLA-backed uptime guarantees. Predictive maintenance intelligence is delivered per-tenant via API, enabling differentiated premium service offerings.
Banks and financial institutions with in-house data centres use DCconnect to meet RTO and RPO commitments. Predictive AI provides early warning before regulatory uptime obligations are breached.
Hospital and healthcare network operators with on-premise infrastructure use DataOS to prevent failures in systems supporting clinical applications where downtime directly affects patient outcomes.
Logistics operators running distributed edge data centres use DCconnect for predictive maintenance of remote infrastructure where on-site engineers are not available and downtime breaks supply chain continuity.
Mission-critical government infrastructure operators deploy VisionEdge for physical security monitoring and DataOS for predictive maintenance across secure facilities where unplanned outages carry national security implications.
Data and Cloud Connect Private Limited was founded in July 2023 in Hyderabad, India. The company was started around one observation: enterprise data centres generate extraordinary volumes of telemetry, thermal, and operational data that almost universally go unused for predictive intelligence.
DCconnect builds AI platforms that turn this data into operational intelligence. DataOS trains predictive failure models on operator-specific telemetry. VisionEdge applies computer vision to physical monitoring via existing camera infrastructure. Together, they give operations teams the ability to act before failures happen rather than respond after.
Both platforms are in active development, funded by the company founders. DCconnect is bootstrapped and operationally independent, building on managed hyperscaler infrastructure to train and serve models at enterprise scale.
Data and Cloud Connect Private Limited incorporated in Hyderabad. Research into infrastructure telemetry AI and thermal vision models begins.
DCconnect DataOS architecture finalised. CUDA-accelerated telemetry ingestion pipeline prototyped and validated. TensorRT failure prediction models benchmarked at 8.4x inference improvement.
Engineering team expanded to 11-25. Model training infrastructure provisioned on managed hyperscaler cloud. Triton Inference Server deployment completed and production-validated.
DCconnect VisionEdge development started. Thermal anomaly detection models trained on infrastructure camera datasets. cuDNN vision pipeline integrated and benchmarked.
Early pilot deployments initiated with enterprise data centre operators. Predictive failure model accuracy validated at 91%+ on held-out telemetry data. Scaling infrastructure capacity.
Leads DCconnect product and technical strategy. Background in enterprise infrastructure operations and AI systems design with a focus on building intelligent platforms for data centre and infrastructure operators.
Infrastructure AIProduct StrategyOversees DataOS and VisionEdge engineering. Deep expertise in CUDA kernel development, TensorRT optimisation, and Triton Inference Server deployment. Leads the telemetry AI and computer vision model development teams.
CUDATensorRTComputer VisionMachine learning engineers focused on predictive failure model architecture, thermal vision model training, and time-series anomaly detection research across enterprise infrastructure environments.
Predictive AIVision ModelsWe are building AI infrastructure intelligence platforms for enterprise data centre operators. If you have expertise in CUDA programming, ML systems, or computer vision, reach out.
Whether you are evaluating DCconnect DataOS for predictive maintenance, exploring VisionEdge for thermal monitoring, or requesting a live demonstration, reach out and our team will respond within one business day.
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