Presear architects cloud-native AI infrastructure — training clusters, serverless inference, multi-cloud pipelines, and cost-optimised MLaaS — across AWS, GCP, and Azure.
Technical Depth
From serverless inference to multi-cloud orchestration — we match the right cloud architecture to your AI workload and budget.
Deploy ML models without managing servers — auto-scaling inference endpoints that spin up on demand, handle traffic bursts, and scale to zero during idle periods. We configure Lambda, Cloud Run, and Azure Functions for sub-second cold starts and cost-efficient pay-per-call pricing models, eliminating idle compute waste.
Orchestrate GPU and TPU training clusters that scale dynamically with job size — launching spot/preemptible instances for large training runs and releasing them upon completion. We use Kubernetes node pools, managed training services, and spot interruption handlers to maximise throughput while minimising cost per training run.
Architect AI workloads across AWS, GCP, and Azure simultaneously — routing training jobs to the cheapest GPU pool, keeping inference close to end users, and eliminating single-cloud lock-in. We implement Terraform-based multi-cloud IaC, cross-cloud VPN peering, and unified observability so you retain control across every provider.
Build fully managed, event-driven ML pipelines using cloud-native tooling — Vertex AI Pipelines, SageMaker Pipelines, or Azure ML Pipelines — with automated data ingestion, model training, evaluation gates, and registry promotion. Every pipeline is versioned, reproducible, and integrated with CI/CD systems for gated model releases.
Accelerate delivery by integrating cloud-native AI APIs — Vision AI, Speech-to-Text, Translate, Rekognition, Comprehend — into your applications without building models from scratch. We architect hybrid solutions that combine managed APIs for commodity AI tasks with custom models where differentiation matters, balancing speed and proprietary advantage.
AI workloads are notoriously expensive if left unmanaged — GPU idle time, over-provisioned endpoints, and unoptimised data transfer costs can balloon monthly bills. We implement FinOps practices specifically for AI: spot instance scheduling, inference autoscaling policies, right-sizing, data transfer optimisation, and tagging frameworks for chargeback attribution.
Our Process
A rigorous five-stage process. Click any step to explore what happens — and why it matters.
We begin with a thorough assessment of your AI workloads, data locality requirements, compliance constraints, and existing cloud footprint. From this, we design a reference architecture — specifying compute, storage, networking, and IAM topology across one or multiple clouds — before writing a single line of infrastructure code.
AI is only as good as its data supply chain. We build cloud-native ingestion pipelines — streaming via Kafka or Pub/Sub, batch via Airflow or Step Functions — feeding cleaned, versioned data into object storage (S3/GCS/Blob) and feature stores that training and inference jobs can reliably consume at any scale.
We configure distributed training jobs on managed ML platforms — handling GPU cluster provisioning, distributed data loading, checkpoint storage, and experiment tracking. For large models, we implement model parallelism and gradient checkpointing strategies. Spot instance interruption handling ensures training jobs complete without data loss or cost overruns.
Model serving is where latency, cost, and reliability converge. We deploy inference endpoints using SageMaker Endpoints, Vertex AI Prediction, or custom Kubernetes-based serving stacks — with autoscaling policies, canary rollout configurations, and multi-region routing for high availability. Models are containerised with TorchServe, TF Serving, or FastAPI depending on latency requirements.
Once live, we establish continuous observability across model performance, infrastructure health, and cloud spend. Prometheus and Grafana dashboards surface latency, throughput, and prediction quality. FinOps tagging frameworks enable per-model cost attribution and budget alerts, ensuring infrastructure costs remain predictable as workloads scale.
Real-World Impact
Production cloud AI deployments across industries — each delivering measurable outcomes from day one.
Core Challenge
Large retailers need SKU-level demand forecasts updated daily across hundreds of thousands of products — a compute and data engineering challenge that overwhelms on-premise infrastructure and requires elastic cloud scaling to process within overnight batch windows.
Who Benefits
Retail chains, e-commerce platforms, and FMCG distributors that run large-scale replenishment planning and need cloud AI infrastructure capable of ingesting POS, promotion, and external data signals to generate daily forecasts at SKU-location granularity.
Request Case StudyCore Challenge
Healthcare AI models must serve predictions reliably with high availability, strict data residency compliance, and audit logging — requirements that make standard cloud deployments insufficient without careful architecture and HIPAA/DPDP-aligned configuration.
Who Benefits
Hospitals, health-tech platforms, and diagnostic companies that need compliant cloud AI inference for clinical decision support — with full audit trails, private VPC deployment, and guaranteed uptime SLAs that meet healthcare operational standards.
Request Case StudyCore Challenge
Financial institutions need to run computationally intensive risk simulations and credit scoring models on demand — with burst capacity for end-of-day batch runs and real-time inference for transaction-level scoring — without maintaining idle GPU infrastructure year-round.
Who Benefits
Banks, NBFCs, and fintech lenders that need elastic compute for model training during regulatory reporting cycles and low-latency inference endpoints for real-time credit and fraud scoring integrated into their transaction processing systems.
Request Case StudyCore Challenge
Media platforms need to process, tag, transcribe, and moderate large volumes of video and audio content at scale — tasks that require GPU-accelerated inference on elastic cloud infrastructure capable of handling unpredictable content upload spikes without manual provisioning.
Who Benefits
Streaming platforms, news agencies, and content marketplaces that ingest user-generated or licensed content and need automated AI processing pipelines for transcription, content moderation, metadata enrichment, and clip segmentation at cloud scale.
Request Case StudyPowered By
Industry-leading cloud platforms, orchestration tooling, and observability frameworks — chosen for reliability, cost transparency, and enterprise-grade security.
Frequently Asked
Answers to the questions engineering leaders and CTOs ask before starting a cloud AI engagement with Presear Softwares.
Ask Our Cloud AI TeamPartner with Presear Softwares to build cloud-native AI infrastructure that scales elastically, stays cost-efficient, and runs reliably at production grade.