Inference
Serverless GPU router
API gateway that routes LLM requests to the cheapest available cluster based on SLA requirements
↓ 60% compute costs
Build AI infrastructure that scales with your workloads, optimizes compute costs, and accelerates deployment. We design MLOps foundations with monitoring, automation, and developer workflows that help teams ship, manage, and scale production AI systems efficiently.
We deliver infrastructure that treats models like code. Three commitments we make before writing any infrastructure configs.
We configure dynamic batching and autoscaling clusters so you only pay for active inference. Workloads scale to zero during off-hours and burst immediately when traffic spikes.
We eliminate the DevOps bottleneck. Data scientists can push a model from a local Jupyter notebook to a production API endpoint with automated containerization and testing.
Not just CPU/RAM metrics. We instrument your inference pipelines to track latency per token, data drift, model degradation over time, and exactly how much each API call costs.
Most ML infrastructure fails because it's built by software engineers who don't understand tensor operations, or data scientists who don't understand networking.
We align our technical approach with the distinct operational and scale constraints of infrastructure owners.
Across platforms. Each with a specific scaling mechanism and a specific performance metric.
API gateway that routes LLM requests to the cheapest available cluster based on SLA requirements
Nightly pipeline that detects data drift, triggers retraining, and shadows the new model
Redis-backed feature store serving pre-computed fraud signals to models in <10ms
Internal developer portal allowing data scientists to spin up GPU-backed Jupyter nodes
Ray cluster orchestration across multi-node setups for large-scale embedding generation
Custom Grafana/Prometheus setup tracking statistical degradation in recommendation models
Migrated PyTorch inference to optimized vLLM engine with INT8 quantization
Fully containerized LLM deployment on disconnected on-premise hardware for a defense contractor
API gateway that mirrors live traffic to experimental models without impacting user experience
From messy scripts to automated, scalable infrastructure.
Review current compute spend, deployment bottlenecks, and model serving latency. Draft the target state architecture across Kubernetes, Ray, or serverless platforms.
Establish the foundational Kubernetes clusters, configure GPU node pools, and setup the developer workspaces (e.g. Kubeflow/MLflow).
Build automated pipelines that package models into containers. Deploy high-throughput serving engines (vLLM, Triton) and configure dynamic autoscaling.
Implement Grafana dashboards tracking drift, latency, and cost. Conduct comprehensive training with your DevOps and Data Science teams.
We don't force technologies. We choose the stack that best fits your AI workflows, scalability goals, integrations, and long-term product vision.
Carefully crafted AI-powered platforms designed to deliver real business impact, seamless user experiences, and intelligent automation across industries.
Deep teams with industry context - not generalists googling compliance acronyms. Each industry below has 30+ shipped projects and a partner who knows the regulator.
Telemedicine, EHR/EMR, claims automation, clinical decision support. HIPAA, HL7/FHIR, GDPR. Active partnerships with 14 hospital networks.
Core banking, neobank, payments, lending, KYC, fraud. PCI DSS, RBI sandbox, Open Banking, ISO 20022. We've shipped to Tier-1 banks in 4 countries.
Headless commerce, marketplace, omnichannel, AR try-on, AI recommendations. Shopify Plus, BigCommerce, custom. 22+ storefronts live with avg +34% AOV.
Last-mile optimisation, TMS, WMS, fleet IoT, route prediction, real-time tracking. Shipped to UPS, Alod and 11 other logistics operators.
OTT platforms, content recommendation, real-time encoding, multi-DRM, distribution at network scale. Sony Pictures, Hello Baby Direct and more.
LMS, adaptive learning, AI tutors, government portals. Shipped UKIERI for the British Council and 6 state-government education portals.
Real names, real companies, real numbers. Video on the left, written notes on the right - choose whichever feels more honest.
Although regulations prevented the site's launch, it met all requirements in terms of form and function. Fullestop's project plan charted a clear course to completion. The team's flexible, diverse talent pool enabled them to manage each stage of the project with consistent levels of skill.
Weekly demos, no surprises, and they push back when we're wrong. That last part is rare. Cut our cloud bill 47% in the first audit.
We constantly come up with top-tier resources and breathtaking
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Fullestop designs modular, cloud-native AI architectures using platforms like Google Vertex AI and LangChain, enabling seamless scalability tailored to your evolving business needs. Rewrite this answer in general way
We serve diverse sectors including healthcare, logistics, e-commerce, education, and social networking, delivering customized AI solutions aligned with industry-specific challenges.
Our technology-agnostic approach ensures smooth integration of AI tools into your current cloud or on-premise infrastructure, minimizing disruption and maximizing ROI. Add API interfacing as well, and improve the answer.
Reliable infrastructure supports efficient data pipelines, model management, and scalable compute resources, transforming AI prototypes into operational, secure business applications.
MLOps automates and governs model training, deployment, and monitoring, ensuring AI stays accurate, efficient, and scalable throughout its lifecycle .
Vector stores enable fast semantic search and content retrieval by indexing embeddings, which is crucial for building personalized, context-aware AI systems.
We offer continuous monitoring, maintenance, and iterative improvements to keep your AI solution secure, performant, and aligned with your business goals.
