WaqoorAI

End-to-end platform to build, train, evaluate, and deploy LLMs & deep-learning apps—on-prem or cloud.

AI platform LLM deep learning model training inference RAG vector database retrieval MLOps LLMOps Kubernetes on-premises hybrid cloud multi-tenant RBAC observability monitoring GPU parallelism chat agents embeddings evaluation
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About

WaqoorAI is an enterprise AI platform that unifies classic ML, deep learning, and generative AI to accelerate the full lifecycle—from data and training to governed deployment and continuous operations—across on-prem, private cloud, and hybrid Kubernetes. It delivers production-grade serving (low-latency inference, autoscaling, heterogeneous CPU/GPU scheduling, A/B and canary routing), end-to-end observability and FinOps, and rigorous security, compliance, and multi-tenancy. Its agentic capabilities power autonomous and collaborative agents for planning, multi-step reasoning, tool/function calling, and safe actioning under policy, RBAC, and audit. Enterprise knowledge and RAG pipelines, a model registry with CI/CD promotion, and integrated evaluation turn experimentation into measurable outcomes. Developers build fast with SDKs, REST and gRPC APIs, and OpenAI-compatible endpoints—plus first-class code support—for seamless app integration and interoperability.

Business Benefits

Governed AI Lifecycle

Orchestrate the full AI lifecycle—data prep, training, evaluation, registry, and CI/CD—under policy controls and gated promotion to production for consistent, audit-ready releases.

High-Performance Serving

Deliver low-latency inference with autoscaling, CPU/GPU and multi-GPU scheduling, and intelligent traffic management (A/B and canary) to meet real-time workloads at scale.

Agentic Automation

Build autonomous and collaborative agents with planning, multi-step reasoning, tool/function calling, and safe actioning—governed by RBAC, policies, and full audit trails.

Enterprise Knowledge & RAG

Connect to enterprise data with out-of-the-box connectors, chunking and embeddings, vector stores, caching, and evaluation dashboards to ground models in authoritative context.

Open Integration & APIs

Integrate fast with SDKs, REST/gRPC services, and OpenAI-compatible endpoints; first-class code support enables rapid app development and seamless interoperability.

Security, Compliance & Tenancy

Enforce SSO/OAuth/SAML, RBAC, hierarchical org/projects, encryption in transit and at rest, data retention controls, optional PII redaction, and strict tenant isolation.

Observability & FinOps

Track metrics, traces, drift and quality; monitor token/GPU usage and costs; set alerts and feedback loops to optimize performance and ROI across teams and environments.

Flexible Deployment

Run on-premises (including air-gapped), in private cloud, or hybrid via Kubernetes/Helm—standardized, portable, and enterprise-ready.

Key Features

  • Unified AI Lifecycle

    Plan, build, evaluate, register, and promote models through gated CI/CD with traceable lineage and approvals, ensuring controlled, audit-ready releases across environments.

  • Agentic Orchestration

    Create autonomous and collaborative agents that plan tasks, call tools and APIs, orchestrate multi-step workflows, and execute safely under policies, RBAC, and audit logs.

  • High-Performance Inference

    Serve models with low latency using autoscaling, CPU/GPU and multi-GPU scheduling, and intelligent traffic management including A/B tests, canary releases, and blue-green swaps.

  • Developer SDKs & APIs

    Integrate rapidly via REST, gRPC, and OpenAI-compatible APIs and first-class SDKs for Python and JavaScript; webhook and event streams enable reactive, decoupled application patterns.

  • Enterprise Knowledge & RAG

    Index enterprise data with connectors, chunking, embeddings, and vector stores; build retrievers with caching and evaluation dashboards to measure relevance and response quality.

  • Security & Compliance

    Enforce SSO/OAuth/SAML, fine-grained RBAC, tenant isolation, encryption in transit and at rest, configurable retention, rate limiting, and optional PII redaction.

  • Observability & FinOps

    Monitor metrics, traces, and logs alongside token/GPU utilization and cost; detect drift and quality regressions and trigger alerts and feedback loops for continuous improvement.

  • Flexible Deployment

    Deploy on-premises (including air-gapped), in private cloud, or hybrid via Kubernetes/Helm; standardized packaging ensures portability and consistent performance.

  • Governance & Policy Controls

    Apply policies across projects and environments, require approvals for sensitive actions, and maintain immutable audit trails for compliance and operational accountability.

  • Meet Scalability, Fixability, and SLAs

    Bring your own datasets, storage, and schedulers; integrate with Kubernetes/Helm for policy-driven placement, autoscaling, and failover. Mix nodes of different sizes/vendors while maintaining reproducibility, auditability, and SLAs

  • Customize Your Environment

    Run training entirely on your own infrastructure—air-gapped or on-prem—with strict data isolation. Scale horizontally and vertically across CPUs/GPUs, leverage distributed training, and elastically allocate resources for performance and cost efficiency.

  • Self-Guided, Intuitive Workbench

    A no-code, guided platform that makes AI easy to use—spin up projects with step-by-step wizards, chat seamlessly with users and models in multi-session threads, plug in tools and APIs on demand, and adapt workflows with flexible templates and drag-and-drop orchestration.

What Our Clients Say

"WaqoorAI enabled secure on-prem fine-tuning on our proprietary data and delivered agent-orchestrated workflows in weeks. OpenAI-compatible APIs cut integration time by over 50% while observability kept latency and cost predictable."

Ahmed Saleh
CTO, AlNour Telecom

"Data never left our environment—air-gapped training with RBAC, encryption, and full audit trails satisfied our compliance requirements. WaqoorAI’s flexibility across CPU/GPU clusters ensured reliable performance at scale."

Youssef Nassar
CIO, Mada Networks

"WaqoorAI’s self-guided workbench let our educators and mentors build domain assistants without engineering support, chat naturally with learners and models, and integrate securely with our LMS via OpenAI-compatible APIs. Private, on-prem training on our curricula protected IP, while analytics and agent orchestration improved engagement and reduced content development time."

Noura Al-Faisal
Director, National Learning Community Consortium

Frequently Asked Questions (FAQs)

The platform enables autonomous and collaborative agents with planning, multi-step reasoning, tool/function calling, workflow orchestration, and safe actioning under policy, RBAC, and audit trails—so teams can automate complex business processes with confidence.

Developers build quickly using SDKs, REST/gRPC APIs, and OpenAI-compatible endpoints. First-class code support and templating streamline integration, while webhooks and connectors enable event-driven workflows.

It delivers low-latency inference with autoscaling, CPU/GPU and multi-GPU scheduling, and intelligent traffic management (A/B and canary). This ensures predictable performance from pilot to large-scale production.

The platform provides SSO/OAuth/SAML, fine-grained RBAC, tenant isolation, encryption in transit and at rest, audit logging, rate limiting, configurable retention, and optional PII redaction to support enterprise compliance requirements.

Yes. WaqoorAI offers connectors, indexing/chunking, embeddings, vector stores, caching, and evaluation dashboards to build measurable, reliable retrieval-augmented workflows.

Yes. WaqoorAI supports on-prem and air-gapped installations as well as private cloud or hybrid via Kubernetes/Helm, giving you full control over data locality and infrastructure.

End-to-end observability tracks metrics, traces, token/GPU utilization, and spend. Built-in quality and drift monitoring with alerts and feedback loops helps teams maintain accuracy and optimize ROI.

The platform supports a broad mix of open-source, custom, and proprietary models for Machine Learning, Deep Learning, LLMs, and GenAI, including fine-tuned variants (LoRA/PEFT). A registry and versioning system manages promotion through CI/CD gates.

Policies, approvals, and gated promotion enforce consistent, auditable releases from dev to production. Every change is logged with lineage, ownership, and rollback controls.

Most teams start by pointing existing clients to the OpenAI-compatible API and moving inference first, then incrementally adopting registry, RAG pipelines, agents, and observability to standardize operations with minimal disruption.

Experience WaqoorAI in Action

Book a tailored walkthrough or start a free trial with our solutions team.

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