Enterprise AI Platform

The Technology Behind
Intelligent Enterprise AI

Production-ready AI capabilities — MCP integration, RAG pipelines, Agentic AI orchestration, and custom models — engineered for security, scale, and real business outcomes.

99.9% Uptime SLA
<100ms Response Time
SOC 2 Type II Certified
12+ System Integrations
AI Core
MCP
RAG
Agents
Security
Core Capabilities

Everything You Need to Deploy
Production-Grade Enterprise AI

Eight integrated AI capabilities — each production-ready, enterprise-secure, and designed to work together as a unified platform.

MCP Integration

Connect any LLM to your ERP, CRM, databases, and APIs through a single, secure, vendor-agnostic gateway — no custom integrations.

Model Context Protocol

RAG Pipeline

Retrieval-Augmented Generation across your knowledge base — eliminating hallucinations with context-grounded, accurate AI responses.

Vector Search

Agentic AI

Multi-step reasoning agents that plan, execute, and coordinate tasks autonomously across systems — with human-in-the-loop oversight.

Autonomous Agents

Custom AI Models

Domain-specific models fine-tuned on your data — healthcare, finance, accounting, and enterprise operations — for higher accuracy than generic LLMs.

Fine-tuning & Training

Enterprise Security

RBAC, end-to-end encryption, SSO integration, audit logging, and SOC 2 Type II certification — built into every layer of the platform.

SOC 2 Type II

Vector Database

High-performance semantic search and embedding storage — enabling millisecond retrieval from millions of documents and knowledge sources.

Semantic Search

Workflow Orchestration

AI-driven workflow automation connecting decisions, data, and actions across your enterprise systems — from trigger to completion.

Multi-system Automation

AI Monitoring & Analytics

Real-time dashboards tracking AI performance, usage, errors, and business impact — with alerting and continuous improvement loops.

Observability
MCP Integration

Model Context Protocol — The Universal AI Gateway

MCP is the open standard developed by Anthropic for securely connecting large language models to enterprise systems. AIplay's MCP Gateway eliminates custom point-to-point integrations — replacing them with a single, auditable, vendor-agnostic access layer.

Vendor-AgnosticConnect GPT-4, Claude, Gemini, or any custom model — switching AI models never requires rebuilding integrations.
Zero Data MovementAI queries your enterprise data in-place — nothing leaves your security perimeter without authorisation.
Auto-ScalingHandle enterprise-scale request volumes with automatic scaling, rate limiting, and intelligent caching.

Live Architecture

Enterprise Systems
ERP CRM HRMS Database File Store APIs +6 more
MCP Gateway — Secure Integration Layer
Authentication & RBAC
Rate Limiting
End-to-End Encryption
Audit Logging
Intelligent Caching
SSO Integration
AI Models (Vendor-Agnostic)
Claude GPT-4 Gemini Llama Custom LLM
Explore by Capability

Deep-Dive into Each Technology

RAG — Retrieval-Augmented Generation

RAG connects AI models to your live knowledge base — documents, policies, SOPs, and databases — delivering accurate, context-grounded responses instead of hallucinated guesses. AIplay's RAG pipeline is built for enterprise scale: millions of documents, sub-100ms retrieval, and continuous knowledge base updates.

Vector Database Semantic Search Document Parsing Auto-Indexing Sub-100ms Retrieval Multi-language

RAG Flow

1User query processed by LLM to extract intent
2Vector search retrieves top-k relevant documents
3Context injected into prompt alongside original query
4LLM generates grounded, accurate, cited response

Agentic AI — Autonomous Multi-Step Reasoning

Agentic AI systems don't just answer questions — they plan, execute, and coordinate sequences of actions across your enterprise systems. AIplay deploys production-grade agents with human-in-the-loop oversight, tool use, function calling, and full audit trails for every decision.

Autonomous Agents Function Calling HITL Oversight Tool Use Multi-Agent Audit Trail

Agent Decision Loop

Plan

Define task steps

Execute

Call tools & APIs

Observe

Evaluate results

Iterate

Refine & continue

Custom AI Model Development

Generic LLMs are trained on the internet — not your industry. AIplay develops domain-specific models fine-tuned on your data, delivering significantly higher accuracy for industry-specific tasks than off-the-shelf alternatives.

HealthcareMedical coding, clinical notes, drug interactions
Accounting & FinanceTax analysis, audit automation, fraud detection
Enterprise OperationsSupply chain, workflow optimisation, resource allocation
1
Weeks 1–2

Assessment

Analyse your data, requirements, and use cases to define model scope and architecture.

2
Weeks 2–4

Data Preparation

Clean, label, and structure your training data for optimal model performance.

3
Weeks 4–8

Model Training & Validation

Fine-tune or build from scratch, with iterative validation against your business benchmarks.

4
Week 8+

Production Deployment

Deploy with monitoring, performance dashboards, and continuous improvement pipelines.

Enterprise-Grade Security & Compliance

Security isn't a feature added after deployment — it's architected into every layer of AIplay's platform. From access control to audit logging, every AI interaction is authenticated, encrypted, and traceable.

RBAC & Identity Management100%
End-to-End Encryption (AES-256)100%
Audit Log Coverage100%
GDPR / HIPAA ComplianceFull
SOC 2 Type II CertificationCertified
SSO Integration Coverage98%

Flexible Deployment — Any Environment

All deployment options include the full MCP integration layer, security controls, and feature parity — your infrastructure choice never constrains your AI capabilities.

Cloud

  • AWS, Azure, GCP
  • Managed infrastructure
  • Auto-scaling
  • Global CDN

On-Premise

  • Full data sovereignty
  • Air-gapped capable
  • Your hardware
  • Zero data egress

Hybrid

  • Split workloads
  • Sensitive data on-prem
  • Cloud for scale
  • Unified management

Edge

  • Ultra-low latency
  • Offline capable
  • IoT integration
  • Local inference
Platform Performance

Engineered for Enterprise Scale

0Platform Uptime SLA
<100msAPI Response Time
0Enterprise Integrations
SOC 2Type II Certified
0AES Encryption
24/7Enterprise Support
Technology Stack

Built on Best-in-Class AI Infrastructure

Large Language Models Model Context Protocol Agentic AI Framework RAG Pipeline Vector Database Function Calling AWS / Azure / GCP Enterprise Security Workflow Orchestration Human-in-the-Loop AI Monitoring Edge Deployment
FAQ

Frequently Asked Questions

Have a technical question not covered here? Book a consultation and speak directly with our AI engineers.

01 What is Model Context Protocol (MCP) and why does it matter for enterprise AI?
MCP is an open standard developed by Anthropic for securely connecting large language models to enterprise data sources, APIs, databases, and tools. It acts as a universal gateway — enabling real-time, context-aware AI responses without moving data to third-party servers. For enterprise AI, MCP eliminates custom point-to-point integrations and provides a single, auditable access layer across all your systems.
02 Does MCP integration cause vendor lock-in with a specific AI model?
No. AIplay's MCP implementation is vendor-agnostic by design. The MCP Gateway can connect any LLM — GPT-4, Claude, Gemini, or a custom model — to your enterprise systems. Switching or combining AI models does not require rebuilding your integration layer, protecting your investment as the AI landscape evolves.
03 What security features are built into AIplay's MCP implementation?
AIplay's MCP Gateway includes role-based access control, end-to-end AES-256 encryption, SSO integration, rate limiting, comprehensive audit logging, and SOC 2 Type II certification. Every request is authenticated, logged, and scoped to specific data sources — ensuring AI models access only what they are authorised to access.
04 Can AIplay develop a custom AI model for my specific industry?
Yes. AIplay's Custom AI Model Development covers assessment, data preparation, fine-tuning or from-scratch model building, and production deployment with monitoring. Industry specialisations include healthcare (medical coding, clinical notes), accounting (tax analysis, audit automation), finance (risk assessment, fraud detection), and enterprise operations (supply chain, workflow optimisation).
05 How long does it take to develop and deploy a custom AI model?
Most fine-tuned domain models reach production in 6–10 weeks: Assessment (1–2 weeks), Data Preparation (2–4 weeks), Model Training & Validation (2–6 weeks), and Production Deployment with monitoring. The AI Readiness Audit clarifies scope and timeline before any build commitment.
06 What performance and uptime guarantees does AIplay offer?
AIplay's platform is engineered for enterprise workloads with a 99.9% uptime SLA, sub-100ms response times, automatic scaling to handle traffic spikes, and global CDN distribution. These specifications apply across cloud, hybrid, and on-premise deployments where infrastructure permits.
07 What is RAG and how does it prevent AI hallucinations?
RAG (Retrieval-Augmented Generation) connects the AI to your live knowledge base — documents, SOPs, databases — before generating a response. Instead of relying on training data alone, the AI retrieves the most relevant, up-to-date information from your systems and uses it as grounded context. This eliminates hallucinations because responses are based on verified source material rather than pattern-matched guesses.
08 What does Human-in-the-Loop (HITL) mean for Agentic AI deployments?
HITL means that for high-stakes decisions or uncertain situations, the AI pauses and routes the task to a human approver before proceeding. AIplay configures HITL checkpoints based on your risk thresholds — low-risk routine tasks run fully autonomously, while high-value or irreversible actions require human sign-off. This gives you the efficiency of automation with the control of human oversight.
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Schedule a live technical demo — see the MCP Gateway, RAG pipeline, and Agentic AI platform running on your use case.