AI & LLM Platform SaaS

Enterprise GenAI is Live

Most organizations can prototype with LLMs quickly, but moving to production introduces hard problems: secure access to models, grounding responses in internal knowledge, preventing data leakage, controlling costs, and creating repeatable release processes for prompts and models. This platform brings those essentials together in a single Kubernetes-native SaaS, so teams can ship AI features safely and reliably.

Why an AI & LLM Platform on MTD Cloud

Building GenAI applications typically requires stitching together multiple layers: model providers, prompt management, retrieval pipelines, vector databases, policy/guardrails, evaluation frameworks, and observability dashboards. That fragmentation slows teams down and makes governance difficult—especially in regulated industries.

MTD Cloud’s AI & LLM Platform SaaS provides a standardized, governed foundation:

• faster adoption across multiple teams,

• consistent security and compliance controls,

• predictable cost management,

• and a clear path from experiments to production.

What’s included

• Model Gateway & Routing

  A unified endpoint to access multiple models (hosted or self-hosted), with routing, fallbacks, and consistent request/response patterns.

• RAG (Retrieval-Augmented Generation)

  Ingest documents, chunk content, generate embeddings, and retrieve relevant context so answers are grounded in your knowledge—optionally with citations.

• Guardrails & Policy Controls

  Apply controls to prompts and outputs (PII handling, secret detection patterns, policy enforcement), reducing hallucinations and leakage risks.

• LLMOps & Evaluation

  Track prompt/model versions, run regression tests with golden datasets, and roll out changes with confidence.

• Observability & Cost Control

  Visibility into latency, errors, and token usage per team/app, with budgets and guardrails to prevent surprises.

• Enterprise Governance

  Multi-tenant patterns, RBAC, auditability, and integration-ready foundations for regulated environments.

Best-fit use cases

This platform is ideal for:

• Internal copilots for engineering, operations, HR, and customer support

• Knowledge assistants grounded in policies, documentation, and runbooks

• AI features embedded into existing apps (search, summarization, classification, automation)

• Regulated GenAI use cases requiring governance, auditability, and cost control.

Quick start

Call the platform’s Model Gateway to run a chat request (illustrative):

1// Example of using cloud SAAS
2curl -X POST https://api.mtdcloud.eu/llm/v1/chat/completions \
3  -H "Authorization: Bearer $MTD_TOKEN" \
4  -H "Content-Type: application/json" \
5  -d '{
6    "model": "router-default",
7    "messages": [
8      {"role": "system", "content": "You are a helpful enterprise assistant."},
9      {"role": "user", "content": "Summarize this week’s operational incidents and their root causes."}
10    ],
11    "stream": true
12  }'
13
14// Example RAG request structure (illustrative):
15{
16  "model": "router-default",
17  "messages": [
18    { "role": "user", "content": "What is our policy for production access approvals?" }
19  ],
20  "retrieval": {
21    "knowledgeBase": "company-policies",
22    "topK": 5,
23    "includeCitations": true
24  }
25}
26

Conclusion

The AI & LLM Platform SaaS helps teams move beyond prototypes by providing the critical building blocks for**secure, governed, and observable GenAI in production**.

Instead of re-implementing model access, retrieval pipelines, safety controls, and cost tracking in every project, you get a shared platform that accelerates delivery while keeping risk under control—especially important for banking, insurance, and other regulated environments.