Development of a Corporate AI Platform
Build an internal AI Hub with Model Gateway, RAG vector store, and self-hosted LLM for enterprise knowledge base. Integrate Confluence and SharePoint, use vLLM on Kubernetes for MLOps, and manage AI costs centrally. Our corporate AI platform integrates Model Gateway, RAG vector store, self-hosted LLM, MLOps with vLLM on Kubernetes, and enterprise knowledge base from Confluence and SharePoint, with AI data security and cost management. Typical annual savings for a 200-employee company are up to $22,000 (approx. 2 million rubles). Every team in a company uses ChatGPT in the browser — data leaks, costs uncontrolled, compliance absent. The alternative is a centralized platform: in 12–16 weeks, you get a single entry point with Model Gateway, RAG on a self-hosted vector store, and a self-hosted LLM, while a dashboard with cost allocation and audit log provides transparency. For example, in one project for a fintech company, we deployed RAG with Qdrant and Llama 3 — support response time dropped from 10 minutes to 10 seconds, and latency p99 did not exceed 200 ms. Centralized control reduces AI costs by 30–50%, saving up to 2 million rubles ($22,000) annually for a 200-person company. Each query costs as low as $0.001 with self-hosted models versus $0.01 with cloud APIs, making self-hosted 10 times cheaper per query. Additionally, vLLM on Kubernetes provides 2 to 3 times lower latency than cloud alternatives, perfect for real-time applications.
Step-by-Step Guide to Building Your Corporate AI Hub
- Assess your AI needs and data sources.
- Deploy a Model Gateway for unified access to all models.
- Set up a self-hosted vector store (Qdrant/Weaviate) for RAG.
- Integrate corporate knowledge bases (Confluence, SharePoint, Google Drive).
- Deploy a self-hosted LLM (e.g., Llama 3 via vLLM on Kubernetes).
- Configure access control with SSO and role-based permissions.
- Train teams on prompt engineering and monitor usage via admin panel.
How to Build a Corporate AI Hub?
Model Gateway: Unified Endpoint
We configure a single endpoint for access to all models: GPT-4o, Claude 3.5, self-hosted Llama 3, and Mistral. Rate limiting by department, cost allocation to cost-centers, full audit log of every request. Integration with corporate SSO (SAML/OIDC). The gateway supports fallback between models — if the self-hosted LLM is unavailable, it automatically switches to a backup model.
Knowledge Base (RAG): Vector Document Store
A vector store (Qdrant, Weaviate) is deployed on your servers. Automatic indexing from Confluence, SharePoint, Google Drive using embeddings (text-embedding-ada-002, 1536 dim). Document access is restricted: department A cannot see department B's documents. Models are augmented with context from the knowledge base — this solves the hallucination problem and improves answer quality. If needed, we apply chunking with overlap and re-ranking for accuracy.
Self-hosted LLM: Deployment Inside the Perimeter
Details on model customization
For confidential data, we run Llama 3 70B or Mixtral 8x7B via vLLM on Kubernetes. Optimization using quantization (INT4/FP8) reduces GPU requirements. For tasks requiring GPT-4 level, we use Azure OpenAI with a signed data privacy addendum Azure OpenAI Service documentation. All data remains within the company's infrastructure. We also configure [fine-tuning LoRA](https://huggingface.co/docs/peft/en/developer_guides/lora) for customizing the model to corporate terminology. Self-hosted vLLM is 2 to 3 times cheaper per token than cloud APIs and offers 2 to 3 times lower latency — critical for real-time applications. Self-hosted models are 10 times cheaper per query than cloud APIs (e.g., $0.001 vs $0.01).Departmental Tools
- Developers: code review, documentation and test generation.
- HR: resume screening, JD creation, onboarding assistant.
- Legal: contract analysis, summarization.
- Customer Support: answer generation from knowledge base.
- Analytics: natural language to SQL, report building.
Admin Panel: Transparent Controlling
User rights management, quotas, usage monitoring by team, cost breakdown. See which departments are using AI most effectively, where training or prompt optimization is needed. Alerts on budget overruns or usage anomalies.
Cloud vs Self-hosted: Which One is Better?
| Criterion | Cloud Providers (OpenAI, Anthropic) | Self-hosted (vLLM, TGI) |
|---|---|---|
| Latency p99 | 500–1500 ms (depends on region) | 100–300 ms (on own GPU cluster) — 2 to 3 times lower |
| Privacy | Data processed on provider servers | Full control, data never leaves perimeter |
| Cost per 1M tokens | $2–$15 (depending on model) | $0.5–$3 (including GPU cost) — 2 to 3 times cheaper |
| Customization | Fine-tuning possible but limited | LoRA, P-tuning, full control over config |
| Compliance | DPA, but data outside company | GDPR, 152-FZ, own certificates |
RAG with self-hosted vector store improves answer accuracy by 30% compared to prompt-only methods. With self-hosted solutions, latency p99 is 2 to 3 times lower than cloud alternatives — critical for real-time applications. Average savings on AI costs are 30–50% (up to 2 million rubles or $22,000 per year for a 200-person company).
Implementation Timeline (12–16 Weeks)
| Stage | Duration | Result |
|---|---|---|
| Infrastructure and Gateway | Weeks 1–4 | Kubernetes, vLLM, Model Gateway, SSO, basic chat |
| RAG and Integrations | Weeks 5–9 | Connect Confluence, SharePoint, indexing, access control |
| Tools and Admin Panel | Weeks 10–13 | Pilot departments, dashboard, analytics |
| Completion | Weeks 14–16 | Security audit, load testing, onboarding, change management |
Deliverables
- Architecture and configuration documentation.
- Access to admin panel with monitoring and logs.
- Team training (workshops on prompt engineering and administration).
- 1-year warranty: support, updates, SLA 99.9%.
ROI: Numbers and Facts
Based on our implementation experience (10+ projects, 5+ years in AI/ML):
- Time savings: 2–4 hours per employee per week. A 100-person team saves 200–400 hours weekly.
- AI cost reduction: centralized control cuts costs by 30–50% compared to uncontrolled individual subscriptions.
- Compliance: we guarantee adherence to security requirements and regulations (GDPR, 152-FZ).
Self-hosted solutions with vLLM provide latency p99 2 to 3 times lower than cloud alternatives — critical for real-time applications. Our certified engineers (AWS, Azure, Kubernetes) support the project at all stages.
Get a consultation on your architecture — we will select the optimal GPU configuration, models, and MLOps tools for your tasks and budget. Contact us — we will calculate a pilot in one day.