Data & Generative AI

Private AI Models. Total Corporate Privacy.

Unlock your corporate knowledge safely. We build Retrieval-Augmented Generation (RAG) pipelines and deploy LLMs locally or on private cloud infrastructure.

Corporate AI Security Challenges

Public API Leak Risks

Sending proprietary data to commercial public APIs violates data policy and exposes business secrets.

Unstructured Knowledge Silos

PDFs, wikis, and contracts spread across servers with no centralized, structured querying mechanism.

Model Hallucinations

Generative models guessing facts instead of basing answers on actual enterprise history.

What We Build For Your Enterprise

Private LLM Deployment

We deploy open-weight models (Llama 3, Mistral, Qwen) in your virtual private server (VPS/Cloud) with complete isolation.

Structured RAG Pipelines

Enabling semantic search and prompt injection on company databases, wikis, PDFs, and legal contracts.

Data Pipelines & Warehousing

Building automated ETL processes, data consolidation, and analytics databases (PostgreSQL/pgvector).

Intelligent Workflows

Building autonomous agents that run structured tasks, draft responses, and trigger custom code.

Enterprise AI Stack

Python / PyTorch
Ollama / vLLM
pgvector (Postgres)
LangChain / LlamaIndex
Azure Databricks
Vertex AI (Google Cloud)
SAP HANA SQL / pgvector
Power BI / Data Science
Qdrant / Milvus
Docker / Nvidia-Docker
HuggingFace Open LLMs
FastAPI / API Integration

Frequently Asked Questions

Q.Do my data leave our servers when using LLMs?

No. Unlike ChatGPT APIs, we deploy open models locally on your own servers or virtual private cloud. The data never leaves your infrastructure, maintaining absolute secrecy.

Q.Do I need expensive GPUs to run local AI?

Not necessarily. Depending on the model size and query volume, we can optimize models to run on high-performance cloud CPUs or budget GPUs (like AWS G5 instances) efficiently.