Build your own Private AI
— LLM + RAG systems.

We help you build production-ready Large Language Models tailored to your business data. From architecture design to deployment — secure, scalable, and fully owned.

🚀 Start Build → 📞 Book a Call

Custom LLM pipelines

Fine-tune open-source LLMs on your data

Pick a base model (Llama-3, Mistral, Qwen, Mixtral). We handle data preparation, LoRA / QLoRA fine-tuning, evaluation, and deployment — end-to-end.

Base model selection and benchmarking
Data cleaning, deduplication, and PII redaction
LoRA / QLoRA / full fine-tuning
RLHF and DPO for instruction tuning
Evaluation harness (MMLU, HumanEval, custom evals)

Llama-3 Mistral Qwen LoRA Hugging Face

# Fine-tune Llama-3 with Glixy
from glixy import FineTune

job = FineTune(
  base="llama3-8b-instruct",
  method="qlora",
  dataset="./company-tickets.jsonl",
  epochs=3,
  lr=2e-4,
)

job.submit(
  cluster="a100-cluster-01",
  nodes=2,
)
# → ETA 4h 12m · cost ₹14,500
# → eval mmlu: 67.2 (+3.1)
# → eval custom: 91.4 (+18.7)

RAG systems

Retrieval-Augmented Generation — your docs as context

Plug your PDFs, wikis, support tickets, and knowledge bases into a vector store. Your LLM answers with citations grounded in your real data — no hallucinations.

Document parsing for PDF, DOCX, HTML, Markdown, code
Smart chunking with semantic boundaries
Embeddings: BGE, E5, OpenAI-compatible models
Hybrid search (vector + BM25) for higher recall
Re-ranking with cross-encoders for top-k accuracy

RAG Pipeline · live

Indexing

📄 Documents ingested 2,143,892

🧩 Chunks created 14.8M

🎯 Avg query latency 87 ms

Index health

96% optimal · weaviate cluster

What we provide

Production-ready private AI stack

✦

Custom LLM pipelines

Fine-tuning, prompt engineering, prompt caching, structured output (JSON mode).

LoRAQLoRA

📚

RAG systems

Hybrid retrieval (vector + BM25), re-ranking, citation generation, conversation memory.

LangChainLlamaIndex

🔍

Vector databases

Weaviate, Pinecone, Qdrant, pgvector — fully managed and tuned for your scale.

10M+ vectors<100ms p95

🔒

Private deployment

On-premise or in our data center. Your weights, your data, your control. Full compliance.

SOC 2GDPR

⚡

API layer

OpenAI-compatible REST API. Drop-in replacement for existing apps. Rate limiting, auth, logging.

OpenAI-compat

📊

Eval & monitoring

Hallucination detection, response quality scoring, drift alerts, usage analytics.

RAGASCustom evals

Use cases

Real businesses, real LLMs

🤖

Chatbots

Product-aware support and sales bots

📚

Knowledge assistants

Internal Q&A across wikis & docs

🎧

Support automation

Auto-triage, draft replies, escalate

📊

Data extraction

Structured data from unstructured text

Build your own Private AI
— LLM + RAG systems.

Power your AI with private LLMs

Fine-tune open-source LLMs on your data

Retrieval-Augmented Generation — your docs as context

RAG Pipeline · live

Production-ready private AI stack

Custom LLM pipelines

RAG systems

Vector databases

Private deployment

API layer

Eval & monitoring

Real businesses, real LLMs

Chatbots

Knowledge assistants

Support automation

Data extraction

Your LLM, your data, your moat.

Build your own Private AI— LLM + RAG systems.

Power your AI with private LLMs

Fine-tune open-source LLMs on your data

Retrieval-Augmented Generation — your docs as context

RAG Pipeline · live

Production-ready private AI stack

Custom LLM pipelines

RAG systems

Vector databases

Private deployment

API layer

Eval & monitoring

Real businesses, real LLMs

Chatbots

Knowledge assistants

Support automation

Data extraction

Your LLM, your data, your moat.

Build your own Private AI
— LLM + RAG systems.