LLM RAG Models
Custom large language model creation, fine-tuning, and retrieval-augmented generation (RAG) pipelines that ground AI outputs in your enterprise knowledge base for accurate, traceable, and hallucination-free responses.
Why RAG?
Eliminate Hallucinations
Instead of relying solely on parametric knowledge, RAG retrieves actual documents from your knowledge base and conditions the LLM's generation on grounded evidence, dramatically reducing factual errors.
Always Current
Update your knowledge base without retraining. New documents, policies, or product information are ingested and immediately reflected in answers — no fine-tuning required.
Full Traceability
Every answer includes citations to the source documents. Users can verify, audit, and trust the outputs — essential for regulated industries like finance, healthcare, and legal.
Our RAG & LLM Services
Custom LLM Fine-Tuning
Domain-adaptive fine-tuning of foundation models (Llama, Mistral, GPT, Claude) on proprietary enterprise data using LoRA, QLoRA, and full fine-tuning techniques
- LoRA / QLoRA / DoRA adapters
- Domain-specific instruction tuning
- RLHF & DPO alignment
- Multi-GPU distributed training
RAG Pipeline Architecture
End-to-end retrieval-augmented generation pipelines with chunking strategies, embedding models, and hybrid search for accurate, grounded responses
- Document chunking & preprocessing
- Dense + sparse hybrid retrieval
- Re-ranking pipelines
- Context window optimization
Vector Database Integration
Design and deployment of vector storage solutions with HNSW indexes, metadata filtering, and multi-tenancy for production RAG at scale
- Pinecone / Weaviate / Qdrant
- pgvector & Timescale Vector
- Milvus & Chroma
- Multi-modal embeddings
Embedding Model Selection
Evaluation and deployment of state-of-the-art embedding models for semantic search, clustering, and classification tailored to your domain vocabulary
- OpenAI / Cohere / Voyage embeddings
- Open-source (BGE, E5, GTE)
- Cross-encoder re-rankers
- Custom embedding training
Evaluation & Observability
Comprehensive RAG evaluation frameworks with faithfulness, relevance, and answer correctness metrics for continuous quality monitoring
- RAGAS / TruLens / DeepEval
- Human-in-the-loop annotation
- A/B testing pipelines
- Latency & cost tracking
Production Deployment
Scalable LLM serving infrastructure with GPU optimization, caching layers, rate limiting, and guardrails for enterprise-grade reliability
- vLLM / TGI / Ollama serving
- Semantic caching (GPTCache)
- Guardrails & content filtering
- Auto-scaling & load balancing
RAG Pipeline Architecture
Ingestion Layer
Document processing pipeline that ingests data from multiple sources (S3, SharePoint, APIs, databases), performs chunking with optimal overlap strategies, and generates embeddings for vector storage.
Retrieval Layer
Hybrid retrieval combining dense vector search with keyword (BM25) search, metadata filtering, and multi-stage re-ranking to surface the most relevant context for each query.
Augmentation Layer
Context assembly, prompt templating, and query transformation including query rewriting, decomposition, and hypothetical document embeddings (HyDE) for improved retrieval quality.
Generation Layer
LLM inference with grounded generation, citation tracking, and confidence scoring. Supports streaming, structured output (JSON mode), and tool-calling for agentic workflows.
Supported Technologies
Ready to Build Your RAG System?
From proof-of-concept to production-grade RAG pipelines — our team delivers end-to-end solutions tailored to your data and domain.