Enterprise-grade RAG solutions
We build RAG pipelines that link LLMs to your internal knowledge— reducing hallucination and boosting business value.
Why RAG works
No more blind answers
LLMs are generative, but not factual. RAG fixes that:
IDC predicts 80% of enterprise GenAI apps by 2026 will adopt RAG as a core architecture to meet trust and auditability needs.
- 01
Adds citations & transparency to answers
- 02
Brings internal knowledge into context window
- 03
Reduces hallucinations and irrelevant outputs
- 04
Supports long, complex documents without training
- 05
Enables real-time updates without model retraining
What we build
RAG that fits your stack
We deliver:
Tech stack we use
Composable. Secure. Scalable.
| Component | Options |
|---|---|
| Embeddings | OpenAI, Cohere, Hugging Face, GTE |
| Vector DB | Pinecone, Weaviate, Qdrant, FAISS |
| Frameworks | LangChain, LlamaIndex, Haystack |
| Models | GPT-4, Claude, Falcon, Mistral |
| UI | Streamlit, React, internal portals |
Is your LLM making things up?
Enterprise outcomes
Proven. Measurable. Live
| KPI | Baseline | Post-RAG |
|---|---|---|
| Backend retraining cost | 30–40% | <5% |
| Document change sync | Low | 4.5+/5 |
| Time to factual response | ~6s | 2–3s |
| User confidence score | Weekly | Instant |
| Hallucination rate | High | Near-zero |
How to start
Deploy RAG in 4 steps
Select the use case
Choose high-risk/high-value domains (e.g., legal, support, sales enablement).
Connect your data
Integrate PDFs, knowledge bases, tools, and proprietary sources.
Build + tune pipeline
Choose the retrieval method, chunking logic, embedding model, and LLM pairing.
Deploy + monitor
Test output quality, reduce drift, and build feedback loops.
