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.
What we build
RAG that fits your stack
We deliver:
Tech stack we use
Composable. Secure. Scalable.
| Component | Options |
|---|---|
| Embeddings | OpenAI, Cohere, HuggingFace, GTE |
| Vector DB | Pinecone, Weaviate, Qdrant, FAISS |
| Frameworks | LangChain, LlamaIndex, Haystack |
| Models | GPT-4, Claude, Falcon, Mistral |
| UI | Streamlit, React, internal portals |
Accelerate LLM apps by 50% with RAG.
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.
