Answer engines
Search that returns cited answers instead of a list of documents.
[ services — rag & llm integration ]
RAG systems that answer from your data.
Retrieval-augmented generation and LLM API integration. Answer engines, embeddings pipelines, vector search, and grounded AI applications over your data.
200+
products shipped
50+
AI builds
4.9/5
client rating
2–4 wks
MVP launch
( what we build )
From answer engines to AI copilots.
We ground every system in your data so output is accurate, traceable, and current.
RAG chatbots
Support and internal assistants that answer from current knowledge.
Embeddings & vector search
Ingestion and retrieval pipelines over docs, PDFs, and databases.
LLM API integration
Provider-agnostic calls, prompting, guardrails, and structured output.
( engineering for grounded AI )
Built for accuracy, eval, and scale.
Retrieval quality
Chunking, embedding model, and reranking tuned and measured with an eval set.
Cited answers
Every response traces to a source for trust and auditability.
Guardrails
Policies, output checks, and data isolation for privacy and compliance.
Production-ready
Pipeline, caching, cost controls, and monitoring for real workloads.
( comparison )
| Need | Recommended approach | Why it works |
|---|---|---|
| Answers grounded in your knowledge | RAG + vector search | Accurate, citeable, up-to-date responses |
| Simple LLM calls | Direct API integration | Faster, lower cost, no retrieval needed |
| Search with cited answers | Answer engine | Users get answers, not document lists |
( faq )
Questions buyers ask
What is RAG (Retrieval-Augmented Generation)?
RAG combines an LLM with a retrieval system over your own data. Instead of answering from memory, the model retrieves relevant chunks from your knowledge base and answers from that context, which improves accuracy and reduces hallucination.
What can I use RAG for?
RAG powers answer engines over product docs, support wikis, policies, contracts, and internal knowledge. It's ideal for chatbots, search that returns cited answers, copilots, and tools that need grounded, up-to-date responses.
How do you keep RAG answers and citations accurate?
We tune chunking, embedding models, and reranking, measure retrieval quality with an evaluation set, and keep each answer grounded in retrieved context so every claim traces to a source.
Which vector databases and frameworks do you use?
We build with pgvector, Pinecone, Qdrant, and Weaviate and orchestrate with LangChain, LlamaIndex, or direct provider SDKs, choosing the stack that fits your data size and cost.
Can you integrate an LLM by API without RAG?
Yes, for simpler cases. We also build plain LLM API integrations with prompting, guardrails, and structured output when retrieval isn't needed.
How long does a RAG or LLM integration take?
A retrieval-grounded MVP over one source typically ships in 2-4 weeks. Production RAG with multi-source ingestion, eval, and guardrails runs 4-10 weeks.