Custom LLM & RAG Systems
Private, grounded AI that answers from your knowledge, not the open internet.
Your data, retrievable and trustworthy.
We build retrieval-augmented systems that ground LLM answers in your own documents, policies, and data, with citations, access control, and evaluation built in.
- Knowledge base ingestion and chunking
- Vector search and retrieval pipelines
- Grounded generation with citations
- Access control and PII handling
- Evaluation and hallucination testing
- Deployment and ongoing tuning
Where it fits.
- Internal knowledge and support assistants
- Sales and onboarding enablement
- Policy, compliance, and SOP lookup
- Research and document analysis
See if Custom LLM & RAG Systems is the right move for your team.
Request a free quoteCustom LLM and RAG Systems We Build and Run
We build retrieval systems on your private data and run them in production for mid-market and enterprise teams. Our senior engineers design the ingestion, chunking, embedding, and retrieval layers, then connect them to Claude, GPT, or open models like Llama, Hermes, and Mistral depending on your accuracy, privacy, and cost needs. We own the system end to end, including the parts that keep answers grounded and current.
We run ongoing evaluation against your real questions and refresh the retrieval layer continuously, so the system stays accurate as your knowledge base grows.
- We build the full pipeline: ingestion, embeddings, vector search, reranking, and grounded generation
- We deploy with private or open models when data residency and control matter
- We add evaluation, citation, and guardrails to reduce unsupported answers and keep claims traceable
- We keep the index fresh and the system maintained as your documents and data change
Grounded, not guessing.
We define accuracy criteria up front and test against real questions, so answers stay verifiable and on-source.
Questions, answered.
Pasting documents into a chat window is manual, hits context limits fast, and forgets everything between sessions. A RAG system indexes your full knowledge base into a vector store, retrieves only the most relevant passages for each question, and feeds those to the model so answers stay grounded in your source material with citations. For example, a support team can ask across thousands of product docs and SOPs at once and get an answer that links back to the exact page it came from, instead of someone copy-pasting one PDF at a time.
We configure the model to prioritize retrieved context and instruct it to say it does not know when the source material does not cover a question, rather than guessing. We add retrieval quality controls, citation requirements so every claim traces back to a document, and evaluation sets that test for hallucination before launch. We also tune the retrieval step itself, since most wrong answers come from pulling the wrong passages, not from the model, so we measure and improve what gets retrieved first.
Your knowledge base, embeddings, and conversation logs stay inside infrastructure you control, whether that is your cloud account or an isolated environment we manage for you. Your content is never used to train public foundation models. Depending on sensitivity we can run open models like Llama or Mistral fully in your environment so no data leaves it, or use API models such as Claude or GPT under enterprise terms that exclude training on your data, in which case the prompt and the passages retrieved for each question are sent to that provider at inference time while your stored knowledge base, embeddings, and logs remain in your environment. We scope this in the first conversation based on your compliance needs.
A focused pilot on one knowledge source and one use case typically takes a few weeks, while a production system across multiple data sources, access controls, and integrations runs longer. We start by scoping the use case and inventorying your sources, then build an ingestion and retrieval pipeline, tune it against real questions from your team, and validate accuracy before rollout. You see a working prototype early so we are tuning against your actual content and edge cases, not a generic demo.
Knowledge changes constantly, so the system needs ongoing re-indexing as documents are added or updated, plus monitoring of what people ask and where answers fall short. NYFTY does not just hand off a build, we can run and manage it for you, including refreshing the index, reviewing failed or low-confidence queries, and retuning retrieval as usage grows. If you prefer to own it in-house, we build it on infrastructure your team controls and document the pipeline so your engineers can maintain it directly.