Lots of hype, no plan, scattered pilots.
What we did
- Ran an opportunity + risk assessment
- Prioritized a roadmap by ROI
- Stood up a governed first use case
Result Replaced scattered experiments with one funded roadmap and a working pilot.
AI Consulting
Custom LLM & RAG Systems
Private, grounded AI that answers from your approved knowledge, not the open internet.
What it is
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.
Core services include
- 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
Definition
What is Custom LLM & RAG (Retrieval-Augmented Generation) Systems?
A Custom LLM & RAG system is a private AI assistant that answers questions using your own documents and data instead of the open internet, retrieving the relevant source material at question time and using a large language model to write a grounded, cited answer.
How it works
Your content, documents, help articles, product specs, tickets, policies, is split into passages, converted into embeddings, and stored in a vector database; when someone asks a question, the system retrieves the most relevant passages and feeds them to the language model so the answer is grounded in your material and can cite its sources.
Who it’s for
For organizations sitting on large internal knowledge bases, support teams, professional-services firms, technical and compliance-heavy operations, that need staff and customers to get fast, accurate answers from trusted internal sources. The outcome is efficiency and time savings: less time hunting through documents, fewer escalations, and fewer wrong answers, since responses are designed to stay grounded in your approved content.
In practice
A company connects its product manuals, warranty policies, and past support tickets to a private assistant, so a support rep can ask "what's covered under the extended warranty for this model?" and get an answer that quotes the exact policy paragraph and links to the source document.
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 quoteBuild, manage, run
Custom 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, Qwen, 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, to help the system stay 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
See it in action
Grounded answers, cited from your own docs.
Your Brand · Knowledge AssistantGrounded · SSO gated
Can enterprise clients cancel mid-term, and what refund applies?
Yes; with 60 days' written notice MSA §7.2. Unused months refund pro-rata, minus onboarding fees Refund Policy p.3. Pre-2025 contracts keep the legacy 30-day window.
RETRIEVED FROM APPROVED SOURCES
Enterprise-MSA-2025.pdf · §7.20.94
Refund-Policy-v4.docx · p.30.91
Legal memo · internal wiki0.87
98.4% grounded, last eval1,283 approved docsDeclines to guess when unsure
Illustrative example, styled to show the kind of output we deliver.
Selected work
Representative engagements.
Turning AI hype into a funded plan and a working first use case.
Wanted answers grounded in their own docs, not hallucinations.
What we did
- Built a RAG system over their knowledge base
- Added citations + an evaluation harness
- Access controls + monitoring
Result Deflected common tickets with sourced answers the team could trust.
Examples are anonymized to honor client NDAs and edited to illustrate typical scope, outcomes vary by market, budget, and starting point.
How & why it works
Grounded in your sources, not the model's memory.
The system retrieves relevant passages from your documents and prompts the model to ground its answer in those passages, reducing reliance on unsupported pretrained knowledge. At query time we retrieve the passages from your own sources that actually address the question and instruct the model to answer from those passages, with a citation for each claim, so the answer is guided by your source material, with citations and guardrails to reduce unsupported claims.
- Ingest, chunk, and embedWe pull your sources (PDFs, wiki, tickets, CRM, databases) through an ingestion pipeline that cleans and splits each document into structure-aware chunks, typically a few hundred tokens, broken on headings and sections so a passage stays self-contained, then embed each chunk into a vector store alongside metadata like source, section, date, and access level. A scheduled re-index keeps the index current as documents change, and stale versions are replaced rather than duplicated.
- Hybrid retrieval + rerankingFor each question we run dense vector search (semantic similarity) alongside BM25 keyword search, so exact identifiers, SKUs, policy codes, error strings, aren't lost to fuzzy embeddings. The merged candidate set is then passed through a cross-encoder reranker that reads query and passage together and scores true relevance, narrowing dozens of candidates down to the handful of passages worth putting in the prompt. Retrieval quality is a major driver of answer quality, so this step is critical to reducing irrelevant or unsupported responses, it's where we focus much of our evaluation and optimization effort to push retrieval precision as high as we can.
- Access-filtered, grounded generationEvery chunk is filtered against the user's permissions via metadata, so the source context provided to the model is limited to materials that user is authorized to access, with guardrails and testing to reduce unsupported answers. The selected passages are assembled into the prompt with instructions to answer strictly from them, cite the specific chunks used, and say it doesn't know when the sources don't cover the question, turning 'I can't find that' into a correct, safe outcome instead of a fabricated one.
- Evaluate before and after launchWe build an evaluation set of real questions with known good answers and score every change on faithfulness (is each claim supported by a retrieved passage?), context relevance (did retrieval surface the right passages?), and answer correctness, often with an LLM-as-judge grader plus human spot-checks. Nothing ships on vibes; a retrieval or prompt change either moves those numbers or it's reverted.
- Monitor and retune in productionOnce live we log queries, retrieved passages, and confidence, and review low-confidence or thumbs-down interactions to find gaps; a missing document, a chunk that splits an answer in half, a query phrasing the retriever misses. Those become fixes to ingestion, chunking, or retrieval, and get added back to the eval set so if a similar failure recurs, it is far more likely to be caught in regression testing before it reaches users.
Worked exampleA mid-size insurance provider whose support and underwriting teams answer the same policy and procedure questions dozens of times a day, digging through hundreds of PDFs, an internal wiki, and email threads.
- Ingested policy PDFs, the internal wiki, and claims SOPs; chunked on document structure (roughly a few hundred tokens with heading context preserved) and embedded into a vector store, with a nightly re-index job so edits typically appear within about a day
- Retrieval runs hybrid: dense vector search plus BM25 keyword match to catch exact policy codes and form numbers, then a cross-encoder reranks the top candidates down to the handful of passages actually sent to the model
- Every answer must cite the source chunks it used, and metadata filters restrict retrieval to what each role is allowed to see so underwriting-only documents don't surface for a frontline agent
- Faithfulness and correct-citation are measured against a purpose-built eval set at each iteration (illustrative example: tuning chunk size and adding the reranker moved the share of answers judged fully grounded from roughly the low-70s to the high-80s, with unsupported claims on that set dropping sharply); these are example results from one project's eval methodology, not a guaranteed or typical outcome
Why it works
Accuracy in these systems is largely governed by retrieval, not just by how large or clever the model is: if the right passage is in the prompt a modest model tends to answer correctly, and if it isn't even a strong model is more likely to guess. That's why much of the leverage sits in chunking, hybrid search, reranking, and citations rather than in prompt wording alone; and why an evaluation harness matters, because it turns "the AI feels accurate" into a measured faithfulness score you can defend, improve deliberately, and watch for regressions as your documents and usage change.
FAQ
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.
Let’s make it measurable.