RAG Development Services
Systems that answer from your data (grounded, cited, and evaluated) instead of improvising from a model's memory.
Who this is for
The situations that bring people to us
Your knowledge is trapped in documents, tickets, and heads
Policies, contracts, past projects, support history: the answers exist, but finding them takes the one person who remembers where to look.
A generic chatbot hallucinated your policy
Models improvise when they lack grounding. Retrieval-augmented generation forces answers to come from your sources, and to show where they came from.
Search finds documents. Nobody wants documents.
People want the answer, with the source attached. That is precisely the shape of output a well-built RAG system produces.
What we build
Concrete deliverables, not categories
Knowledge assistants
Internal or customer-facing assistants over your documents and systems, answering with citations and honest 'I don't know' behavior.
RAG-backed product features
Retrieval built into your product (matching, recommendations, contextual help) where grounded output is a feature users pay for.
▸ In production: the parsing-and-matching pipeline inside NexeraHR
Retrieval infrastructure
Ingestion, chunking, embeddings, vector stores, and (the part most vendors skip) retrieval evaluation sets, so quality is measured rather than assumed.
Natural-language data access
Safe NL-to-database querying with schema grounding and validation guardrails.
▸ Technical deep-dive: our MongoDB MCP server
How it works
A RAG pipeline that can defend its answers
Retrieval-augmented generation stands or falls on the unglamorous stages. Ingestion and chunking decide what can ever be found; embedding and indexing decide how it is found; retrieval decides what the model sees for each question; and generation is constrained to answer from that context, with citations, or say it cannot.
The eval loop is the difference between our builds and most: a test set of real questions with known-good answers, scored for retrieval hit-rate and groundedness on every change. When quality dips, the evals tell us which stage broke (chunking, retrieval, or prompting) instead of leaving everyone guessing.
Proof, not promises
Natural language to MongoDB, safely
An MCP server in TypeScript that turns plain-English questions into validated MongoDB queries, grounded in retrieved schema context with guardrails against destructive operations.
How we work
Four steps, each with an artifact
Artifacts beat adjectives. Every step of an engagement ends in something you can hold us to.
Step 1
Scoping call
→ Written scope & estimate
Thirty minutes on what you are building and why. You leave with a written scope, an honest estimate, and our view on whether AI is even the right tool.
Step 2
Architecture sprint
→ System design document
We design the system before we bill for building it: data flows, model choices, failure modes, and the success measure we will be judged against.
Step 3
Build in weekly demos
→ Working software, week one
Short cycles, working software every week, and decisions made in the open. You see progress in the product, not in status reports.
Step 4
Launch & run
→ Monitoring, evals & handover
We ship it, instrument it, and either run it with you or hand it over with documentation your team can actually operate from.
RAG pilots start with your real documents and twenty of your real questions. We agree the eval set first, then build until the numbers clear the bar, so 'is it accurate?' has an answer with a methodology attached, not a shrug.
Stack for this work
Straight answers
Questions buyers actually ask
What is RAG, in one paragraph?
Retrieval-augmented generation is an architecture where, before a language model answers, the system retrieves relevant passages from your own data and requires the model to answer from them, usually with citations. It turns a model that improvises from memory into a system that reports from sources, which is what makes it fit for policies, contracts, and product data.
RAG or fine-tuning: which do we need?
Choose RAG when the knowledge changes, must be citable, or is private; choose fine-tuning when you need a consistent style, format, or behavior the base model does not have. Many production systems layer both. Our fuller decision framework is in the article linked below. It is a one-read answer for most teams.
How accurate will it be, honestly?
Accuracy is a property of the whole pipeline, so we refuse to quote a number before measuring your case. What we commit to is the method: an evaluation set built from your real questions, scored for retrieval quality and groundedness, with the numbers shared as they are. Vendors quoting universal accuracy figures are quoting marketing.
Can this run on our infrastructure, for data security?
Yes. We regularly deploy on client cloud accounts or self-hosted infrastructure (we run our own products on k3s) and can keep embeddings, stores, and logs inside your boundary. Model calls can route to providers with no-training data terms or to self-hosted models where the case justifies it.
What does a RAG system cost?
A scoped pilot over a defined corpus with an evaluation set typically lands in the low-to-mid five figures (USD). Cost scales with source messiness, integration count, and the accuracy bar, not with document volume alone, which is cheaper than most teams expect.
Related AI development services
Engineering deep-dives on rag & llm applications
Next step
Talk to us about your rag & llm applications project.
Thirty minutes. You leave with a written scope and an honest opinion, including whether this is the right tool at all.
Prefer async? hello@visionnexera.com · We reply within one business day.