ORVINUSRAG Systems & AI Chatbot Development
A chatbot that hallucinates is a liability. We build assistants grounded in your documents, with citations to prove it.
Retrieval-augmented generation (RAG) is the architecture that makes AI chatbots trustworthy: instead of answering from the model's general training, the system first retrieves the relevant passages from your documents — manuals, contracts, knowledge bases, tickets — and instructs the model to answer from that evidence, with citations.
Building RAG that works in production is a data engineering problem more than a model problem. Answer quality is decided by ingestion and chunking strategy, embedding and index choice, retrieval tuning, and reranking — long before the LLM writes a word. It's exactly where naive implementations fail and where we spend our engineering effort.
The same foundation powers document intelligence beyond chat: extraction, classification, and summarization pipelines that turn unstructured files into structured, searchable data your business can act on.
RAG (retrieval-augmented generation) systems ground AI chatbot answers in an organization's own documents using vector search and citations. ORVINUS builds production RAG chatbots and document intelligence systems including ingestion pipelines, permission-aware retrieval, extraction with confidence scoring, and measured answer-quality evaluation.
Retrieval-Augmented Chatbots on Your Knowledge
We build the full RAG pipeline: document ingestion with format-aware parsing (PDF, DOCX, HTML, tickets, databases), chunking strategies tuned to your content's structure, embedding generation, and vector search with reranking so the model always receives the best evidence available.
The chat layer on top handles what real deployments need: conversation memory, source citations users can click, confidence thresholds with honest 'I don't know' behavior instead of guessing, permission-aware retrieval so users only see answers from documents they can access, and feedback capture that turns usage into an improvement dataset.
Document Intelligence Systems
Document intelligence turns files into data: extracting fields from invoices and contracts, classifying incoming documents, summarizing reports, and validating that extracted values make sense before they enter your systems. We built exactly this for doorlist.ai's real-estate document processing.
Pipelines are engineered for accuracy you can audit: extraction confidence scores, human-review queues for low-confidence cases, and structured outputs that flow directly into your database or workflow — not another folder of PDFs.
Built With
The technologies we reach for on this work — and why we use each one.
See what we use every tool for in the full technology stack.
What You Get
Ingestion pipeline
Automated document intake, parsing, chunking, and indexing that keeps the knowledge base current as sources change.
Grounded chat assistant
A production chatbot with citations, memory, permissions, and honest uncertainty — embedded in your product or internal tools.
Retrieval quality report
Measured answer accuracy on a test set from your domain, with the tuning decisions documented.
Document intelligence workflows
Extraction and classification pipelines with review queues, feeding structured data into your systems.
Other AI MVP Development Specializations
LLM Integration Services
OpenAI, Anthropic, and Gemini models wired into your product — with the API architecture to run them reliably at scale.
Explore LLM IntegrationAI Copilots & Workflow Automation
Assistants embedded in your product and automations that run your workflows — with analytics dashboards that show the impact.
Explore Copilots & AutomationFull-Stack SaaS Development
The complete product around the AI: frontend, backend, payments, admin, access control, and cloud deployment.
Explore Full-Stack SaaSWhere This Work Lands
SaaS & Startups
Ship an AI-powered product in weeks — on architecture that won't need rebuilding when you grow.
AI for SaaS & StartupsReal Estate & PropTech
Property matching, lead intelligence, and document automation for teams that move faster than the market.
AI for Real Estate & PropTechE-Commerce
Personalization, automation, and predictive intelligence for stores that compete on experience.
AI for E-CommerceCommon Questions
How is RAG different from just using ChatGPT?
ChatGPT answers from general training data; a RAG system retrieves the relevant passages from your documents first and answers from that evidence with citations. That's the difference between plausible-sounding text and answers your team can act on.
How do you prevent the chatbot from making things up?
Grounding plus honesty engineering: retrieval supplies evidence, prompts constrain answers to that evidence, confidence thresholds trigger 'I don't know' responses instead of guesses, and evaluation suites measure factuality on your domain before and after every change.
Can the chatbot respect user permissions on documents?
Yes — permission-aware retrieval filters the search index by the requesting user's access rights, so answers only ever draw on documents that user is allowed to see. Critical for legal, HR, and multi-tenant deployments.
How much data do we need for a useful RAG system?
Less than teams expect — a few hundred well-chosen documents often outperform a sprawling unmaintained wiki. Discovery includes a corpus audit; quality and structure of sources matter far more than raw volume.
Ready for RAG & Chatbots?
Free discovery call — we scope the work, name the trade-offs, and respond within 24 hours.