Document Ingestion
PDFs, Word docs, Confluence, Notion, Slack, email archives — we build pipelines that ingest, parse, and keep your knowledge base current.
AI That Actually
Knows Your Data
Not a chatbot with amnesia. A production RAG system that ingests your documents, understands context, and gives accurate answers — with sources.
ChatGPT Doesn't Know Your Business
Generic LLMs hallucinate. They don't know your products, your policies, your contracts. You've probably tried pasting docs into ChatGPT — it works for a demo, falls apart in production.
Real RAG is an engineering problem: ingestion, chunking, embedding, retrieval, ranking, generation, evaluation. You can't shortcut any of it. Every layer matters, and most "AI solutions" on the market are thin wrappers around a single API call — no retrieval strategy, no evaluation, no monitoring. They work in a notebook. They fail under real load with real data.
Production RAG requires engineering discipline — the kind that comes from building systems, not playing with demos. We solve the whole stack.
The Full RAG Stack
Intelligent Chunking
Context-aware splitting that preserves meaning. Not naive 500-token blocks — semantic boundaries that make retrieval actually work.
Vector Search & Hybrid Retrieval
Dense embeddings meet sparse keyword search. Re-ranking, metadata filtering, and multi-index strategies for precision at scale.
LLM Routing & Optimization
The right model for the right query. Cost-efficient routing between GPT-4, Claude, open-source — with fallbacks and rate limiting.
Hallucination Guardrails
Citation tracking, confidence scoring, and answer grounding. When the system doesn't know, it says so — instead of making things up.
Evaluation & Monitoring
Automated quality scoring, retrieval metrics, user feedback loops. You'll know exactly how well your RAG system performs — and when it degrades.
From Documents to Answers
Audit your data
We map your document landscape — formats, volumes, update frequency, access patterns. This shapes every architectural decision downstream.
Build the pipeline
Ingestion, embedding, indexing, retrieval, generation. Each layer tuned for your data, your queries, your accuracy requirements.
Deploy & monitor
Production infrastructure with logging, metrics, and alerting. Your RAG system gets smarter over time — and you can prove it.
Don't Take Our Word for It
Niro helped me turn a concept into a fully functioning Minimum Viable Product which I will be proud to take to stakeholders, potential collaborators and funders. The speed of development only enhanced the design quality because tweaks were so quick to make. Moreover, he rapidly helped with: - design & userX for an EdTech product for children, young people and adults - keeping the MVP lean and focused but without limiting its capacity to grow and be developed in the future. - packaging it up so I can take it to a different developer in the future if needed. The Iron Mind approach is to thoroughly explain the technology behind the design decisions. As a result, I felt that we not only built a working product from first principles, but I was upskilled along the way! This was my first time working with Niro and I hope not the last!
Niro created an excellent website, exactly to our specifications, and did so qiuckly. The AI assistant he built is intuitive and allows us to change and further develop our online presence. His skills are impressive and he was highly responsive and charming in all our interactions.
For years, Niro has been my go-to expert for building CRM systems, structuring databases, and developing clear strategies for managing client relationships in a truly organized way. With IronMind AI, that vision has fully come together. The platform creates a clean, streamlined ecosystem that brings outreach, CRM, and day-to-day operations into one cohesive flow. What really stands out is the ability to solve problems quickly and approach challenges from fresh, practical angles—removing obstacles that have been slowing things down for years. I highly recommend working with IronMind AI to anyone looking to elevate their systems, simplify their workflow, and move to the next level with clarity and efficiency.
Iron Mind built us a complete SDR performance dashboard in 4 days. It integrates SalesLoft and HubSpot in real-time, tracks KPIs, and gives us full visibility into team performance—something we'd been trying to solve for months. Their use of agentic coding is next level. What normally takes weeks, they delivered in days without sacrificing quality.
What Ironmind and Niro Knox pulled off for me was unreal—my custom secret network proxy app went from idea to fully running in a single day, right when my business needed it most. The speed, precision, and execution weren’t just impressive—they were business-saving, and honestly felt like having an unfair advantage on demand.
Working with Ironmind and Niro was a game-changer for us at KaizIn. I had a vision: a fast, AI-powered personal branding platform that could generate LinkedIn covers, post creatives, and YouTube thumbnails in under a minute. They didn’t just execute, it felt like they were building alongside us. They nailed both the product and the experience. If you're building something in AI, you want a team like Ironmind.
We truly are in a new dawn where an entire backend system was built for us in less than a week. We are incredibly pleased with the work done on our website. From the start, the process was highly professional, quick, and thorough. The developer adapted the design completely to our specific requirements, ensuring the final product aligned perfectly with our vision. Beyond the aesthetics, we were impressed by the technical execution—the code is well-optimized for performance, and the site was fully prepped for SEO right out of the gate. If you are looking for a developer who is reliable, detail-oriented, and capable of delivering a tailored, high-performance site, we highly recommend their services. We couldn’t be happier with the result!
Frequently Asked Questions
What is a production RAG system?
A production RAG (Retrieval-Augmented Generation) system is an AI pipeline that retrieves relevant chunks from your own documents before generating an answer. Unlike a raw LLM, it grounds responses in your data — with citations, confidence scoring, and monitoring. Production-grade means it runs under real load, handles edge cases, logs every query, and degrades gracefully when retrieval confidence is low.
How is Iron Mind's RAG approach different from a simple vector search?
Vector search is one layer. Iron Mind builds the full stack: document parsing, semantic chunking, hybrid retrieval (BM25 + dense vectors), re-ranking, LLM routing, hallucination guardrails, citation tracking, and automated evaluation. Simple vector search returns candidate chunks — a production RAG system turns those chunks into accurate, auditable answers with measurable quality metrics.
What document formats can be ingested?
We ingest PDFs, Word documents (.docx), Excel spreadsheets, PowerPoint files, plain text, Markdown, HTML, Confluence pages, Notion exports, Slack message archives, and email (MBOX/EML). For structured databases — PostgreSQL, MySQL, Snowflake — we build custom connectors. Proprietary formats are evaluated case by case during the audit phase.
How do you prevent LLM hallucinations in RAG?
Multiple layers: every answer is grounded in retrieved chunks with source citations; a confidence threshold gates whether the LLM answers or returns "I don't know"; system prompts enforce strict grounding rules; and automated evaluation frameworks (including LLM-as-judge and human spot-checks) continuously measure faithfulness scores. Monitoring alerts fire when answer quality degrades.
How long does a RAG system take to build?
A focused RAG system over a well-scoped document set typically takes 3 to 6 weeks from audit to production deployment. Complex scenarios — multiple data sources, enterprise auth, custom evaluation pipelines, or real-time sync — extend that to 6 to 8 weeks. Timeline is fixed before any code is written; we scope the build precisely after the document audit.
What does a RAG system cost?
Production RAG systems start at $35k for a single-source, focused deployment. Multi-source enterprise RAG with custom evaluation and monitoring runs $50k–$80k depending on scope. All projects are fixed-price — no hourly billing, no scope creep surprises. Ongoing LLM API costs (OpenAI, Anthropic) are separate and typically $100–$1,000/month depending on query volume.
Make Your Data Talk
Tell us about your documents and what you need from them. We'll design a RAG architecture and give you a timeline.
Message received. We'll be in touch within 4 hours.
Prefer to chat?
Your data has the answers.
You just need the right system to find them.