--- title: "RAG Development Services for Your Own Documents | Nexum" description: "RAG development services that build AI assistants answering from your documents, tickets and systems, and hand complex cases to a person." url: https://nexumautomations.com/services/rag-development-services updated: 2026-10-31 publisher: Nexum Automations, an AI consulting firm --- > Nexum Automations is an AI consulting firm that builds what it recommends. It finds where AI pays off in a company, builds it on the tools the team already uses and stays until it is live and working. Answers from your data # RAG development services grounded in your documents not in guesses. RAG development services and RAG consulting for support, operations and legal teams. We build AI assistants that search your documents and systems first, answer from what they find and pass hard cases to a person. 30 minutes. You leave knowing which 3 processes to automate first and what they cost you today. Trusted by companies worldwide **14-21 days** to your first live system **600+** processes automated **100+** hours returned to client teams every month Results ## Assistants and workflows already running. Two real systems that read a company's own material. 1. Government contracting ### Proposals cut from 60 hours to under one. A federal contractor bidding on $10M+ contracts could only submit 3-4 proposals a quarter, because each one took about 60 hours to assemble by hand. We built an AI workflow that pulls past performance, checks compliance and formats to spec, so the team now submits 12+ a quarter. **<1 h** per proposal, down from \~60 **3x** proposal capacity **+23%** win rate 2. SaaS / Software ### 85% of support tickets resolved by AI. A SaaS company with 50,000+ users had its best agents answering the same questions all day, and customers waited two hours for a reply. We built an AI assistant trained on their past tickets that answers instantly and hands complex cases to a person with full context. **1,200+** agent hours back every month **<5 s** first response, down from 2 h **+45%** customer satisfaction [See all case studies](https://nexumautomations.com/case-studies) Our data ## What this work costs by hand An estimate for one person who spends 10 hours a week on this work. Change the hours to match your team. | | Estimate | | ---------------------------------------------------- | ----------------------------- | | Median annual wage, customer service representatives | $44,782 (May 2025) | | Median hourly wage | $21.53 (annual / 2,080 hours) | | Hours a week on this work | 10 (example input) | | Estimated yearly cost per person | $11,196 (hourly x 10 x 52) | Estimated, wages only (no benefits). Wage from the BLS Occupational Outlook Handbook, May 2025: https://www.bls.gov/ooh/office-and-administrative-support/customer-service-representatives.htm. The hours are an example input, not a measurement. The problem ## A plain chatbot answers without knowing your business. 22%-94% Range of hallucination rates across 26 major AI models in one accuracy benchmark. 69%-88% Share of legal questions that general-purpose models got wrong in Stanford research. [Stanford HAI, AI Index 2026 ↗](https://hai.stanford.edu/ai-index/2026-ai-index-report/responsible-ai) Retrieval-augmented generation pairs a search step over your own content with a language model, so answers come from your material instead of from memory. * Answers from general training, so it can sound sure and be wrong. Looks up your own documents first and answers from what it finds. * Knows nothing about your policies, contracts or past tickets. Reads the policies, contracts and tickets your team already wrote. * Gives no way to check where an answer came from. Shows the passages behind each answer so a person can verify it. * Keeps guessing when the question is hard or unusual. Hands complex cases to a person with the context attached. Grounding turns a guess into a lookup. Is it time? ## Signs your team needs answers from your data. Signals you need this * Staff search several tools to answer one question * Support repeats answers already written in old tickets * Contracts or policies are hard to search by meaning * New hires take long to learn where information lives * An earlier chatbot invented answers When not to invest yet * Results are expected in the first days, not after a proper build * You want the cheapest option rather than the one that pays back * A report or an audit is the goal, not a working system * You cannot give access to your document sources or approve each stage What you get ## What our RAG development services include. * A map of the document sources and tasks where answers cost your team the most hours * An assistant that retrieves from your own documents before it writes any answer * Citations on each answer and permissions that respect who may see what * Hand-off of complex cases to a person with the full context attached * Testing on real questions end to end before anything goes live * Team training, plus documentation your people can use day to day * You own it all: the code, accounts, prompts and data. Our [generative AI services](https://nexumautomations.com/services/generative-ai) cover the wider build. How it works ## The Payback Loop. Five steps. Each build pays for the next. We don't start with the AI. We start with the money. One team of AI automation specialists runs all five 1. ### Map Consulting First, we find the manual work in your company and how many hours each task eats every week. Process map 2. ### Rank Consulting Each process gets a dollar figure and a rank by payback. You see what to automate first, and what to leave alone. AI roadmap, ranked by payback 3. ### Build Implementation We build the first system on the tools you already use. Nothing new for your team to learn. A working system 4. ### Prove Implementation Every system is tested end to end before launch. It doesn't go live until it works without errors. Tested, system live 5. ### Repeat Support The first build pays back fastest, so its savings fund the next one. Back to the map, and we keep every system running. Monthly support (optional) ↺ Back to Map Consultancies stop at the roadmap. Freelancers build without one. The loop only works when one team runs all five. You approve each step; we do the work. Your options ## Why not a big consultancy, a freelancer, or a new hire? vs Big consultancy vs Freelancer vs In-house hire | Nexum (done for you) | Big consultancy | Freelancer / no-code builder | In-house AI hire | | | -------------------------------------- | ----------------------- | --------------------------------- | ---------------------- | ----------------------------- | | Tells you what to automate first | ✓ | ✓ | Rarely | Sometimes | | Builds it on the tools you already use | ✓ | Usually hands off to another team | ✓ | ✓ | | Price fixed before work starts | ✓ | Rarely | Sometimes | A salary, not a project | | Time to first live system | 14-21 days | Months | Varies | Months to hire, then build | | You own everything built | ✓ | ✓ | Not always | ✓ | | Time from you, per week | \~1 hr review | Steering meetings | You manage the project | You manage the person | | Typical cost | Fixed price per project | Six-figure engagements | Hourly, open-ended | A full-time salary + benefits | Based on how each option typically works. If another option fits you better, we'll tell you on the call. FAQ ## Questions about RAG development. * ### What do RAG development services include? As a RAG development company, we connect your documents, tickets and systems, prepare them for search, build the assistant, add citations and permissions, and test it on real questions. Our glossary explains [retrieval-augmented generation](https://nexumautomations.com/glossary/retrieval-augmented-generation) in more depth. * ### Is RAG better than fine-tuning a model? For answers that must follow your current documents, usually yes. Retrieval reads fresh material at question time, while fine-tuning bakes knowledge into the model. The trade-offs are laid out in [RAG vs fine-tuning](https://nexumautomations.com/compare/rag-vs-fine-tuning). * ### How do you limit wrong answers? The assistant answers only from retrieved passages and shows them as sources. We run a test set of real questions before launch, and when confidence is low a person takes over the case. * ### Who can see which document? Permissions are part of the build. The assistant checks what each user may access before it retrieves, so a customer or colleague receives only content they could open themselves. * ### How does the content stay current? We connect the assistant to your live sources and refresh what it searches as documents change. Optional monthly support keeps that running and adds the next process on your roadmap. * ### How long does a build take? Plan on 1 to 2 weeks for the roadmap and 6 to 8 weeks for a full system. A first system usually goes live in 14 to 21 days. * ### How does RAG relate to MCP? RAG pulls from documents, while MCP connects a model to live tools and data. Many builds use both, as explained in [RAG vs MCP](https://nexumautomations.com/compare/rag-vs-mcp). Read about [computer vision development services](https://nexumautomations.com/services/computer-vision-development). --- Source: https://nexumautomations.com/services/rag-development-services Free AI diagnosis (30-minute call): https://nexumautomations.com/#book - Services: https://nexumautomations.com/services - Guides: https://nexumautomations.com/guides