$50K–$250K typical projects
Direct answer
Enterprise RAG development with me runs $50K–$250K: the low end covers a single-corpus internal knowledge assistant with evaluation and access control built in, while the top end covers multi-source RAG platforms with hybrid retrieval, permission-aware indexing, and compliance requirements. I'm Top Rated on Upwork with $100K+ earned and verified client reviews, I've worked as a Guest Engineer at Expensify, and I bring 7+ years of production delivery. Every build starts with a golden question set from your actual users, because RAG accuracy claims mean nothing until they're measured on your corpus.
Enterprise RAG projects don't fail because the language model is weak — they fail because retrieval returns the wrong passages and because documents leak to employees who were never allowed to read them. This service treats those two problems, retrieval quality and permissions, as the core of the build rather than afterthoughts.
You need an MVP or v2 shipped on budget with someone who makes architecture decisions and owns delivery end-to-end.
You need a senior IC to augment the team, rescue a codebase, or lead mobile + AI integration without months of hiring.
You need a reliable senior subcontractor for client projects — clear communication, store-ready quality, white-label friendly.
30 minutes — goals, stack, timeline, budget range.
Fixed milestones, clear deliverables, start date.
Weekly demos, async Slack updates, production standards.
Store launch, documentation, knowledge transfer.
Employees ask the same questions in Slack that are answered somewhere in Confluence, Drive, and old tickets. I build ingestion across those sources, hybrid keyword-plus-vector retrieval, and an assistant that answers with citations — honoring each source's access permissions per user. The outcome is answers people can verify, restricted documents that stay restricted, and measured accuracy on a real question set.
A support team wants ticket deflection, but a public-facing bot that hallucinates policies is a liability. I build grounded generation over the docs corpus with strict no-answer behavior when retrieval confidence is low, citation links, and a feedback loop feeding the eval set. The outcome shape: measurable deflection with an auditable record of what the system claimed and its source.
A team shipped a RAG pilot that impressed nobody — wrong passages retrieved, confident nonsense generated. I build a retrieval evaluation first, then overhaul chunking, add hybrid BM25-plus-vector search and reranking, and tighten the generation prompt against the eval set. The outcome is a before-and-after accuracy number on the same questions, which is what decides whether the pilot lives.
When a RAG system gives a bad answer, the cause is almost always upstream of the model: the right passage wasn't retrieved, or it was buried under near-duplicates, or the document was chunked so that the answer straddles two fragments. Fixing generation prompts while retrieval is broken is rearranging deck chairs.
So the build order matters: measure retrieval in isolation first — does the right passage appear in the top results for each question in the golden set — and only then tune generation. Hybrid search combining keyword and vector retrieval, sensible chunking that respects document structure, and a reranking stage do more for answer quality than any prompt engineering, and that's where I spend the budget.
In a company, documents have owners, groups, and confidentiality levels — and a naive RAG system flattens all of that into one index that will happily summarize the compensation plan for anyone who asks nicely. Permission-aware retrieval isn't a feature to add later; it shapes the ingestion architecture, the index design, and the query path from day one.
The requirement is simple to state and expensive to fake: every retrieval must be filtered by what the asking user is allowed to read, at query time, staying correct as source permissions change. Any vendor who waves at this with 'we can add access control later' is proposing a data breach with extra steps. It's the first architecture conversation I have on every enterprise RAG engagement.
Weeks one and two produce two artifacts: a corpus audit (what sources, what formats, what permission models, how stale) and a golden set of 100+ real questions with known-correct answers gathered from the people who'll use the system. That question set is the project's definition of success.
Weeks three through six build ingestion and retrieval, measured continuously against the golden set — retrieval quality first, then grounded generation with citations and explicit no-answer behavior. The remaining weeks cover permission enforcement testing, freshness pipelines so updated documents reindex automatically, observability, and a staged rollout to a pilot group whose feedback feeds the eval set before wider release.
Source count and permission complexity dominate. One corpus with uniform access — say, public product docs — is the $50K–$80K zone. Each additional source system brings its own connector, format quirks, and permission model to honor, and reconciling five permission models into one query-time filter is genuine architecture work.
The other big lever is deployment constraints. If compliance requires models and indexes to run inside your VPC or on-premises rather than against API providers, infrastructure and model-serving work grows substantially. Add freshness requirements — minutes versus nightly — and formal audit trails of every answer, and you're describing the top half of the range. The model API bill, which buyers fixate on, is rounding error next to these.
The loudest red flag is an accuracy promise made before anyone has seen your corpus or built an eval set — accuracy on a demo dataset transfers to your documents roughly never. Second: proposals that say 'we embed everything and use semantic search' with no mention of hybrid retrieval, chunking strategy, or reranking; that's the architecture of every failed pilot I've been hired to rescue.
Third: no citation strategy. If the system can't show which document each claim came from, users can't verify answers and will correctly stop trusting it. And fourth, silence about permissions — ask directly what happens when a user asks about a document they can't access, and walk away from anyone who improvises the answer.
If your corpus is small and stable — a few hundred pages that rarely change — you may not need RAG at all; good search or even a well-organized FAQ delivers most of the value at a tenth of the cost. RAG earns its complexity when the corpus is too large to navigate and changes too often to memorize.
The other wrong reason is hoping RAG will fix documentation chaos. If your documents are outdated and contradictory, the system will retrieve outdated, contradictory passages and cite them beautifully — garbage in, confidently-grounded garbage out. In that case, spend first on curating the corpus; I'd rather scope that honestly than bill for a RAG system that faithfully surfaces your worst documents.
You approve milestones and a price before any build starts — no open-ended hourly surprises.
You see running software each week, not status reports, so you can course-correct early.
The person who scopes the work is the person who builds it — no junior layers, no agency markup.
Top Rated on Upwork with public client reviews and $100K+ earned, plus contributions to Expensify. Check the receipts before you commit.
Chat-Based Financial Collaboration
Contributed 50+ merged pull requests to New Expensify — the production React Native app used by millions for chat-based expense management — working through Expensify's fully public open-source process alongside their core engineers, and recognized with a Guest Engineer invitation to their Curaçao offsite.
Food Truck Discovery Platform
Production food-truck discovery platform for US cities — customer and vendor React Native apps on iOS and Android, plus customer and vendor web, with AI features. Helps people find trucks nearby while vendors manage schedules, orders, and events.
Rewards & Engagement Platform
End-to-end React Native app for the US market with gamified rewards, real-time engagement, and secure payment flows — built from product scoping to App Store launch.
Production enterprise RAG runs $50K–$250K. A single-source internal assistant with hybrid retrieval, citations, evaluation, and basic access control lands at $50K–$80K. Multi-source ingestion, per-user permission enforcement, VPC or on-premises model hosting, near-real-time freshness, and audit requirements each add materially, with fully-loaded platforms reaching $250K. The eval set and permission architecture are where the money goes — the LLM API costs are comparatively trivial.
On a well-curated corpus with hybrid retrieval, reranking, and strict grounding, RAG systems answer most golden-set questions correctly and — just as important — decline to answer when retrieval confidence is low. But no honest number exists before measuring on your corpus with your questions. That's why I build the golden question set in week one: it converts 'how accurate' from a sales claim into a tracked metric you watch improve.
Yes. Embeddings, the vector index, and the language model can all run inside your VPC or on-premises using open-weight models, so no document text ever leaves your infrastructure. Expect trade-offs: self-hosted models trail frontier APIs somewhat in generation quality, and infrastructure work grows, which is part of what pushes engagements toward the upper end of the range. For regulated industries it's the standard architecture, not an exotic request.
Projects typically fall in the $50K–$250K range depending on scope, integrations, and timeline. I provide a fixed-scope proposal after a 30-minute scoping call.
MVPs often ship in 8–12 weeks. Production systems with AI backends or RAG may run 12–20 weeks. Rescue and audit engagements can start within days.
Yes. I work with founders, CTOs, product teams, and agencies worldwide — US, UK, EU, and APAC time zones with async updates and weekly demos.
Yes. I specialize in React Native + Python (FastAPI) + AI (RAG, agents, OpenAI/Claude) under one senior owner — fewer handoffs, faster shipping.
Book a free 30-minute scoping call on this site, hire through Upwork, or email dhairyasenjaliya@gmail.com with your brief and timeline.
30-minute scoping call · Clear milestones · Senior engineer ownership