RAG Development Services. Ground Your LLM on Enterprise Data. Zero Hallucinations.
- 150+ production RAG systems shipped. Pinecone, Weaviate, pgvector, Qdrant, Chroma + LangChain / LlamaIndex specialists on the bench.
- Production RAG live in 4–14 weeks. Hybrid search, reranking, citation surfacing, Ragas evals — not a demo-day chatbot.
- Model-agnostic embeddings + LLMs. Claude, GPT-5, Gemini, Cohere, Voyage, on-prem. You own the code + IP.
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Trusted by Startups, SMBs, and Fortune 500 Brands
Trusted by 300+ Global Brands. Here’s What They Say.
Verified client feedback from CIOs, CTOs, and product leaders shipping production software with DreamzTech — the reviews behind our 96% retention rate and 200+ 5-star ratings on Clutch, Google, G2, and Goodfirms.
End-to-End RAG Development Services, Configured to Your Enterprise Data
Six RAG development services we ship on repeat. Not toy demos. Not off-the-shelf ChatGPT wrappers. Production retrieval-augmented generation systems grounded on your private data — with hybrid search, reranking, citation surfacing, Ragas evals, and full observability. Wired to Claude, GPT-5, Gemini, Cohere, or your on-prem LLM. Production RAG live in 4–14 weeks.
- Claude
- Perplexity
- OpenAI
- Google Antigravity
- Gemini
- Grok
✓Vector Database Setup · Pinecone, Weaviate, pgvector, Qdrant, Chroma
✓Document Ingestion & Chunking Pipelines · PDFs, Docs, Wikis, DBs
✓Hybrid Search & Reranking · BM25 + Semantic + Cross-Encoder
✓Enterprise RAG Copilots · Grounded Chat Assistants on Your Knowledge Base
✓Embedding Model Selection & Fine-Tuning
✓RAG Evaluation, Guardrails & Anti-Hallucination
- Offices in Arizona & Nevada
- Inc. Regional 2026 Winner
- ISO 9001 / 27001
- SOC 2 Type II
- 300+ Global Clients
Recent RAG Development Projects
Real production RAG systems shipped by DreamzTech as a specialist RAG development company — running in customer workflows today, cutting cycle times, and paying back within a quarter. Not lab demos. Not off-the-shelf ChatGPT wrappers. Live retrieval-augmented generation on enterprise data.
Legal · RAG on Contract Repository
Legal Services Firm — RAG Development Services on 40K Contracts, 3-Second Answers
A legal services firm needed to query 40K historical contracts + clause libraries in natural language — instead of paying junior associates to grep PDFs. We delivered end-to-end RAG development services: layout-aware clause chunking, hybrid semantic + BM25 search on pgvector, Cohere Rerank on top-K, and Claude Sonnet as the reasoning layer with mandatory source citation. Lawyers get grounded answers in ~3 seconds. Ragas faithfulness score: 0.94. Live in 10 weeks.
Fintech · Enterprise LLM Copilot
US Neobank — Customer-Support RAG Copilot on Banking Docs + Real-Time Account APIs
A US neobank needed a chat-first customer support copilot that could answer account questions, look up transactions, explain fees, and process routine service requests. We built a RAG copilot grounded on their FAQ + banking policies (Weaviate + Cohere Rerank), with live account API tool-calls for real-time balances and transactions. Every answer surfaces citations. PII redaction, audit trails, and SOC 2 Type II compliant delivery. Now handles 45% of what used to hit the call center.
SaaS · Knowledge-Base RAG
Enterprise SaaS Vendor — RAG on 200K Support Docs, 62% Deflection of L1 Tickets
An enterprise SaaS vendor needed to cut L1 support ticket volume across 6 product lines without hiring more agents. We built a RAG system over 200K knowledge-base docs, help articles, and release notes: Unstructured-based ingestion, pgvector on Postgres, hybrid BM25 + dense retrieval with Cohere Rerank, and GPT-4o for grounded answers with mandatory citations. Multi-tenant ACLs keep customer content scoped. Ragas context precision: 0.91. 62% of L1 tickets now self-served. 9 weeks from scoping to production.
Three Ways to Ship Your RAG System. One Standard of Delivery.
Three delivery shapes tuned for where your RAG program is today. Same senior RAG engineers, same US-based Project Manager, same eval + guardrail discipline — structured to match your appetite for AI risk and timeline.
Path 01 · RAG Sprint
Prove ROI with a 4-week RAG proof-of-value
Not sure if RAG will work on your corpus? Start with a 4-week RAG Sprint. We ingest a slice of your knowledge base, build the vector DB + hybrid search + reranker + eval harness, and prove retrieval quality before you commit to a full build. Fixed scope, fixed price.
- 4 weeks · fixed scope · fixed price
- Working RAG POV on your real data
- Ragas eval report + roll-out plan on day 30
Path 02 · End-to-End RAG Build
Ship a production RAG system in 4–14 weeks
Ready to build? We take one high-value use case — enterprise RAG copilot, knowledge-base assistant, multi-tenant SaaS RAG, or multimodal search — and ship a production-grade retrieval-augmented generation system in 4–14 weeks. Full RAG stack: ingestion, vector DB, hybrid search, reranking, evals, citation surfacing, observability.
- Full RAG stack: vector DB + hybrid search + rerank
- Ragas evals + citation surfacing
- Production monitoring & drift detection
Path 03 · Dedicated RAG Squad
Long-term RAG capability, embedded on your team
Rolling out RAG across multiple business units? Get a dedicated squad of RAG engineers, embedding specialists, MLOps, and evals engineers embedded in your team. Ship a new RAG use case every 6–10 weeks. From $22/HR.
- RAG engineers + embedding specialists + MLOps
- One RAG use case shipped every 6–10 weeks
- From $22/HR · scale on 30-day notice
Why Build Your RAG System with DreamzTech?
The RAG development company Fortune 500 brands, mid-market SaaS platforms, and PE-backed portfolios trust to ship production retrieval-augmented generation systems — not ChatGPT wrappers. 150+ RAG projects delivered. US-based project management. LangChain, LlamaIndex + Pinecone, Weaviate, pgvector, Qdrant, Chroma specialists on the bench. Full IP + model-agnostic delivery on Claude, GPT-5, Gemini, or your on-prem LLM.
Custom RAG Development Services vs Off-the-Shelf ChatGPT & Boilerplate RAG SaaS
See how a custom-built RAG system from DreamzTech stacks up against off-the-shelf ChatGPT Enterprise, boilerplate RAG SaaS tools, and template chatbots — on retrieval accuracy, faithfulness, IP ownership, model flexibility, and time-to-production.
| Metric | Off-the-Shelf AI / ChatGPT Wrapper | DreamzTech Custom RAG |
|---|---|---|
| Retrieval quality | Vector-only search · frequent off-topic chunks | Hybrid BM25 + dense + rerank · 30–50% MRR@10 lift |
| Grounding & citations | No citations · can't audit an answer | Every answer cites sources · click through to the original doc |
| Faithfulness / hallucinations | "Trust the model" · no eval harness | Ragas + custom evals · continuous drift monitoring |
| Fit to your workflow | Generic · you conform to vendor UX | Built around your ops · your data, ACLs, CRM/ERP tools |
| Time to production | 6–18 months typical consulting build | 4–14 weeks · production RAG, not a POC |
| Model & vector-DB lock-in | One vendor's LLM + hidden vector store | Model-agnostic · swap Pinecone/Weaviate/pgvector, Claude/GPT-5/Gemini |
| Code & IP ownership | SaaS lock-in · you rent the RAG | 100% to client · NDA + day-one transfer |
Award-Winning Excellence in RAG Development Services
Recognized globally as a top-tier RAG development company — Inc. 5000, Inc. Regional 2026, TIME Fastest-Growing 2026, Forbes Select 200, Clutch, Goodfirms, and G2 — for shipping 150+ production retrieval-augmented generation systems with US-based project management, model-agnostic delivery, and full IP transfer.
Get Your Free RAG Roadmap in 24 HRs
What Our Clients Say About DreamzTech
Real feedback from real engagements to help you make a confident decision.
"It's been really amazing since the time we have been involved with DreamzTech. We have been doing a lot and would not have been able to make it without the support from DreamzTech. We have achieved our goals, accomplished a lot of things. I just want to put this note for this possible team partnership with this big US firm that has a long long way to go."
"Ever since our collaboration with DreamzTech, our business is now known for its innovation and agility. We are able to deliver projects consistently on time and budget. Our technological capabilities have boomed with specific attributes put in place with our partnership with DreamzTech and your incredibly talented developers who work with us."
"It's a great pleasure to speak about DreamzTech. They have been our partner since the inception of our company. They helped us to build and scale our technology to all over the world that is being used by major brands today. Without their help we would not be able to achieve what we have today and I thank DreamzTech for all that they have done for us."
"It's been really amazing since the time we have been involved with DreamzTech. We have been doing a lot and would not have been able to make it without the support from DreamzTech. We have achieved our goals, accomplished a lot of things. I just want to put this note for this possible team partnership with this big US firm that has a long long way to go."
"Ever since our collaboration with DreamzTech, our business is now known for its innovation and agility. We are able to deliver projects consistently on time and budget. Our technological capabilities have boomed with specific attributes put in place with our partnership with DreamzTech and your incredibly talented developers who work with us."
"It's a great pleasure to speak about DreamzTech. They have been our partner since the inception of our company. They helped us to build and scale our technology to all over the world that is being used by major brands today. Without their help we would not be able to achieve what we have today and I thank DreamzTech for all that they have done for us."
A 4-Step RAG Development Process Built for Outcomes, Not Billable Hours
From your first scoping call to production RAG launch — clear milestones, weekly demos, AI-augmented sprints. You see working retrieval quality on your real data every week. No black-box “trust us” timelines.
Discovery & Corpus Scoring
30-minute scoping call. We score your use case for RAG viability — corpus quality, chunking difficulty, retrieval feasibility, model fit, ROI potential. Roadmap and estimate delivered in 24 hours.
RAG Architecture & Guardrails
Our RAG architects design the full pipeline — ingestion strategy, chunking, embedding model, vector DB, hybrid search + reranker, LLM selection, eval harness, guardrails, PII redaction, ACLs, human-in-the-loop review. Reviewed with your security + compliance team before Sprint 1.
Build, Eval & Pilot
2-week sprints. Every sprint runs the RAG system against your real corpus with automated evals. Ragas + custom eval harness for faithfulness, answer relevance, context precision, context recall, and hallucination detection. Human-in-the-loop shadow mode for the first 2 weeks post-launch.
Production & Continuous Improvement
Full production RAG launch. Observability dashboards: retrieval hit rate, MRR@10, faithfulness score, token cost, latency, hallucination rate, user feedback signals. Monthly retrieval-quality tuning cycles. Chunking + embedding + reranker A/B tests. Cost optimization ongoing.
Frequently Asked Questions About RAG Development Services
The retrieval quality, cost, safety, and integration questions US CTOs, Heads of AI, and enterprise operators ask before they hire a RAG development company.