RAG Engineering & Deployment
RAG Development Services to Enhance Your Applications with Powerful AI Capabilities
Our RAG development services turn your scattered documents, wikis, and databases into a knowledge assistant your team and customers can actually trust — with citations, access control, and answers grounded in your own data, not the open internet.
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The Problem With "Just Add a Chatbot"
Most companies' knowledge is scattered across PDFs, Notion, Confluence, Google Drive, ticketing systems, and internal databases. Search is broken. New hires can't find answers. Support teams re-answer the same 40 questions every week. And when a company bolts a generic LLM chatbot on top of this mess, it hallucinates — because it was never given your actual data to ground its answers in.
A real RAG (Retrieval-Augmented Generation) system fixes this at the architecture level: it retrieves the right information from your own sources first, then generates an answer constrained to that information, with a citation back to the source document. That's the difference between a demo that looks impressive and a system your team will actually rely on.
What Is RAG, and Why Not Just Fine-Tune?
RAG and fine-tuning solve different problems, and most companies that try one when they needed the other waste months and budget.
RAG
Best for: Frequently changing data, Q&A over documents, citation requirements
Data freshness: Updates instantly when source documents change
Cost to maintain: Lower — update the knowledge base, not the model
Source attribution: Built-in, cites the retrieved document
Typical timeline: 2–6 weeks
Typical use case: Internal knowledge assistant, support copilot, document Q&A
Fine-Tuning
Best for: Teaching a model a fixed skill, tone, or domain reasoning pattern
Data freshness: Requires retraining to update knowledge
Cost to maintain: Higher — retraining cycles for every major update
Source attribution: Not possible, answers come from model weights
Typical timeline: 4–10 weeks
Typical use case: Domain-specific writing style, structured output, specialized reasoning
Most production systems we build end up using both — RAG for knowledge grounding, with a fine-tuned model where tone or output structure matters. If you're unsure which you need, our retrieval-augmented generation consulting starts with an AI Strategy & Architecture Audit built specifically to answer that question before you spend on either.
What We Build
A production-grade RAG system, not a weekend prototype:
Ingestion pipelines for PDF, DOCX, HTML, Notion, Confluence, Slack, and structured databases
Chunking and embedding strategy tuned to your content type — technical docs, legal contracts, and support tickets all need different approaches
Hybrid search (vector + keyword/BM25) — pure vector search alone under-retrieves on exact terms, model numbers, and acronyms
Source citations on every answer — no black-box responses your team can't verify
Role-based access control (RBAC) so users only ever retrieve content they're authorized to see
Conversational memory for multi-turn follow-up questions
Evaluation harness (retrieval precision/recall + answer faithfulness scoring) before anything goes live — see our LLM Evaluation & Benchmark Engineering service for the deeper version of this
Monitoring and feedback loops post-launch to catch retrieval drift as your content grows
Build vs. Buy
Off-the-shelf RAG-as-a-service tools get you a demo in a day — and then hit a wall on access control, citation accuracy, and handling messy real-world documents (scanned PDFs, inconsistent formatting, nested tables). Choosing to build a RAG system for your business with a dedicated engineering partner costs more upfront, but it's the only path to enterprise-grade accuracy, compliance (RBAC, data residency), and a system that improves as you tune it — rather than a black box you're stuck waiting on a vendor to fix.
Who This Is For
Healthcare networks needing clinical SOP or protocol assistants with strict access control by role and department
SaaS platforms embedding a documentation/support copilot directly into their product
EPC and engineering firms with decades of technical documents that need to be searchable by spec, not just keyword
Financial services teams needing compliance-aware internal knowledge assistants with full audit trails
Internal IT/HR teams replacing broken intranet search with a real Q&A system
Trusted Across 50+ Countries
Codersarts maintains a 4.9/5 client satisfaction rating across hundreds of engagements. Clients consistently point to clarity and responsiveness as the differentiator — Tan (Malaysia) described the team's explanations as a turning point for his project, while Jing (China) highlighted the patience and thoroughness of the team's technical support.
Results
A Series B fintech platform reduced average support resolution time by 40% after we deployed a citation-backed RAG assistant over their policy and compliance documentation.
A multi-location hospital network in India deployed a clinical SOP assistant across 6 departments with role-based access, replacing a manual lookup process that previously took staff 10–15 minutes per query.
A global EPC engineering firm cut technical document search time from hours to seconds across a 50,000+ document archive spanning three decades of project records.
(Client names withheld under NDA; case studies available on request.)
Pricing
Starter
Scope: Single knowledge source, standard hosting, core retrieval + citations
Price: $6,000–$12,000 + $200–$500/mo
Production
Scope: Multi-source ingestion, hybrid search, RBAC, eval harness, monitoring
Price: $20,000–$40,000 + $500–$1,500/mo
Enterprise RAG Deployment / On-Prem
Scope: Self-hosted deployment, compliance/data residency, SSO, dedicated support
Price: $45,000–$80,000 + $1,000–$2,500/mo
For context: simple RAG builds in the US market typically run $15,000–$25,000, production-grade systems $40,000–$80,000, and enterprise on-prem deployments $80,000–$150,000+. Our pricing reflects high-quality offshore delivery at 35–55% of those rates — same engineering rigor, lower cost base.
How We Work
Architecture audit (Week 1) — review your data sources, access requirements, and success criteria
Build (Weeks 2–5) — ingestion pipeline, retrieval layer, evaluation suite
Pilot & tune (Week 6) — test against real queries, tune retrieval thresholds, fix edge cases
Launch & retainer — deploy to production, ongoing monitoring and optimization
Why Codersarts
As a RAG engineering company, we've shipped systems for document-heavy industries — healthcare, engineering, finance — where a wrong or unsourced answer isn't an option. Citation-first design is built in from day one, not bolted on after launch. You work with an engineering team operating under a fixed-scope contract, not a single freelancer billing by the hour with no defined deliverable.
Related Services
LLM Evaluation & Benchmark Engineering — for teams that need deeper eval/red-teaming than the standard RAG harness
AI Agent Development — when your assistant needs to take actions, not just answer questions
LLM Integration & API Orchestration — for wrapping multiple models/providers into one product feature
AI Strategy & Architecture Audit — if you're unsure whether RAG, fine-tuning, or both fit your case
Get Started
Book a Free Architecture Audit →
FAQ
How long does a typical build take? Starter tier: 2–3 weeks. Production tier: 5–6 weeks. Enterprise/on-prem: 8–12 weeks depending on compliance requirements.
Can you work with our existing vector database or LLM provider? Yes — we're provider-agnostic and integrate with Pinecone, Weaviate, pgvector, and others, and with OpenAI, Anthropic, or open-source models depending on your constraints.
What happens after launch? Production and Enterprise tiers include a monthly retainer for monitoring, retrieval tuning, and handling new data sources as your content grows.
Do we need fine-tuning instead of RAG? Usually no — RAG solves knowledge-grounding problems, which is what most "the chatbot doesn't know our content" complaints actually are. Fine-tuning is for changing model behavior, not adding knowledge. Our architecture audit will tell you definitively which (or both) you need.
How do you handle data security and access control? RBAC is built into the retrieval layer itself — users only ever retrieve chunks they're authorized to see, enforced at the database query level, not just the UI. Enterprise tier includes self-hosted/on-prem deployment for full data residency control.
What if our documents are messy — scanned PDFs, inconsistent formatting? This is exactly the kind of real-world document handling generic RAG-as-a-service tools fail on. Our ingestion pipeline includes OCR, layout-aware parsing, and table extraction as standard, not an add-on.