So You Want to Build Your Own RAG System: An Enterprise Reality Check

Why On-Premises RAG?

Let's start with why you're even considering this. If you're working with healthcare data, financial records, defense contracts, or trade secrets, you already know why. The fines for data breaches aren't abstract—they're career-ending. HIPAA violations can cost up to $2 million per incident. GDPR fines can reach 4% of global revenue. If you're handling ITAR-controlled information, we're talking about potential jail time.

Your board doesn't care about the elegance of vector embeddings. They care that customer data never leaves your control. So let's talk about what it actually takes to build this yourself.

The Journey: Building Your Own Enterprise RAG System

I've built these systems. Here's what you're actually signing up for.

Step 1: Accessing Your Source Data

Enterprise data is messy. You have numerous silos: SharePoint, network drives, SQL databases, Confluence, and probably a dozen other systems. Each has its own authentication—LDAP here, OAuth there, Kerberos, bespoke, and others.

Writing connectors isn't hard, but writing reliable connectors is. You need to handle token refresh, connection pooling, retry logic, and graceful degradation when services are down. You need to respect rate limits without grinding to a halt. You need incremental synchronization and versioning so you're not reprocessing everything nightly.

Figure 4-8 weeks to get reliable connectors for your main data sources. That's with engineers who already know your infrastructure.

Step 2: Determining Content Type

File extensions lie. MIME types are suggestions. You'll encounter PDFs that are actually scanned images, Excel files that are HTML tables with .xls extensions, and documents with a variety of different encodings. You need a detection pipeline that examines file headers, tries multiple parsers, and has fallback strategies. For scanned documents, you need OCR. For tables, you need structure extraction. This isn't rocket science, but it's tedious work that has to be bulletproof, otherwise you're in for the daunting task of continual care and feeding of accurately identifying file types for problematic files.

Two to three weeks if you're focused and have dealt with document parsing before.

Step 3: Generating Rich Metadata

Raw text isn't enough. You need entity extraction—people, organizations, dates, amounts. You need document classification. You need language detection if you're multinational. You need PII detection for compliance. The challenge isn't running NER models—it's making them work for your domain. Generic models don't know your abbreviations, your product names, or your internal terminology. You'll need to adapt or fine-tune models, which means labeled data, which means involving subject matter experts who have better things to do. Beyond keywords and terms, you'll need schema detection, generation of a flattened data representation, an inverted index for search, and more.

This is 6-10 weeks of work, assuming you have feature extraction and metadata expertise on your team.

Step 4: Storing Metadata at Scale

Your metadata will be larger than your source data. Ten years ago, metadata was seen as a fraction of the size of source data, but today, metadata is the gasoline by which data systems are fueled. Your metadata needs to be queryable in milliseconds, versioned for compliance, and available to multiple services simultaneously. This means persistence and fast retrieval of potentially very complicated relationships amongst documents to their constituent parts and features, positional information about features, and a queryable form allowing you fast access to structured parts. You need proper indexing, caching layers, and a data lifecycle strategy. Some data needs to be kept for seven years. Other data must be deleted after 90 days. Get this wrong and audits could become problematic.

Four to six weeks to build something that won't fall over in production.

Step 5: Identifying Semantic Cells

Documents have structure. A PowerPoint has slides. PDFs have sections. Emails have threads. You need to preserve these boundaries because they matter for retrieval quality. This means building parsers that understand document structure for each format you support and the user intent behind that structure - or the chaos therein. Not just extracting text, but maintaining hierarchy and relationships. A footnote needs to stay connected to its reference. A chart caption needs to stay with its chart.

Eight to twelve weeks if you want to handle the common enterprise formats properly.

Step 6: Semantic Chunking

You need to split semantic cells into chunks that fit your model's context window while preserving meaning. Too small and you lose context. Too large and you waste tokens on irrelevant content. The optimal size varies by content type and model. Code needs different handling than prose. Tables shouldn't be split mid-row. You need overlap strategies so important information doesn't get lost at boundaries.

This work is time- and test-intensive and can go on for weeks and even months.

Step 7: Generating and Enriching Embeddings

You'll need multiple embedding strategies. Dense embeddings for semantic similarity. Sparse embeddings for keyword matching. Maybe domain-specific embeddings if you're in a specialized field. Generating embeddings at scale doesn't require GPUs—modern CPU implementations are fast enough for most workloads. But you do need infrastructure to handle the compute load, whether it's GPU or CPU, versioning for when you upgrade models, and systems to keep embeddings synchronized with your metadata.

Six to eight weeks including infrastructure setup.

Step 8: Storing Embeddings and Metadata

Vector databases are relatively new, and on-premises options are limited. You'll probably use something like pgvector, Weaviate, or Qdrant. These need to handle billions of vectors while maintaining sub-second query times. The challenge is maintaining consistency between your vectors and metadata, implementing proper backup strategies (traditional backup tools don't handle vector indices well), building sharding strategies for scale, and keeping HNSW indices up to date. And, not every DBA is familiar with vector embeddings, and will have questions about how adding new data types to existing systems might make their lives difficult.

Four to six weeks to get this production-ready.

Step 9: Building the Retrieval System

This is where it all comes together. You need hybrid search—combining semantic similarity with keyword matching and metadata filtering. You need query expansion to handle synonyms and acronyms. You need re-ranking to surface the most relevant results. Security is critical. Users should only see documents they have access to. This means integrating with your enterprise auth systems and implementing security in ways that preserve the intended protection of the source data. You also need evaluation metrics to know if your system is actually working. Precision, recall, and user feedback loops. Comments, feedback, thumbs-up, thumbs-down. Without measurement, you're flying blind, and with measurement, you need people to analyze and triage that feedback.

Eight to twelve weeks for a system that actually works well.

Step 10: Managing Model Infrastructure

You need to serve LLMs efficiently. This means choosing the right models (there are hundreds), setting up serving infrastructure (vLLM, TGI, Ollama, or similar), and implementing proper load balancing, queuing, failover, and protections to ensure models don't get kicked from RAM because someone changed a context window size mid-conversation. Models fail. Requests timeout. You need circuit breakers, fallback strategies, and graceful degradation. You need monitoring to know when quality degrades. You need A/B testing to evaluate new models safely.

Six to ten weeks with a team that understands both ML and infrastructure.

Step 11: Building the User Experience

Users need a clean interface. Not just a chat box, but a proper application with authentication, audit logging, source citations, and export capabilities. Enterprise users expect SSO. Compliance requires audit trails. Power users want API access. And then, you'll want these experiences to take action on behalf of the user (yes, agents). You'll probably need integrations—Slack, Teams, email. Each has its own quirks, authentication requirements, and of course, the problem compounds as soon as the code itself is able to take action on behalf of a user.

Eight to twelve weeks for something users will actually use.

The Hidden Costs

The engineering timeline is just the beginning. You need 24/7 operations once this is in production. You need security audits—both for compliance and because you really don't want to be the next breach headline. You need disaster recovery that actually works when tested. Technical debt accumulates fast. Every model upgrade potentially means regenerating all embeddings. Performance degrades as data grows—what worked for 10GB won't work for 10TB. External systems change their APIs constantly. And then there's the human cost. The engineers who built this become critical dependencies. When they leave—and they will—you lose not just people but institutional knowledge. The system becomes increasingly brittle as the original team moves on.

The Bottom Line

Realistically, you're looking at 6-8 months to build an MVP with a skilled team. Figure $1-2 million in engineering costs, plus infrastructure. Ongoing maintenance will cost you another $2 million annually.

Success rates aren't great. Many enterprise AI projects fail outright, don't scale beyond pilot, or are rendered inadequate as soon as new data types are required, new source data systems need to be integrated, or (gasp) the requirements change. The ones that succeed usually do so by dramatically reducing scope.

The Alternative: Just Use View

Here's why we built View: because we've been through this process, and it doesn't need to be this hard.

View handles data ingestion from all the standard enterprise sources. It automatically detects content types, extracts metadata, extracts cells, and chunks documents appropriately. It generates embeddings, maintains vector indices, and implements hybrid retrieval yielding state-of-the-art accuracy. The model infrastructure is pre-configured and optimized. And every aspect of the platform is configurable.

Most importantly, it's completely on-premises. Your data never leaves your infrastructure. No API calls to external services. No embeddings sent to the cloud. This isn't marketing—it's architecture.

When we say "full-stack", we mean "full-stack", not "it's full stack but requires an API key to cloud-hosted service XYZ to function".

What takes months to build from scratch deploys in minutes with View. Not because we've cut corners, but because we've already solved these problems. The connectors are battle-tested. The parsing handles edge cases. The retrieval actually returns highly relevant and accurate results.

As Victor Jakubiuk from Ampere noted: "Users can connect, process, search, and chat with data assets within an hour, all without giving up control of their data or sending it outside of their organization."

The Real Question

Building your own RAG system is absolutely possible. I've done it. But unless you have unique requirements that no existing solution addresses, it's probably not the best use of your engineering resources.

You could spend six months and seven figures building something that might work. Or you could deploy View and have something working today on a system you control and operate. Your data stays on-premises. Your compliance requirements are met. Your users get what they need.

The interesting problems in your organization probably aren't "how do we chunk documents" or "how do we implement hybrid search." They're domain-specific challenges that, when addressed, yield actual value to your business.

Focus on those. Let us handle the RAG infrastructure.

Ready to skip the build? Contact View and let's talk about your actual requirements.

Because building your own RAG system isn't just time-consuming - it's a solved problem.