Your company has thousands of documents: policies, product specs, support tickets, contracts, engineering docs, Slack threads, meeting recordings. Your employees spend 20-30% of their work week searching for information they know exists somewhere.
Retrieval-Augmented Generation (RAG) fixes this. Instead of asking an LLM to generate answers from its training data (which doesn’t include your internal documents), RAG retrieves the relevant chunks from your actual data and feeds them to the LLM as context. The LLM generates an answer grounded in your real information, with source citations.
We’ve built RAG systems for companies ranging from 50-person startups to enterprises with millions of documents. The technology works. The difference between a RAG system that gives accurate answers and one that hallucinates garbage is in the engineering: chunking strategies, embedding models, retrieval algorithms, prompt design.
What can you build with RAG?
Internal knowledge bases
Your team asks questions in plain English and gets answers from your Confluence pages, Google Docs, SharePoint sites, and internal wikis. With source citations, so they can verify and go deeper.
“What’s our refund policy for enterprise clients who cancel mid-contract?” The system pulls the relevant clause from your Master Services Agreement template and your CS team’s internal policy doc, synthesizes a clear answer, and links to both sources.
Customer self-service
Your customers search your help center, and instead of getting a list of 15 articles to read through, they get a direct answer to their specific question, generated from your support docs, knowledge base, and product documentation. Support ticket volume drops 30-50%.
Document Q&A for specialized domains
Legal teams querying contract databases. Compliance officers searching regulatory filings. Medical researchers asking questions across thousands of papers. Financial analysts extracting insights from earnings calls and SEC filings. RAG handles domain-specific language that generic search engines fumble.
Developer documentation search
Engineers ask questions about your codebase, APIs, and internal tools. The RAG system pulls from your README files, API docs, architecture decision records, and even code comments. Onboarding time for new engineers drops from weeks to days.
Compliance and audit support
“Show me every instance where we mention data retention in our policies, contracts, and internal communications.” The system retrieves all relevant passages, organized by source and date, ready for audit review.
RAG architecture: from simple to advanced
Not every problem needs the same RAG architecture. We match complexity to your requirements. (When the system also needs to take action on top of retrieval, we build it as an AI agent with retrieval as a tool.)
Naive RAG
The simplest approach. Documents are chunked, embedded into vectors, stored in a vector database, and retrieved by semantic similarity when a query comes in.
How it works:
- Your documents get split into chunks (typically 500-1000 tokens).
- Each chunk gets converted to a vector embedding using a model like OpenAI’s text-embedding-3-large.
- Vectors are stored in a database (Pinecone, Weaviate, etc.).
- When a user asks a question, the question is embedded and the most similar chunks are retrieved.
- Retrieved chunks are passed to the LLM as context, along with the user’s question.
- The LLM generates an answer based on the retrieved content.
Best for: Homogeneous document collections (all the same type), straightforward factual queries, proof-of-concept builds.
Limitations: Struggles with multi-part questions. Misses relevant context that uses different terminology. Retrieval quality degrades as the document collection grows past 10,000 chunks.
Advanced RAG
Adds sophistication at every stage of the pipeline to improve retrieval accuracy and answer quality.
Key improvements over naive RAG:
- Hybrid search. Combines vector similarity search with keyword (BM25) search. Vector search understands meaning; keyword search catches exact terms, product names, and acronyms that embeddings sometimes miss.
- Re-ranking. After initial retrieval, a cross-encoder model re-scores results for relevance. This catches documents that are semantically relevant but scored lower in the initial retrieval. We typically use Cohere Rerank or a fine-tuned cross-encoder.
- Query transformation. The user’s question gets reformulated for better retrieval: expanding abbreviations, generating sub-questions for multi-part queries, and hypothetical document embedding (HyDE) for abstract questions.
- Intelligent chunking. Instead of fixed-size chunks, documents are split by logical boundaries: sections, paragraphs, semantic shifts. Metadata (document title, section heading, date, author) gets attached to each chunk for filtering.
- Citation tracking. Every chunk maintains a reference to its source document, page number, and section. The final answer includes clickable citations so users can verify.
Best for: Production systems with 10,000+ documents, mixed document types, enterprise deployments where accuracy matters.
Agentic RAG
The RAG system becomes an agent that can reason about how to retrieve information, not just retrieve and generate.
What makes it agentic:
- Multi-step retrieval. The agent breaks a complex question into sub-questions, retrieves answers for each, and synthesizes a final response. “Compare our Q3 and Q4 revenue by product line” triggers separate retrievals for Q3 data, Q4 data, and product line definitions.
- Tool use. The agent can query SQL databases, call APIs, and access structured data alongside the vector store. Not everything belongs in a vector database. Product catalogs, financial data, and inventory levels are better queried directly.
- Self-evaluation. The agent checks whether the retrieved context actually answers the question. If not, it reformulates the query and tries again. This loop reduces “I don’t know” responses and hallucinated answers.
- Routing. For organizations with multiple knowledge domains, the agent decides which vector store, database, or API to query based on the question type.
Best for: Complex enterprise environments with multiple data sources, questions requiring reasoning across documents, and use cases where accuracy is non-negotiable.
Let’s figure out which RAG architecture fits your data and use case.
Our vector database expertise
The vector database stores your document embeddings and handles similarity search at query time. Choosing the right one matters for performance, cost, and operational complexity.
| Database | Best For | Self-Hosted | Managed Cloud | Our Take |
|---|---|---|---|---|
| Pinecone | Teams that want zero ops overhead | No | Yes | Easiest to start with. Scales well. Limited filtering capabilities compared to Weaviate. |
| Weaviate | Complex metadata filtering, hybrid search | Yes | Yes | Our default for advanced RAG. Native hybrid search. Excellent filtering. |
| ChromaDB | Prototyping, small datasets | Yes | Yes | Great for proof-of-concept. We often start here and migrate to Weaviate or Pinecone for production. |
| pgvector | Teams already on PostgreSQL | Yes (extension) | Yes (via Supabase, etc.) | No new infrastructure. Good enough for datasets under 1M vectors. Performance degrades above that without careful tuning. |
| Qdrant | High-performance, large-scale | Yes | Yes | Strong performance. Good filtering. Growing ecosystem. |
We’re not married to any vendor. We pick based on your existing infrastructure, data volume, query patterns, and operational preferences.
Connecting to your existing data
RAG is only as good as the data it retrieves from. We build connectors for every major data source.
Document stores
- SharePoint / OneDrive. Full document library ingestion with permission-aware retrieval (users only see documents they have access to).
- Google Drive. Docs, Sheets, Slides, and PDFs with folder-level access control.
- Confluence. Space and page ingestion with real-time sync.
- Notion. Database and page content extraction.
File storage
- AWS S3 / Google Cloud Storage / Azure Blob. Bulk document processing from object storage.
- Local file systems. For on-premise deployments.
Databases
- PostgreSQL, MySQL, SQL Server. Structured data that complements unstructured document retrieval.
- MongoDB. Document databases with mixed content.
Communication tools
- Slack. Channel message history (with proper consent and access controls).
- Microsoft Teams. Chat and channel content.
- Email archives. When institutional knowledge lives in inboxes.
Specialized formats
- PDF (including scanned documents with OCR)
- Word, Excel, PowerPoint
- HTML / web pages (internal wikis, intranet sites)
- Markdown (engineering docs, README files)
- Code repositories (GitHub, GitLab: code, comments, PRs, issues)
Data ingestion isn’t a one-time job. We build pipelines that sync on a schedule (hourly, daily) or in real-time via webhooks. When someone updates a Confluence page, the vector store reflects the change within minutes.
RAG vs. fine-tuning vs. prompt engineering
This is the question every technical leader asks. Here’s when each approach works.
| Factor | RAG | Fine-Tuning | Prompt Engineering |
|---|---|---|---|
| What it does | Retrieves your data at query time and passes it to the LLM | Modifies the LLM’s weights using your data | Provides instructions and examples in the prompt |
| Data freshness | Always current, retrieves live data | Frozen at training time | Current (if data fits in context window) |
| Data volume | Millions of documents | Thousands of examples | Dozens of examples |
| Setup cost | $15,000-$80,000 | $30,000-$150,000 | $2,000-$10,000 |
| Accuracy on your data | High, grounded in retrieved sources | High for the patterns in training data | Moderate, limited by context window |
| Hallucination risk | Low (with proper guardrails) | Moderate (can still hallucinate beyond training data) | High (for domain-specific questions) |
| Best for | Answering questions about your specific documents | Changing the model’s behavior, tone, or reasoning patterns | Simple tasks, prototyping, low-data scenarios |
| Maintenance | Update document pipeline | Retrain periodically | Update prompts |
Our recommendation for most enterprise use cases. Start with RAG. It handles the “what does our data say?” question without the cost and complexity of fine-tuning. Add fine-tuning later if you need the model to reason differently, not just access different data.
Prompt engineering is always part of the solution. Even with RAG, the system prompt that tells the LLM how to use retrieved context, when to say “I don’t know,” and how to format citations is critical engineering work.
What it costs
Proof of concept: $15,000 – $25,000
- Single data source (e.g., your Confluence workspace)
- Naive or basic advanced RAG
- Web chat interface
- 4-6 week delivery
- Purpose: prove the concept works with your actual data before committing to a full build
Production system: $40,000 – $80,000
- Multiple data sources with sync pipelines
- Advanced RAG (hybrid search, re-ranking, intelligent chunking)
- Access control (users see only what they’re authorized to see)
- Analytics dashboard (query volume, satisfaction, failure modes)
- 8-14 week delivery
Enterprise agentic RAG: $80,000 – $150,000
- Agentic architecture with multi-step retrieval and tool use
- Multiple knowledge domains with intelligent routing
- SQL database integration alongside vector stores
- Custom evaluation framework with automated accuracy testing
- SSO, RBAC, audit logging
- On-premise or private cloud deployment
- 14-24 week delivery
Ongoing costs
- Vector database hosting: $50-$500/month depending on data volume
- LLM API costs: $200-$2,000/month depending on query volume
- Embedding costs: Minimal. $10-$50/month for most datasets.
- Infrastructure: $100-$500/month for orchestration and sync pipelines
Compared to US-based AI consultancies charging $250-$400/hour, our rates represent 60-70% savings. Our team of 20+ AI/ML engineers in Lahore works with the same tools, the same models, and the same architectural patterns, coordinated through our US office in Danville, California.
Get a scoped estimate for your RAG project.
Common RAG pitfalls (and how we avoid them)
Poor chunking destroys accuracy
If you split a document in the middle of a paragraph that explains a policy exception, the system will retrieve half the answer and generate something misleading. We use semantic chunking that respects document structure (headings, paragraphs, lists, tables), and we test chunking strategies against a set of ground-truth questions before deploying.
Retrieval without re-ranking misses context
Vector similarity search is good, but it’s not perfect. A question about “employee termination policy” might rank higher for a chunk about “software license termination” because the embeddings are similar. Re-ranking with a cross-encoder catches these mistakes. On our benchmarks, adding re-ranking improves answer accuracy by 15-25%.
No evaluation framework means no improvement
You can’t improve what you don’t measure. Every RAG system we build includes an evaluation pipeline: a set of test questions with known correct answers, run automatically after every change to the retrieval pipeline. If a new chunking strategy or embedding model degrades accuracy, we catch it before it hits production.
Ignoring access control creates security risks
If your HR documents and engineering docs are in the same vector store without access controls, a junior engineer could ask a question and get context from confidential HR files. We implement permission-aware retrieval that respects your existing access control model. If a user can’t see the source document, they can’t retrieve chunks from it.
Why build your RAG system with Contrive?
We’ve been shipping software for 12 years. RAG is an infrastructure project as much as it’s an AI project. It touches your databases, your file storage, your authentication systems, and your deployment pipeline. That’s software engineering, and we’ve been doing it since 2014 across 250+ projects. (See our process for the day-to-day cadence.)
20+ AI/ML engineers. Dedicated specialists for embeddings, retrieval optimization, LLM prompt engineering, and evaluation frameworks. Not a team that watched a LangChain tutorial last week and started selling.
We run production AI systems. Our voice agent platform and our own internal knowledge tools run on the same technology we sell to clients.
95% client retention. Projects like Skolaro (1.5M users), KanbanZone (50K users), Grundsteuer Digital, and vPeer demonstrate our ability to build and scale production systems.
Frequently Asked Questions
How accurate are RAG systems compared to a human expert searching the same documents?
In our evaluations, a well-built RAG system answers factual questions with 85-95% accuracy, comparable to a knowledgeable employee who takes time to look things up. The advantage is speed (2-5 seconds vs. 10-30 minutes) and consistency (the system doesn’t have bad days or forget to check a source). Where RAG falls short is nuanced judgment calls that require understanding organizational context beyond what’s documented. For those questions, the system should surface the relevant documents and let a human make the call.
How do you handle documents that change frequently?
We build sync pipelines that detect changes and update the vector store automatically. For Confluence and SharePoint, we use webhook-based triggers. When a page is updated, the new version is chunked, embedded, and indexed within minutes. For file storage (S3, Google Drive), we run scheduled sync jobs. The system maintains version history, so if a user asks a question and gets an answer based on a document that was just updated, the citation shows the current version.
What about hallucination? How do you prevent the system from making things up?
Three layers of defense. First, retrieval quality: if the system retrieves the right context, the LLM has the information it needs and is far less likely to hallucinate. Second, prompt engineering: we instruct the LLM to only answer based on the provided context and to explicitly say “I don’t have enough information to answer this” when context is insufficient. Third, citation verification: every answer includes source references, so users can verify claims against the original documents. On our production systems, hallucination rates are under 5%, and most of those are minor phrasing issues, not factual errors.
Can we start with a small pilot and expand later?
Absolutely, and we recommend this approach. Start with one data source and one team of users. Prove the value, collect feedback, refine. Then expand to additional data sources and user groups. Our architecture is designed for incremental expansion. Adding a new data source is a pipeline configuration change, not a rebuild. Most clients start with a $15,000-$25,000 proof of concept and scale to a full production system within 3-6 months.
Do we need to move our data to a new system?
No. Your data stays where it is: SharePoint, Confluence, Google Drive, S3, your databases. We build connectors that read from your existing systems, create embeddings, and store vectors in a separate database. The original data is never moved or copied in full. Only the vector embeddings (mathematical representations, not the text itself) and metadata live in the vector store. If you delete a source document, the corresponding vectors are removed on the next sync cycle.
Contrive Solutions builds RAG systems and enterprise knowledge solutions from our offices in Lahore, Pakistan and Danville, California. 12 years in business. 250+ projects. 20+ AI/ML engineers. Tell us about your data and we’ll design the right architecture. Or call: +1 (775) 459-7713 (US) or 042 35199410 / +92 327 4945650 (Pakistan) | connect@contrivesolution.com
