RAG and Knowledge Systems

RAG and Knowledge System Development for Enterprise

We build retrieval-augmented generation systems that give your team accurate, citation-backed answers from your own data. Not a demo that works in ideal conditions. A production system that handles your real documents, your real questions, your real edge cases.

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Citation-Backed AnswersHybrid SearchProduction-GradeVeritas-Proven Technology

Enterprise RAG and knowledge systems that actually work

A bad RAG system means hallucinated answers, lost trust, and abandoned projects. Veritas, our content platform, is built on the same RAG technology we deploy for clients - knowledge graphs, hybrid search with re-ranking, and mandatory citation on every claim. We build for production from day one, not demos that break with real data.

Battle-Tested Technology

Veritas is built on our RAG stack. The same engineering that powers knowledge-grounded content generation powers what we build for clients.

Citation on Every Answer

Every response traceable to source documents with confidence scores. No black-box answers. Full transparency for users and auditors.

Hybrid Search

Combining semantic search, keyword search, and learned re-ranking for retrieval accuracy that pure vector search cannot match.

Multi-Format Ingestion

PDFs, Word docs, emails, Confluence, SharePoint, databases, spreadsheets - we handle the messy reality of enterprise data.

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End-to-end RAG and knowledge system capabilities

Specific, concrete deliverables - not vague promises. Here is what you get.

Data Audit and Retrieval Strategy

Assess your data sources, quality, formats, and access patterns. Design a retrieval strategy optimized for your specific use case.

Document Ingestion Pipeline

Multi-format processing for PDFs, Word, email, Confluence, SharePoint, and databases. Handling tables, images, and complex layouts.

Chunking and Embedding Optimization

Custom chunking strategies based on your document types. Embedding model selection and optimization for your domain vocabulary.

Vector Store Architecture

Selection and configuration of the right vector database for your scale and requirements. Pinecone, Weaviate, pgvector, or Qdrant.

Hybrid Search and Re-ranking

Combining semantic and keyword search with learned re-ranking models. Significantly better retrieval accuracy than pure vector search.

Citation and Confidence Scoring

Every answer traceable to source documents with confidence levels. Configurable thresholds for when the system says it does not know.

Evaluation and Testing Framework

Systematic accuracy measurement with test datasets, regression testing on every update, and benchmark tracking over time.

Production Deployment and Monitoring

Latency optimization, cost management, query analytics, drift detection, and automated alerting on retrieval quality degradation.

The numbers that matter

12+

Content types generated by Veritas using our RAG stack

100%

Citation coverage on Veritas-generated content

Sub-2s

Target query latency for production deployments

Multi-format

PDF, Word, email, Confluence, databases supported

Real deployments, real results

Content and Marketing

Veritas content intelligence platform

Knowledge graphs powering citation-backed content generation across 12+ content types

Finance Automation

FlowFin AI assistant

RAG-powered querying across 20+ finance modules with natural language interface

Financial Services

International development finance organization

Knowledge retrieval system deployed for document analysis and workflow automation

The Optivus Method

Every engagement follows four phases. You always know what is being delivered and what comes next.

01

Scope

Audit your data sources, understand your users' questions, and design the retrieval strategy. We test with sample data before committing to the full build.

02

Build

Ingestion pipeline, chunking optimization, vector store setup, search and re-ranking tuning. Weekly demos with your actual data and real questions.

03

Ship

Production deployment with monitoring on retrieval quality, latency, and user satisfaction. Documentation and training for your team to manage the system.

04

Scale

Add new data sources, optimize based on query analytics, tune re-ranking models on real usage, and expand to new use cases within the organization.

Learn more about The Optivus Method

Products we have built with this technology

We do not just consult - we build production AI products. The same engineering powers what we deliver for clients.

Industries we serve

Our AI expertise transfers across industries. The underlying technology applies regardless of domain.

Ready to discuss your project?

Book a 30-minute call. Tell us about your workflow and we will scope the right approach together.

Common questions about rag and knowledge systems

Retrieval-Augmented Generation (RAG) is a technique that grounds AI responses in your actual data. Instead of relying on the AI model's training data (which leads to hallucinations), RAG retrieves relevant documents from your knowledge base and uses them as context for generating accurate, citation-backed answers.
RAG is better for most enterprise use cases. It works with your existing data without expensive model training, provides citation-backed answers, and updates instantly when your data changes. Fine-tuning is better when you need the model to learn a specific style or behavior pattern. Most clients need RAG. Some need both. We help you decide based on your specific requirements.
Accuracy depends on data quality, chunking strategy, retrieval method, and the specific use case. A well-built RAG system with hybrid search and re-ranking typically achieves 85-95% retrieval accuracy on production queries. We measure accuracy systematically with test datasets and track it over time. We also implement confidence scoring so the system tells users when it is not confident in an answer.
PDFs, Word documents, PowerPoint, Excel, emails, Confluence, SharePoint, Notion, databases (PostgreSQL, MySQL, MongoDB), APIs, web content, and custom formats. If your data is digital, we can ingest it. The harder question is data quality - we audit your data and tell you honestly what will work well and what needs cleanup.
A focused RAG system for one data source and use case takes 3-6 weeks. Enterprise knowledge systems with multiple sources, complex retrieval, and evaluation frameworks take 8-16 weeks. We ship a working prototype within the first 2 weeks so you can test with real questions early.
Multiple layers: citation requirements (every claim must reference a source document), confidence scoring (the system says it does not know when retrieval confidence is low), hybrid search (reducing the chance of irrelevant retrieval), and systematic evaluation (measuring hallucination rates and tracking them over time). No system is perfect, but we minimize hallucinations to acceptable production levels.
It depends on your scale, existing infrastructure, and requirements. pgvector is great if you already use PostgreSQL and have moderate scale. Pinecone offers managed simplicity for cloud-native setups. Weaviate and Qdrant provide more control for complex retrieval patterns. We recommend based on your specific situation, not a one-size-fits-all answer.
Operational costs include embedding generation (typically $0.01-0.10 per 1000 documents), vector storage ($10-100/month depending on scale), and LLM inference for answer generation ($0.01-0.10 per query depending on model). For most enterprise deployments, total operational cost runs $200-2000/month. We help optimize costs as part of the build.

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