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AI EngineeringRAG & Knowledge Management
Build Enterprise RAG with Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) is a pattern that retrieves the most relevant knowledge before a model answers, so responses stay grounded in real sources. We design enterprise RAG systems with secure retrieval, vector search, citations, and a production RAG pipeline that turns documents, tickets, and knowledge bases into accurate answers.
RAG Capabilities
Production RAG Systems
Our RAG development services cover ingestion, retrieval, citations, and RAG as a service delivery so enterprise teams can launch grounded knowledge systems faster.
Document Intelligence Pipeline
Ingest PDFs, Word docs, slide decks, wikis, and emails — extract structured knowledge with OCR, table parsing, and layout understanding.
Vector Database Architecture
Design and deploy semantic search infrastructure with Pinecone, Weaviate, Qdrant, or pgvector for millisecond retrieval at scale.
Hybrid Search & Re-ranking
Combine dense vector search with BM25 keyword search, then re-rank with cross-encoders for maximum retrieval precision.
Knowledge Graph Integration
Layer graph databases over vector stores to capture entity relationships and enable multi-hop reasoning across your knowledge base.
Embedding Model Selection
Fine-tune or select domain-specific embedding models (OpenAI, Cohere, BGE, E5) optimized for your content type and retrieval task.
RAG Evaluation & Monitoring
Measure retrieval quality (MRR, NDCG), answer faithfulness, and hallucination rates with automated evaluation frameworks.
Why Choose Us
Why Agile Infoways for RAG Systems
We've built RAG pipelines processing millions of documents across legal, healthcare, and financial services.
Accuracy-First Engineering
We measure hallucination rates and retrieval precision at every stage — accuracy is a KPI, not an afterthought.
Chunking Strategy Experts
Semantic chunking, recursive splitting, and document-aware segmentation that preserves context and improves retrieval quality.
Production at Scale
RAG systems handling 10M+ documents with sub-200ms P99 retrieval latency using optimized indexing and caching layers.
Access Control & Privacy
Document-level permissions, PII redaction pipelines, and audit logging ensure your sensitive knowledge stays protected.
Our Capability
Technical Stack
Best-in-class tooling for every layer of the RAG pipeline, from ingestion and chunking to retrieval, re-ranking, monitoring, and grounded generation.
LlamaParse / Unstructured
Advanced document parsing for complex PDFs, tables, and multi-modal content.
Pinecone / Weaviate
Managed vector databases with metadata filtering and namespace isolation.
Elasticsearch + BM25
Hybrid search combining dense and sparse retrieval for best-of-both results.
LangChain / LlamaIndex
Battle-tested RAG frameworks with extensive connector libraries and retrieval chains.
RAGAS / TruLens
Automated RAG evaluation frameworks measuring faithfulness, relevance, and groundedness.
Presidio / AWS Macie
PII detection and redaction to keep sensitive data out of the vector index.
Our ApproachHow We Build
How We Build
RAG Pipelines
A rigorous process from data audit through production deployment with continuous quality measurement.
01
Knowledge Audit & Data Mapping
02
Ingestion & Embedding Pipeline
03
Retrieval Optimization
04
Evaluate, Monitor & Improve
Step 01
Knowledge Audit & Data Mapping
Catalog all knowledge sources, assess quality, identify gaps, and define the retrieval scope and access control requirements.
Source inventoryData quality assessmentAccess control matrixRetrieval scope definition
Step 02
Ingestion & Embedding Pipeline
Build robust ingestion with parsing, chunking, metadata enrichment, and embedding generation with incremental update support.
Document parsing pipelineChunking strategyMetadata schemaIncremental updates
Step 03
Retrieval Optimization
Benchmark retrieval approaches, tune chunk sizes, test re-ranking models, and implement query expansion for maximum accuracy.
Retrieval benchmarksRe-ranking tuningQuery expansionLatency optimization
Step 04
Evaluate, Monitor & Improve
Deploy automated evaluation with RAGAS or custom metrics, monitor drift in production, and run weekly improvement cycles.
Automated eval dashboardDrift detectionWeekly quality reportsContinuous improvement
Our ApproachHow We Build
How We Build
RAG Pipelines
A rigorous process from data audit through production deployment with continuous quality measurement.
Step 01
Knowledge Audit & Data Mapping
Catalog all knowledge sources, assess quality, identify gaps, and define the retrieval scope and access control requirements.
Source inventoryData quality assessmentAccess control matrixRetrieval scope definition
Step 02
Ingestion & Embedding Pipeline
Build robust ingestion with parsing, chunking, metadata enrichment, and embedding generation with incremental update support.
Document parsing pipelineChunking strategyMetadata schemaIncremental updates
Step 03
Retrieval Optimization
Benchmark retrieval approaches, tune chunk sizes, test re-ranking models, and implement query expansion for maximum accuracy.
Retrieval benchmarksRe-ranking tuningQuery expansionLatency optimization
Step 04
Evaluate, Monitor & Improve
Deploy automated evaluation with RAGAS or custom metrics, monitor drift in production, and run weekly improvement cycles.
Automated eval dashboardDrift detectionWeekly quality reportsContinuous improvement
Use Cases
RAG in Production
Real knowledge management systems delivering measurable accuracy improvements.
LE
LegalContract Intelligence Platform
The Challenge
Law firm lawyers spending hours searching thousands of contracts for precedents and clause variations.
The Outcome
RAG system over 500K contracts enables sub-second semantic search with cited clause extraction, reducing research time by 85%.
LlamaIndexPineconeGPT-4oCitation tracking
FI
FinancialRegulatory Compliance Assistant
The Challenge
Compliance team manually cross-referencing 10,000+ pages of regulations updated quarterly.
The Outcome
RAG assistant answers compliance questions with cited regulation text, cutting review time from days to minutes.
WeaviateBM25 hybridAudit trailVersion tracking
HE
HealthcareClinical Knowledge Base
The Challenge
Physicians unable to quickly access relevant clinical guidelines during patient consultations.
The Outcome
RAG system over 50K clinical guidelines provides real-time, evidence-cited recommendations at point of care.
Medical NLPHL7 FHIREmbedding fine-tuningPII protection
EN
Enterprise SaaSInternal Knowledge Copilot
The Challenge
Employees wasting 2+ hours daily searching Confluence, Notion, and Slack for internal knowledge.
The Outcome
Unified knowledge copilot across 5 sources answers questions with citations, saving 40 hours/week per 100 employees.
Multi-source ingestionSlack/ConfluenceAccess controlUsage analytics
Industries
AI Solutions Across Key Verticals
Deep domain expertise meets cutting-edge AI — delivering results where they matter most.
Before You Build
Planning Your RAG Implementation
The questions teams ask when evaluating retrieval-augmented generation — from cost and accuracy to security and fit.
8 questions answered
01
What is retrieval-augmented generation (RAG), and how does it work?
Retrieval-augmented generation (RAG) connects a large language model to your own documents: it retrieves the most relevant chunks from a vector database, then generates a cited answer grounded in them. Because knowledge lives outside the model, it stays current, traceable, and access-controlled — built on solid data engineering pipelines.
02
RAG vs fine-tuning: which does my use case actually need?
RAG and fine-tuning solve different problems, and most enterprise use cases need RAG. RAG injects current, proprietary knowledge at query time with citations, so answers never go stale; fine-tuning changes tone or format but bakes knowledge in. Use them together — add light custom model fine-tuning for domain language.
03
How much does it cost to build an enterprise RAG system, and how long until it's in production?
A production enterprise RAG system typically ships in 8–14 weeks, with a single-source pilot live in 6–8. Cost is driven by document volume, source systems, and accuracy targets — not the LLM. To control budget and timeline, many teams start by hiring dedicated AI/ML engineers who own the pipeline.
04
How do you stop a RAG system from hallucinating or giving wrong answers?
RAG hallucinations are controlled by grounding, then measuring it: hybrid search and re-ranking surface the right sources, prompts keep answers in-context with citations, and faithfulness is scored continuously with RAGAS. An optional agentic verification layer re-checks each answer against its sources before it's shown.
05
Is our proprietary data secure in a RAG system, and where does it live?
Yes — RAG security keeps your data under your control. Deployments run inside your own cloud tenant or on-premises, with document-level access control, PII redaction, encryption, and audit logging for SOC 2 or HIPAA. These controls and monitoring are part of the AI infrastructure and MLOps we stand up.
06
Which industries and use cases get the most value from RAG?
The strongest RAG use cases are legal, healthcare, financial services, and any team buried in internal knowledge — contract search, cited compliance answers, point-of-care guidance, or instant answers from wikis and tickets. Wherever staff lose hours hunting for information, a cited conversational AI assistant pays back fast.
07
Do you offer RAG development services or RAG-as-a-service?
Yes, we offer both RAG development services and a RAG-as-a-service style delivery model depending on how much ownership your team wants. Some clients need a custom build they run long term, while others want a faster managed rollout first. We usually scope the right fit during AI strategy consulting before implementation begins.
08
What does an enterprise RAG pipeline architecture look like?
An enterprise RAG pipeline usually includes ingestion, parsing, chunking, embeddings, a vector database, hybrid retrieval, re-ranking, prompt orchestration, citations, and monitoring around access controls. The exact shape depends on source systems, latency targets, and governance needs. We often pair retrieval with downstream AI automation workflows when answers should trigger approved actions.
Client Stories
Built With Trust. Proven in Production.
Hear directly from the leaders who partnered with us to ship AI-powered products, modernize platforms, and move faster than they thought possible.
"Great company with great management quality developers were really dedicated to get the job done in a timely cost-effective manner."
- Alexandar Salahsour
"They consistently delivers reliable, high-quality development solutions with exceptional communication, value, and trusted partnership."
- Joe Pellegrino, Jordan Pellegrino
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