"Agile Infoways team delivered exceptional iOS and Android apps with responsive support and outstanding problem-solving expertise."
- Rob Machado
AI EngineeringRAG & Knowledge Management
Turn Your Data into Enterprise Intelligence with RAG
Build retrieval-augmented generation systems that ground AI responses in your proprietary knowledge — delivering accurate, citeable answers from internal documents, databases, and knowledge bases.
RAG Capabilities
Production RAG Systems
From document ingestion to real-time retrieval, we build knowledge pipelines that make your enterprise data AI-ready.
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 tools for every layer of the RAG pipeline, from ingestion to 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.
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
Get In Touch
Let's Build Something Remarkable Together
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