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RAG Pipeline Development Company

RAG Pipeline Development —
Enterprise AI That Answers From Your Data.

We build production RAG systems that connect LLMs to your enterprise data — documents, databases, CRMs, and APIs — with near-zero hallucination, source citations, and 99.9% uptime. No generic chatbots. Your data, your answers.

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Senior AI Engineers On-Premise Options RAGAS Evaluation Multi-Source RAG
Vector Databases
Pinecone · Weaviate · Qdrant · pgvector
Embedding Models
OpenAI · Cohere · BGE · Sentence Transformers
LLM Orchestration
LangChain · LlamaIndex · LangGraph
Evaluation
RAGAS · TruLens · LangSmith
40+
RAG Systems Shipped
~0%
Hallucination Rate
99.9%
Uptime
Architecture Overview

How a Production RAG Pipeline Works

A production RAG pipeline is more than chunking PDFs and calling GPT. Every step — ingestion, embedding strategy, retrieval logic, generation, and evaluation — must be engineered for your specific data and query patterns.

Eval-First Architecture

We define RAGAS benchmarks before building. Every retrieval decision — chunk size, overlap, embedding model, reranker — is measured against your actual queries. No guessing, no demo quality.

Hybrid Retrieval by Default

Dense vector search catches semantic similarity. Sparse BM25 search catches exact keyword matches. Combining both maximizes recall on ambiguous queries — especially important for technical documentation and legal text.

01
Document Ingestion
PDFs, Word docs, databases, APIs, and web pages chunked with optimal overlap strategies. We handle multi-format parsing, metadata extraction, and incremental updates as your data changes.
02
Embedding & Indexing
Text converted to vector embeddings using the best model for your domain — OpenAI text-embedding-3, Cohere embed-v3, BGE-M3, or domain-specific fine-tuned embeddings — indexed in a scalable vector store.
03
Semantic Retrieval
Query-time semantic search with hybrid retrieval (dense + sparse) and cross-encoder reranking for maximum recall on ambiguous queries. Context windows are optimized per LLM target.
04
LLM Generation
Retrieved context injected into LLM prompts with precise citation, source attribution, and hallucination guardrails. Streaming responses with structured output schemas for downstream processing.
05
Evaluation & Monitoring
RAGAS metrics (faithfulness, answer relevance, context precision), retrieval quality scores, and latency dashboards via LangSmith or Helicone to continuously optimize accuracy post-launch.
What We Build

RAG Systems for Every Enterprise Use Case

From internal knowledge assistants to GDPR-compliant legal document analysis — every system is engineered for the specific accuracy, latency, and compliance requirements of its use case.

Internal Knowledge Assistants
Chat with your company's PDFs, wikis, Confluence, Notion, and Slack history. Semantic search + LLM answers with source citations — so employees stop asking the same questions and start finding answers instantly.
NotionConfluenceSlack
Customer Support Knowledge Bases
AI support assistants that answer from your product docs, FAQs, and ticket history — deflecting 60–80% of tier-1 support queries automatically, with accurate answers and escalation when confidence is low.
60–80% DeflectionEscalation Logic
Legal & Compliance Document Q&A
GDPR-compliant document analysis for legal teams — contract review, clause extraction, regulatory compliance checking, and risk flagging across thousands of documents in seconds, not days.
GDPR CompliantContract ReviewClause Extraction
Technical Documentation Search
Developer portals and API docs powered by semantic search — engineers find the exact answer in seconds, not minutes of Ctrl+F. Supports code snippets, version-specific answers, and multi-language docs.
API DocsCode SearchMulti-Version
Multi-Source Enterprise RAG
Federated RAG across databases, CRMs, ERPs, and APIs — a single AI interface to your entire enterprise knowledge graph. Query Salesforce, your SQL database, and your document store in one natural language question.
SalesforceSQLERPAPIs
Private Data RAG (On-Premise)
RAG systems where data never leaves your environment — built on Llama 4/Mistral via vLLM and self-hosted Qdrant on your private cloud infrastructure. Fully air-gapped for healthcare, finance, and defense applications.
On-PremisevLLMAir-Gapped
Technology Stack

RAG Technologies We Work With

Vector Databases
PineconeWeaviateQdrantpgvectorChromaMilvus
Embedding Models
text-embedding-3Cohere embed-v3BGE-M3E5-largeSentence BERT
Orchestration
LangChainLlamaIndexLangGraphHaystackDSPy
Evaluation
RAGASTruLensLangSmithHeliconeArize
Data Sources We Ingest
PDF / Word / Excel PostgreSQL / MySQL Confluence / Notion Salesforce CRM SharePoint Slack / Teams REST APIs / GraphQL GitHub / GitLab Jira / Linear Web Crawl S3 / GCS Email Archives
Start Your RAG Project

Book a Free RAG Architecture Audit

Tell us your data sources, your query patterns, and your accuracy requirements. A senior AI engineer will recommend the right vector database, embedding model, and retrieval strategy — free, no obligation.

45-Minute Technical Call
With a senior RAG engineer who knows your domain challenges
Architecture Recommendation
Vector DB, embedding model, retrieval strategy, and evaluation plan
Realistic Delivery Estimate
Timeline, accuracy targets, and cost before you commit
Related Services
AI Agent Development LLM Integration Services Hire AI Engineers LLM & RAG Services
90-Day Warranty

Every RAG system we deliver ships with a 90-day warranty. Retrieval accuracy dips after launch due to our code? We fix it — no invoice, no questions.

Chat with our RAG engineers
Talk to a RAG Engineer
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FAQ

Common Questions About RAG Pipeline Development

Everything you need to know before your architecture call. Have more questions? Talk to us

A RAG (Retrieval-Augmented Generation) pipeline connects an LLM to your own data — documents, databases, and APIs — so it answers from your specific knowledge instead of generic training data. You need one when a ChatGPT-style chatbot fails because it doesn't know your products, policies, or internal data.
For knowledge retrieval tasks, production RAG systems achieve 85–95% answer accuracy — comparable to fine-tuned models but cheaper to update. Codioo RAG systems use hybrid retrieval, reranking, and RAGAS evaluation to minimize hallucination and maximize retrieval precision on your specific corpus.
We select based on scale and query requirements: Pinecone for managed cloud-scale deployments, Weaviate for hybrid search with metadata filtering, Qdrant for high-performance on-premise use, and pgvector for teams already on PostgreSQL who want minimal infrastructure complexity.
A basic RAG pipeline for a single document corpus takes 3–6 weeks. A production-grade multi-source enterprise RAG with evaluation framework, monitoring, and fine-tuned retrieval typically takes 8–14 weeks. Complexity scales with number of data sources, query types, and accuracy requirements.
Yes. We build fully on-premise RAG pipelines using open-source models (Llama 4, Mistral via vLLM) and self-hosted vector databases (Qdrant, Weaviate on your servers). No data leaves your environment — critical for healthcare, finance, and legal applications with SOC2 or HIPAA compliance requirements.
Stop Your LLM Hallucinating. Start Answering From Your Data.

Book a free RAG architecture audit with a senior AI engineer. We'll review your data sources, recommend the right retrieval stack, and give you an accuracy and delivery estimate — free, no commitment required.

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