RAG Development Services
Teams lose hours hunting for answers scattered across databases, documents, and systems. Kodexo Labs builds RAG development services that connect large language models to a company's own data. So staff get accurate, sourced answers in seconds instead of waiting on the engineering team.
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TRUSTED BY ENTERPRISES
Every retrieval project starts with one question: where does the right answer live, and how fast can your people reach it? Here is exactly what a Kodexo Labs retrieval build handles for you.
Our Core Capabilities:
Retrieval pipelines that pull the exact document, record, or row you need.
Vector database setup using FAISS, Pinecone, Qdrant, or ChromaDB.
Text-to-SQL search so plain questions query your live databases directly.
Semantic and hybrid search with reranking for sharper, sourced results.
Secure deployment inside your own AWS VPC or cloud.
Accuracy testing that proves answers before staff rely on them.
IN THE NEWS
AI products shipped for clients across 25+ industries
AI development company verified on Clutch
Client retention rate across our active engagements
Expert Team shipping AI since 2021
How We Build Your RAG System
Good answers depend on good retrieval. Each build below solves a different piece of the problem, from picking the right vector database to proving its accuracy. Your teams trust every answer they get from the system.
Custom Retrieval Pipeline Design
Generic search returns pages of near-misses, not answers. A retrieval pipeline routes each question to the exact source, then returns the right passage.
Each question is sent to the source most likely to hold its real answer.
The right paragraph comes back on its own, so nobody reads a full file.
Stop Losing Hours To Slow, Scattered Data Searches
Your people still dig through systems for answers that should take seconds. Let's map where a retrieval build saves the most time first.
Three teams stopped waiting on slow searches and manual lookups. See what changed after each build shipped.
SmartMedHx
Clinicians were losing an hour a day to note-taking during patient visits. Kodexo built a HIPAA-compliant documentation system that captures the patient interview and structures the clinical record, using the same compliance-first approach behind secure retrieval work. Today 42-plus providers use it daily, 493 patient interviews are processed, and the patent-pending AI stays HIPAA-compliant.
42+
Providers
493
Patient Interviews
HIPAA
Compliant
Extensiv
Extensiv's operations team waited on engineering for every data question, so routine decisions stalled. Kodexo built an agentic system on LangGraph. It reads plain-English questions and answers them directly from the full operational database across 207 tables and 4 databases. The Inc. 5000 team now self-serves answers at 90%-plus accuracy, without waiting on engineers.
90%+
Accuracy
207
Tables
04
Databases
Diesel Laptops
Fleet technicians spent more time searching diagnostic records than fixing trucks, and every minute of lookup meant a truck sitting idle. Kodexo built an AI search system that finds the right answer across 160,000 technical records in seconds, deployed in a self-hosted AWS VPC. The Inc. 5000 team cut its lookup time by 85%.
160,000+
technical records
AWS VPC
self-hosted
85%
reduction
What Clients Say About The Team
Fast-growing organisations do not applaud a consulting partner for polished slide presentations; they praise it for showing up when something actually breaks. The notes below come from founders who watched Kodexo Labs work the problem in real time.
Kodexo Labs has met all expectations; the team delivers on time and manages the project seamlessly. They respond promptly to needs and communicate effectively through virtual meetings, Google Chat, and WhatsApp. Overall, they're highly passionate about the project and excel in customer service.
Christopher Brigham
MD President, Brigham and Associates, Inc.
WATCH VIDEO
- Patient record retrievalClinical note groundingPrior-authorization lookupCare-guideline citation
Retrieval Grounded In The Data Each Industry Actually Runs
Every sector stores knowledge differently, in claims files, contracts, service logs, or catalogs. Answers hide across systems until someone reads them. Retrieval connects staff to the right record, sourced and current, the moment a question gets asked.
Build a HIPAA-compliant, agentic, or omnichannel chatbot. Let's scope it.
Sector-specific platforms either compound their data advantage or fall behind the operators investing now. Find out where yours sits.
Security Controls That Keep Regulated Data Inside Your Walls
Regulated data carries strict rules about where it lives, who may read it, and what gets logged. Kodexo builds retrieval on the controls below, mapped to your obligations. Put plainly, your sensitive records stay protected, auditable, and inside boundaries your compliance team already trusts.
SOC TYPE 2
ISO 27001
HIPAA
GDPR
CCPA
COPPA
NIST AI RMF
EU AI Act
FERPA
PCI-DSS
SOC TYPE 2
ISO 27001
HIPAA
GDPR
CCPA
COPPA
NIST AI RMF
EU AI Act
FERPA
PCI-DSS
Proof From The Retrieval Systems We Shipped, One Real Client Story Per Card
Every card below points to a live retrieval build, a real named client, and a measured result. No stock claims. Read what each system actually changed, then picture the same rigor pointed at your data.
Text-to-SQL Over Live Databases
Extensiv staff once waited on engineers for each data question. We built a LangGraph system that reads plain English over 207 tables and 4 databases at 90 percent accuracy. They now self-serve daily.
Search Across 160,000 Records
Diesel Laptops techs once dug through 160,000 technical records to fix a single truck. Our layer cut lookup time by 85 percent and runs in a self-hosted AWS VPC, so data stays in-house.
HIPAA-Safe Notes With Citations
SmartMedHx needed notes that meet HIPAA and do not slow its clinicians. Our system ties each note to the patient's own chart, cited and traceable. It serves 42 providers and 493 patient interviews.
Grounded Lease Clause Retrieval
A commercial real estate firm spent six billable hours per lease. Our retrieval reads the contract, flags clauses, and lists risk in fifteen minutes, a 97.5 percent cut, with the source in view.
Your Own Data Already Holds The Answers Your Team Needs
Right now that knowledge sits locked in systems only engineering can query. Retrieval opens it to the people who need answers daily. Bring us your hardest data question, and we will show you.
Recognition Worth Naming
Independent platforms and client reviews put Kodexo among the firms buyers trust for AI work. The badges below come from third-party evaluation, not self-assigned labels or paid placement.
Overcoming Common RAG Development Services Challenges
Most retrieval systems look impressive in a quick demo, then stumble once real users and real documents arrive. Wrong answers erode trust, accuracy quietly slips as data grows, and late compliance reviews stall launches. We fix all three before production.
The Retrieval Stack
No single tool builds good retrieval. We choose each vector store, model, and orchestration layer to fit your data, your accuracy targets, and your security rules. The technologies below are building blocks we assemble around, not partnerships we resell.
From First Scoping Call To Production Handoff
Discovery and Scoping
We start by learning what questions your team needs answered and where that knowledge lives. We audit your data sources, define success metrics, and map security requirements upfront so nothing derails the launch later.
Architecture and Database Selection
Next, we choose the vector database and retrieval approach that fit your data volume and latency needs. Whether that means Pinecone, Qdrant, or PostgreSQL with pgvector, the architecture is documented before any code ships.
Pipeline Build
Our engineers build the ingestion pipeline, generate embeddings, and wire up the retrieval logic that connects your sources to the model. We add reranking and citations so every response stays grounded in verifiable content.
Evaluation and Testing
Before launch, we test retrieval accuracy against real queries and known-good answers, measuring precision at the volumes you actually run. We tune chunking, embeddings, and filters until results hold up under production conditions.
Deploy and Handoff
Finally, we deploy inside your cloud environment, connect monitoring, and hand your team clear documentation. You get a working system, plus the knowledge to run, extend, and trust it long after we step away.
Related Insights
What is Agentic RAG?
September 2025 · By Kodexo Labs
Did you know that 73% of enterprises struggle with knowledge retrieval from their vast data repositories? Agentic RAG (Retrieval-Augmented Generation) is revolutionizing how AI systems access, process, and utilize external knowledge to deliver more accurate and contextually relevant responses. For businesses developing custom software and web applications, understanding agentic RAG is crucial for building intelligent, knowledge-driven AI solutions that can autonomously navigate complex information landscapes.
Top Agentic AI Platforms in 2025: A Complete Guide for Businesses
October 2025 · By Kodexo Labs
Are businesses ready for the autonomous AI revolution that’s transforming enterprise operations in 2025? Top agentic AI platforms are enabling companies to deploy intelligent agents that can make decisions, execute tasks, and interact with customers independently, fundamentally changing how organizations operate. This comprehensive guide explores the leading agentic AI platforms, their capabilities, and strategic implementation approaches for modern businesses.
Reactive vs. Proactive AI Agents: What’s the Difference?
August 2025 · By Kodexo Labs
Explore the differences between reactive and proactive AI agents, their decision-making processes, business applications, and implementation strategies. Learn how reactive AI excels in real-time responses (e.g., customer service chatbots) and proactive AI drives long-term value through predictive analytics (e.g., predictive maintenance), with hybrid approaches delivering 40% better performance.
RAG Development FAQs
RAG, short for retrieval-augmented generation, connects a language model to your own knowledge base. Instead of relying only on what the model memorized during training, the system first retrieves relevant documents. It then generates an answer grounded in that retrieved content. This keeps responses current, accurate, and traceable to real sources your team already trusts.