RAG Development Services
RAG Development Services
Generic AI-driven solutions often make mistakes and produce incorrect results. We provide RAG development services and create systems that work with your data, deliver accuracy and control, and provide secure integration into business processes.
Turning Unreliable AI into a Trusted System
AI without access to corporate documents does not create real value. It responds with general phrases, but cannot rely on policies, regulations, or internal processes.
Pain Points
Hallucinations & inaccuracies
If the system doesn’t have access to your internal documents, it starts making up answers that don’t match the reality of your business. This means the risk of wrong decisions, erroneous recommendations, and a loss of trust in the entire RAG as a service initiative.
Lack of source citations
When a user receives an answer without a clear reference to a document or policy, it cannot be verified. The lack of transparency in the sources means the answer cannot serve as the basis for an official decision. AI becomes a “black box” rather than a tool that can be trusted.
Data privacy risks
Many companies fear that integrating AI could lead to the leakage of sensitive information. Using public models without a clear access architecture creates compliance and reputational risks. Without access controls and a secure infrastructure, AI can become a source of vulnerability.
Outdated information
Most LLMs have limitations on the training date. They are unaware of new internal regulations, updated policies, or process changes. As a result, responses may be based on outdated information. This means the risk of errors, incorrect actions, and non-compliance with current requirements.
Outcomes
Grounded, accurate responses
We offer retrieval augmented generation development services, generating answers based on specific documents, knowledge bases, and internal sources. The model receives relevant context before generating the answer, which significantly reduces the risk of hallucinations.
Transparent source citations
Each statement can be supported by a specific document or a fragment of it. This means the user can see where the information comes from and verify it. For regulated industries, this is critical: the system is no longer a “black box”; it is a controlled decision support tool.
Enterprise-grade security
Data is processed within your infrastructure with a clear access policy. Internal documents are not used to train public models and do not exceed permitted limits. Role-based access, action auditing, and integration controls enable you to implement AI without violating compliance requirements.
Real-time knowledge access
Unlike fixed-date learning models, Lumitech creates RAG systems that work with your current data. As soon as a document is updated or new information is added, it becomes available for search and use in responses. This ensures that users are getting access to the current state of the business.
Our App Maintenance and RAG Development Services
We deliver comprehensive application maintenance, including performance monitoring, security updates, infrastructure optimization, and continuous improvements.
Discovery & Data Audit
Connectors & Ingestion Pipelines
Chunking Strategy & Metadata
Embeddings & Index Design
RAG Development Services: Vector Database Setup
Retrieval Optimization
Prompting & Response Formatting
Evaluation Framework
Guardrails & Safety Retrieval Augmented Generation Development Services
Deployment & Monitoring
Ongoing Optimization
RAG Use Cases Across the Enterprise
Our RAG services and systems work with corporate knowledge. They integrate into real business processes and help teams find accurate information faster, make decisions, and reduce operational costs.
CUSTOMER STORIES
Latest Tech Expertise in RAG Development
Our Process: RAG Architecture Implementation Services
01. Discovery & Data Audit
We analyze your data ecosystem: document sources, internal systems, APIs, and access structure. We assess data quality, determine security requirements, and define clear success metrics: answer accuracy, relevance, and speed. This RAG service lays the foundation of the future architecture.
02. Prototype (1-2 weeks)
We create a quick Proof of Concept using a limited dataset. This allows us to evaluate retrieval quality, assess answer relevance, and demonstrate the system's real value even before full development. The prototype gives a clear picture of ROI at an early stage.
03. Custom RAG Development Services: MVP (4-8 weeks)
At the MVP stage, we build a full-fledged end-to-end system: ingestion pipelines, vector database, retrieval logic, UI, and integration with internal systems. Everything is developed with production requirements in mind: scalability, security, and access control.
04. Evaluation & Tuning
The system undergoes thorough end-to-end RAG testing on “golden sets” – a control set of questions and expected answers. We measure accuracy, faithfulness, and recall while gradually reducing the risk of hallucinations. If necessary, we adjust retrieval parameters, chunking, or embeddings.
05. Production Deployment
We deploy the system in your secure environment, whether on-premises, in the cloud, or in a hybrid environment. We configure performance monitoring, query logging, cost control, and a role-based access model. The system goes into stable production mode.
06. RAG Development Services & Solutions: Support & Iteration
RAG is a live system that requires constant optimization. We analyze user behavior, query frequency, weak points in answers, and gradually improve the knowledge base and retrieval algorithms. This ensures stable quality and long-term value for the business.
Retrieval Augmented Generation Development Services: Data, Security & Access Control
Enterprise AI must operate within strict security boundaries, ensuring full control over data access, processing, storage, and regulatory compliance.
RAG Architecture Implementation Services: RBAC / ABAC
We implement role-based or attribute-based access control that mirrors permissions from your source systems. Users can only retrieve and see data they are explicitly authorized to access.
Tenant Separation
For multi-tenant environments, logical or physical data isolation is provided. This allows you to safely serve multiple business units or customers on a single platform without risking information leakage.
PII & Sensitive Data Filtering
Sensitive information is automatically detected and redacted before processing. We apply policy-based filtering to prevent exposure of confidential or regulated data.
Audit Logs & Traceability
We provide full logging of requests, retrieval fragments, and generated responses. This enables internal and external audits, tracks system usage, and ensures transparency in decision-making.
Data Retention Policies
Automatic data lifecycle management is configured in accordance with internal policies and regulatory requirements. This includes control of storage, archiving, and deletion of information.
VPC / On-Prem Deployment
If necessary, the system can be deployed in full within a private cloud environment or on-premises infrastructure. This provides maximum control over data and compliance with internal security policies.
Built with the right tools
Tech Stack to Support Custom RAG Development Services
Tech Stack & Expertise
We work with a proven, production-ready stack and select technologies according to security, scalability, and budget requirements.
- LLMs: OpenAI, Azure OpenAI, Anthropic, Llama
- Frameworks: LangChain, LlamaIndex
- Vector Databases: Pinecone, Weaviate, Milvus, pgvector
- Search & Indexing: Elasticsearch, OpenSearch
- Cloud Infrastructure: AWS, Azure, GCP
- Monitoring & Observability: Langfuse, Prometheus, Sentry
Ready to deploy RAG without security risks or hallucinations?
Most of our clients are based in the US because of the tight business connections between the US and Eastern Europe. Additionally, the Middle East, especially Saudi Arabia and the UAE, is becoming another key region for us.
Additional RAG Development Services & Solutions
AI & ML Development
Delivering custom AI and ML solutions tailored to your data and workflows. Helping automate manual processes, accelerate decisions, and deploy models that scale reliably in production.
Data Science
Our data science services turn raw data into actionable insights, enabling predictive analytics and optimization across operations from customer behavior to supply chains.
AI Prototype
Lumitech rapidly transforms AI ideas into working prototypes within weeks, de-risking initiatives by validating feasibility, business value, and integration potential before full development.
Our partners
Quality of Our Custom Software Company Is Proven By Our Partners
Our partners include companies from the Inc. 5000 and Europe's 1000 Fastest-Growing Companies
Our partners include companies from the Inc. 5000 and Europe's 1000 Fastest-Growing Companies
Good To Know
How much does RAG development cost?
How long does it take to build an enterprise RAG chatbot?
How do you implement retrieval-augmented generation (RAG)?
How to choose a vector database for RAG?
What is the best RAG architecture for enterprise “chat over documents”?
How do you keep the RAG knowledge base up to date?
Ready to bring your idea into reality?
- 1. We'll sign an NDA if required, carefully analyze your request and prepare a preliminary estimate.
- 2. We'll meet virtually or in Dubai to discuss your needs, answer questions, and align on next steps.
- Careers → careers@lumitech.coPartnerships → partners@lumitech.co
Prefer a direct line to our CEO?
Prefer a direct line to our CEO?