Knowledge Assistant
We build assistants that answer questions based on your policies and cite their sources, as in an HR platform with a case database.
RAG Development Services
Turn your documents and internal systems into a RAG AI assistant that answers with sources and respects access rights. Complete the development and get a production-ready system for your team in under 90 days.
12
Yrs of Dev Experience
150+
Completed Projects
80
Certified Developers
RAG Application Development Services by Zentixsoft
Our RAG development services grew out of working systems. RAG searches policies and cases in an HR platform, works with documentation in a tendering platform, and matches candidates based on the content of their profiles in a recruiting platform. That means we know where real data causes problems before those problems surface in your project. We start with a brief assessment: we review your data and use case, agree on how to measure success using your real questions, and present an architecture with a budget estimate. If you already have a prototype, we begin with an audit to find out why it cannot handle real workloads.
Metrics First
Before we start building, we agree on what a good answer looks like: it should be accurate, point to a source, and know when to say it can’t answer.
Your Data Sources
We make the information in your PDFs, wikis, tickets, CRM, ERP, databases, and cloud storage available to the RAG assistant.
Prototype Rescue
We assess the pipeline, data, and accuracy, then continue development and bring the system into production without rebuilding it from scratch.
RAG Development Services:
Your Challenges, Our Solutions
Inaccurate answers, slow search, security concerns, or low adoption - we know what causes these problems. We identify the cause and tailor RAG to your data, workloads, and workflows.
Challenge
The support bot confidently promises a customer a refund within 60 days, although your policy says 30, and provides no source.
Risk
After the first such mistake, the system is switched off, and no one will approve another AI budget.
Solution
We fix the search process that feeds the model outdated versions of documents and require a source for every answer. Without a source, the system refuses to answer.
Challenge
A pilot with a few hundred documents impressed management, but with tens of thousands, answers became slow, inaccurate, and expensive.
Risk
The team starts rebuilding everything from scratch, and the deadline given to management has already passed.
Solution
We conduct an audit and add what the demo did not need: hybrid search, reranking, metadata filters, and monitoring, without rebuilding from scratch.
Challenge
During approval, someone asks whether an intern could use the bot to see documents about executive salaries, and no one can answer with confidence.
Risk
The project remains stuck in approval for months or launches with public documents only.
Solution
We enforce access rights at the search level, so the system cannot retrieve a document the user is not allowed to open, and we log every query.
Challenge
Management asks how accurate the AI assistant is, but all they have are impressions from individual users and a couple of complaints.
Risk
The launch decision is made on gut feeling, and customers are the first to tell you about errors.
Solution
Before development begins, we assemble a test set of your real questions and measure accuracy and the share of answers with sources before launch and after every change.
Challenge
The company has paid for an enterprise RAG assistant, but it gives generic answers and cannot access your CRM, ERP, or internal databases.
Risk
The licenses are paid for, but employees go back to searching manually and asking colleagues.
Solution
We connect your systems - documents, CRM, ERP, and databases - and tailor search to your data structure and the questions your team asks.
Challenge
The team used the assistant for the first two weeks, then stopped because getting an answer meant opening a separate window and pasting in context.
Risk
The money has been spent, and the view that “AI doesn’t work here” takes hold across the enterprise.
Solution
We embed search and answers in the platforms where the team already works, as we have done in our clients’ HR, tendering, recruiting, and ERP systems.
0106
Our RAG
Development Services
We can come in when you’re still weighing up the idea, when a prototype needs work, or after the system has launched. We focus on what your RAG project needs at that point and build around the data and tools your team already uses.
01
RAG Consulting
Before development begins, we check whether RAG will deliver results for you:
Analyze your data sources, volumes, and quality.
Define the use case and security requirements.
Agree on success metrics based on your questions.
Prepare an architecture and budget estimate.
02
Custom RAG Development
03
RAG Platform Integration
04
Agentic RAG
05
RAG Quality Audit
06
LLMOps and Support
Have a similar project?
Show us your data and use case, and we’ll estimate the accuracy you can achieve and the budget required.
RAG Application Development Services We Deliver
Your data can help your team find answers, review documents, and identify relevant options. We build RAG applications for these tasks and integrate them into daily workflows.
Enterprise Search
We build a single semantic search across all enterprise systems, as in OvexSync ERP, where it covers six business areas.
Document Copilot
We build assistants for working with large documents, as in a tendering platform where RAG helps users navigate tender documentation.
Matching Copilot
We integrate semantic matching based on meaning rather than keywords, as in a recruiting platform where AI searches for and evaluates candidates based on their profiles.
RAG Development Services Cost
and Engagement Models
RAG system costs depend on your data, accuracy needs, integrations, and deployment method. We review your use case and provide an estimate, proposed architecture, and suitable collaboration model.
Fixed Price
You know what the project will cost and when the work will be done before you seek approval. Fixed price makes sense for consulting, a pilot, or work covered by a detailed specification.
Time & Material
If the requirements are still taking shape, you can adjust the work as you go and pay for what the team has completed.
Dedicated Team
Get a team that knows your product and data in depth, without spending months searching and hiring. Best suited to long-term platform development services.
Security, Access Control
and Compliance
Company knowledge contains the very data a RAG system needs to access and whose exposure would be most costly. That’s why we build security into the pipeline from the start, rather than adding it just before launch.
Permission-Aware Search
We filter documents by the user’s access permissions during search, so restricted content never enters the model’s context or its answer.
Private Deployment
We deploy the RAG system in your AWS, Azure, or Google Cloud environment so your documents and queries stay within your infrastructure.
Encryption and Secrets
We encrypt data in transit and at rest, and keep keys and passwords for your systems in a secure secrets vault.
Full Audit Trail
We log every query, the documents retrieved, and the response, so you can answer any question from your security team with facts.
Compliance by Design
We build GDPR and CCPA requirements into the architecture from day one, rather than checking compliance a week before launch.
Human in the Loop
For sensitive processes, we add human review, so a response reaches the customer only after a specialist approves it.
What Is RAG and How
It Compares to Fine-Tuning
RAG connects a model to current company knowledge so answers can cite sources. Compare its effort and cost with fine-tuning and prompt engineering below before discussing your project.
Criterion
RAG
Fine-tuning
Prompt engineering
Best for
Answers based on up-to-date company knowledge
A consistent style, format, or narrow task
Simple, recurring queries
Updating data
Immediately - just update the document
Requires retraining
Limited to what fits in the prompt
Source citations
Yes, in every answer
No
No
What determines cost
Data preparation and search quality
Training data and training runs
Iterations on instructions
Advantages
Why Choose ZentixSoft as Your RAG Development Company
We’re a RAG development company that builds business platforms first, then teaches them to answer questions. That’s why we know how RAG performs with real data and continue working with clients after launch.
00
01
02
03
04
05
RAG in Production
RAG already runs in the HR, tendering, recruiting, and ERP platforms we’ve built, beyond demos.
Built Into Your Platforms
We embed answers in the systems your team already uses, so the assistant doesn’t become another tab.
Accuracy You Can Measure
We test accuracy against your questions before launch, so you can judge quality by the numbers instead of complaints.
Stalled Prototypes Welcome
We take prototypes stuck between demo and production and get them working without rebuilding them.
You Own Everything
The code, documentation, and knowledge pass entirely to you, and you can change the model without our involvement.
Partners for Years
55% of clients work with us again or over the long term, and the average engagement lasts 30 months.
How We Build Your RAG System
Each stage ends with a result you can see and verify: from an architecture and budget estimate to a system that answers your questions with the required accuracy.
01
Discovery
We review your data and use case, agree on success metrics, and provide an architecture with a budget estimate.
02
Data Preparation
We connect RAG data sources, clean the content, and split it into chunks with metadata and access levels.
03
Retrieval and Model
We build hybrid search with reranking and connect a model that can be changed without rebuilding the system.
04
Quality Testing
We test the RAG system against a set of your questions, checking accuracy, sources, and refusals where needed.
05
Integration and Launch
We integrate RAG into your platforms and deploy it in your cloud with CI/CD and automated tests.
06
Support and Growth
We continue development after launch, monitoring quality, cost, and response times and refining search based on real user questions.
Our team
Your RAG Development Team
Data, AI, backend, QA, and DevOps engineers with production RAG experience build your system. We assemble the team in as little as five days and add five or more specialists within one to two weeks as needed.
CEOAgness
CTOAndrew
HRDYevhenii
Project ManagerOlha
Full Stack DeveloperDmytro
Full Stack DeveloperMarko
Team LeadYehor
Content LeadMaksim
Project ManagerYuliia
BDMPavlo
Project ManagerOlga
Full Stack DeveloperYurii
Full Stack DeveloperVitalii
Team Lead | DevOpsOleg
Backend DeveloperMaks
Team Lead | DevOpsOleg
QA EngineerElina
Full Stack DeveloperOleksandr
RecruiterOksana
CMOSolomiya
CSOOlga
Project ManagerYeva
Full Stack DeveloperAnastasiia
Tender ManagerYurii
Head of DeliveryKristina
UI/UX DesignerVladyslav
Business AnalystOlena
RAG Tech Stack
Without Vendor Lock-In
We choose RAG tools based on your data, security requirements, and budget. We have several proven options in each category, so the system is easy to adapt as models or workloads change.
Models
OpenAI
Anthropic Claude
Google Gemini
Hugging Face
RAG and Agent Frameworks
LangChain
LlamaIndex
LangGraph
Semantic Kernel
CrewAI
AutoGen
Vector Databases
Pinecone
Weaviate
Qdrant
Search Engines
Elasticsearch
Meilisearch
Model Routing
LiteLLM
Backend and APIs
Python
Node.js
Nest.js
Fastify
REST API
GraphQL
Data Storage
PostgreSQL
MongoDB
Redis
Cloud
AWS
Microsoft Azure
Google Cloud
DevOps and Monitoring
Docker
Kubernetes
Terraform
CI/CD
Prometheus
Grafana
Companies That Trust Us
Why Clients Stay
With Us for Years
The people who have launched products with us can tell you the most about our work. Here’s what they say.
0106
They adapted quickly to changing priorities.
The team quickly understood our goals and translated them into practical solutions. Their ability to adapt and move fast made the collaboration smooth and highly productive.
Joseph F.
Ozeaon | Portugal
Joseph F.
Ozeaon | Portugal
Worked in sprints
Zentix delivered their development work on time, which was an excellent start for the client. The team worked in sprints, updated the client weekly, and delivered tasks on schedule.
Andrew R.
RaDevs | Estonia
Andrew R.
RaDevs | Estonia
Responsiveness and dedication
Responsiveness and dedication to delivering high-quality services were outstanding.
Maria А.
ARGUNOVA | Ukraine
Maria А.
ARGUNOVA | Ukraine
The team delivered exactly what we needed
The team delivered exactly what we needed — high-quality solutions and smooth collaboration from start to finish.
Alex L.
Wavory | Cyprus
Alex L.
Wavory | Cyprus
Their e-commerce expertise made a real difference
Zentix helped us build and launch our e-commerce platform with great attention to detail. The team was responsive, professional, and easy to work with throughout the entire process.
Amir B.
Servicom | Sweden
Amir B.
Servicom | Sweden
They delivered great results and provided useful support throughout the project.
Zentix's work met the client's expectations. The team provided timely deliverables and professional responses throughout the engagement. Their daily online meeting, constant updates on the project's progress, and excellent results were remarkable. Zentix approached tasks promptly and creatively.
Andrey H.
Bestclevers | UK
Andrey H.
Bestclevers | UK
Ready to turn your company’s knowledge into AI that works?
Show us your data, and we’ll recommend where to start.
FAQ
These services involve building systems that connect a language model to your enterprise’s data. The work covers source analysis, document preparation, search, model integration, accuracy testing, and production support. The result is AI that answers based on your content and cites its sources.
These services involve building RAG-based products, such as employee assistants, enterprise search, document assistants, and candidate matching tools. The retrieval layer finds the relevant data, while the application determines who is asking, which interface they use, and what happens next.
The cost depends on the number and condition of your data sources, accuracy requirements, integrations, deployment method, and workload. We first review your data and use case, then provide a budget estimate and architecture before development services begin.
The timeline depends on the volume and condition of your data, the number of integrations, and security requirements. We provide a precise timeline after a consultation, once we’ve reviewed your sources, and break it down by stage so you know what you’ll receive and when.
RAG development takes more than one role. It requires a data engineer to connect sources and prepare data, an AI engineer to build search and integrate the model, a backend developer to build the application, QA to test accuracy, and DevOps to handle deployment and monitoring.
We limit the RAG model to retrieved passages from your documents, require a source for every answer, and test the system against a set of your questions before launch. When there is no source, the system refuses to answer instead of making one up.
Yes. We assess the pipeline, data, and accuracy using a test set of questions, find out why the system loses quality or speed at real-world scale, and bring it into production without rebuilding it from scratch.
Yes. Models are becoming more powerful, but they do not know the contents of your internal documents, and retraining them after every change is costly and time-consuming. RAG gives AI up-to-date knowledge while respecting access permissions and provides a foundation for AI agents.
ChatGPT is a product based on large language models. It uses search when looking things up on the internet or reading an uploaded file, but it is not a RAG system connected to your internal sources with your access rules.
You do. The code, documentation, and intellectual property transfer entirely to your enterprise. The model connects through a routing layer, so you can change it without our involvement. We also transfer knowledge to your team so they can maintain the system themselves.
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