Prototype Stability Audit
We identify exactly where the generative AI prototype fails: data, prompts, or architecture. You fix the cause instead of rebuilding everything from scratch.
Generative AI Development Services
Custom LLM solutions, RAG, and AI agents for your solution: from use case to production with measured accuracy and predictable costs.
80
Certified Engineers
55
% Long-term Clients
12+
Years of Developer Experience
ZentixSoft Generative AI Development Company
As a gen AI development company, we build AI features directly into the solutions businesses already use. That may mean adding new capabilities to a CRM, ERP, or HR platform, or developing the AI infrastructure needed to support them. Data preparation, AI model work, backend development, integration, and monitoring stay with the same team, so the result is ready to work as part of the solution rather than sitting separately as a prototype. Before anything is built, we look at where generative AI can actually make a measurable difference. The assessment covers potential AI use cases, expected quality, typical request costs, and the technical work involved. The scope and expected budget are documented before development starts, along with the factors that may change operating costs once the solution is live.
Honest Advice on GenAI
If standard automation services can solve the task more cost-effectively, we’ll tell you before you spend the budget.
Quality Measured Before Launch
Our company tests the generative AI system on your real data and evaluates quality against agreed criteria and metrics.
Partners Long After Launch
Our company's average client relationship lasts 30 months: AI model updates, quality monitoring, and new use cases.
How Our Generative AI
Development Solutions
Solve Business Challenges
We show which problems most often slow down generative AI adoption and how our development services help reduce these risks.
Challenge
You know what you want to automate, but you are not sure whether GenAI is the right tool or what return it will deliver.
Risk
The budget goes toward an impressive demo that management sees once and then forgets.
Solution
During the feasibility stage, we evaluate the generative AI use case on your data and show the expected return - or tell you if standard automation would be more cost-effective.
Challenge
The generative AI prototype worked in the demo but started making mistakes and generating false answers on real data.
Risk
The team loses trust in the generative AI system, and an incorrect answer reaches the customer.
Solution
We test on your real data, establish baseline quality metrics, and check them after every system change.
Challenge
No one can answer the question, “How much will this cost to run?”
Risk
After launch, generative AI API costs grow with the number of users, and the CFO stops the project.
Solution
Before development, we estimate the cost of typical requests and monthly expenses at the expected load, set limits and alerts, and route simple requests to cheaper models.
Challenge
Management wants AI in the solution this quarter, but your team has no generative AI experience.
Risk
Hiring takes months while competitors launch their AI features.
Solution
Senior engineers with production generative AI experience can join the project within 5 days and work alongside your team.
Challenge
It is unclear who will be able to access what through AI or whether the vendor will train on your data.
Risk
A confidential document reaches the wrong user, and security or legal teams block the project.
Solution
We use vendor APIs that do not use your data for model training and, when needed, deploy the model in your infrastructure.
0105
Our GenAI
Development Services
Experience across different industries shows that generative AI adoption is usually slowed down not by the AI model itself, but by other factors: data, architecture, security, or cost control.
01
GenAI Feasibility Assessment
We assess where generative AI can deliver real value in your processes:
test the strongest generative AI use cases on a sample of your data;
estimate accuracy, cost per request, and expected return for each;
eliminate use cases where the model adds risk without value;
prepare a map of priority generative AI use cases and the target architecture.
02
Custom LLM Solutions
04
AI Agents & Copilots
05
LLM Product Integration
06
LLMOps & Evaluation
Have an Idea for Generative AI?
Submit a request for generative AI development. We’ll assess your data for free, select the right model, and show how the solution can scale without technical debt.
From Prototype
to Production System
After the demo, a generative AI prototype still needs to be prepared for real users. Here’s what our services refine to ensure the AI solution can handle the load.
Real-Data Accuracy Testing
We test the generative AI system on your real requests, measure quality against agreed metrics, and determine when it should refuse to answer or hand the request over to a person.
Cost Under Load
We estimate generative AI costs at the expected load and set up limits and model routing to keep the budget under control.
Gradual Production Rollout
We launch the generative AI system with one team first, monitor usage, and resolve identified issues before expanding access.
Where Generative AI
Pays Off First
GenAI can reduce manual work where people read, compare, and write text every day.
Document-Heavy Workflows
The generative AI model reads contracts, tenders, invoices, and specifications and prepares structured summaries, so the team works with conclusions rather than dozens of source files.
Internal Knowledge Access
Employees get answers from up-to-date company documents with links to the sources, helping new hires get up to speed faster and experienced employees spend less time answering repetitive questions.
Candidate Screening & Matching
The AI system analyzes resumes and ranks candidates against job criteria, helping recruiters prioritize applications for review. The final decision remains with a person.
Which Generative AI
Model Is Right for You
An off-the-shelf generative AI model for general requests, RAG for your data, and fine-tuning only when needed. Everything can be deployed in your infrastructure, and we recommend the right combination after analyzing your data.
Criterion
Off-the-Shelf Model + Prompts
RAG
Fine-Tuning
Best for
General tasks, quick start
Answers from your up-to-date documents and databases
Consistent format, tone, or specialized terminology
What you need
Clear task description and examples
Your documents and access to systems
Hundreds of high-quality “request → response” examples
What it doesn’t solve
Doesn’t know your internal data
Doesn’t help if the required information isn’t in the sources
Doesn’t reliably add new facts; knowledge is updated only through retraining
Main cost driver
Cost per request
Data preparation and retrieval quality
Example preparation, training, and more expensive requests
In your infrastructure
Yes, with open-source models
Yes
Yes, with open-source models
Security, Governance, and Guardrails in Generative AI Development
An incorrect answer can reach a customer, while a document can reach the wrong user. We reduce these risks through architecture, access controls, and rules for validating responses.
Private Data Handling
We use AI provider APIs that do not use your data for model training and mask personal data before transmission.
Permission-Aware Answers
The AI model uses only content that the specific user is authorized to access, so AI does not become a workaround for accessing restricted documents.
Prompt Injection Defense
We validate requests and external content and restrict agent actions to reduce the risk of data leaks and unauthorized actions.
Full Audit Trail
We log requests, responses, and data access to trace system activity and investigate incidents.
Human Review Gates
For regulated and customer-facing processes, a person reviews the response before it is sent, keeping critical decisions under your control.
Compliance-Ready Delivery
From the first stage, we account for applicable GDPR and CCPA requirements in the architecture and data processing to simplify compliance reviews before launch.
How Much Do Generative AI Development Services Cost
Public generative AI cost estimates vary from $5,000 for a simple integration to $500,000 or more for an enterprise platform. Six main factors affect the budget.
Use Case Scope
One process or generative AI functionality across the entire platform: this determines the scope of development, integrations, and testing.
Data Readiness
Preparing clean, structured data usually requires less work than processing documents from different formats and systems.
Accuracy Requirements
The cost of an error in your process determines how much testing, validation, and human involvement are required.
Integration Depth
Deep integrations, access controls, and logging increase the scope of development and testing.
Deployment Model
Enterprise AI API, private cloud, or your own infrastructure have different implementation and generative AI maintenance costs.
Running Costs
After launch, costs depend on usage, model selection, caching, routing, and infrastructure.
Advantages
Why Choose ZentixSoft as Your Generative AI Development Services Company
Our generative AI development solutions are built around principles that address specific risks that prevent AI projects from reaching production.
00
01
02
03
04
Production, Not Pilots
Projects often end with a prototype, while our generative AI solutions work every day in HR, tender, and ERP platforms.
Costs Estimated Upfront
Before development, you get an estimate of typical request costs and monthly expenses at the expected load.
Quality You Can Check
You get a test set and baseline quality metrics that we use to compare results after every system change.
Senior Engineers Directly
At large vendors, presales teams stand between you and the engineers. With us, you work directly with senior engineers who can join your project within 5 days.
Free to Leave, 55% Stay
The prompts, tests, documentation, and code are yours. We plan for switching model providers, while 55% of clients stay with us long-term.
How We Run a Generative AI Project
Each stage of our gen AI development services has a specific deliverable and agreed validation criteria.
01
Feasibility Call
We review your use case, data, and constraints. Within a week, we provide a preliminary scope and budget range or tell you that it is not worth pursuing yet.
02
Discovery & Scope
We agree on success metrics, architecture, and the model approach, so before development starts, you know exactly what you will get and how it will be measured.
03
Prototype on Your Data
We build a working AI version on a limited scale and evaluate it against the agreed metric, so you make the production decision based on data rather than impressions from a demo.
04
Production Build
We integrate the system with your solutions, configure access controls, guardrails, and cost controls, and test it under the expected load.
05
Launch & Evolution
We roll out the system gradually, monitor quality, and maintain it as models are updated and new use cases are added.
Our team
The Team Behind Your
Generative AI Product
Our team brings together AI/ML, data, backend, and MLOps specialists. We tailor the tech stack to each project and provide services from discovery through production support.
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
CSOOlga
CMOSolomiya
Project ManagerYeva
Full Stack DeveloperAnastasiia
Tender ManagerYurii
Head of DeliveryKristina
UI/UX DesignerVladyslav
Business AnalystOlena
Technology Stack for Generative AI Solution Development
For GenAI projects, we choose tools based on the task and integration with your stack, not the vendor, so your solution does not depend on a single model or platform.
Models
OpenAI/GPT
Anthropic Claude
Google Gemini
Llama
Mistral
Enterprise AI platforms
Azure OpenAI
AWS Bedrock
Vertex AI
Frameworks
LangChain
LlamaIndex
LangGraph
Vector databases
Pinecone
Qdrant
Weaviate
Search
Elasticsearch
Meilisearch
Model routing
LiteLLM
Evaluation & monitoring
Evaluation & monitoring
Backend & data
Python
Node.js
PostgreSQL
Redis
Cloud & DevOps
AWS
Azure
Google Cloud
Docker
Kubernetes
Trusted to Build Products
Client Reviews
Behind every review is an AI solution running in production and a team that trusted us with its data and processes.
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
Responsiveness and dedication
Responsiveness and dedication to delivering high-quality services were outstanding.
Maria А.
ARGUNOVA | Ukraine
Maria А.
ARGUNOVA | Ukraine
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
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
Need a Reliable Architecture for Generative AI?
Our engineers will review your use case, data, and current system and propose a solution that can handle your business’s real-world workload.
FAQ
This is the development of generative AI solutions based on generative models. Our focus is on LLM solutions that generate text, responses, and structured data. The generative AI development service covers use case and model selection, data preparation, RAG, AI agents, integration with your systems, guardrails, and post-launch monitoring. The goal is a working solution feature, not a demo.
Service delivery timelines depend on the use case. Within a week after the feasibility call, we provide a preliminary scope of work and development budget range. A prototype with a limited scope is usually ready within a few weeks. A production system with integrations, access controls, and monitoring takes more time, depending on the number of systems and accuracy requirements.
Yes. As a generative AI development agency, we analyze prompts, data, and architecture, determine why the results are unstable, and bring the system to production: testing on real data, guardrails, cost control, and a phased rollout. We check whether the issue lies in the model, data, prompts, or architecture.
In most cases, no, at least not at the start. Prompts combined with RAG solve most business tasks. We consider fine-tuning if tests show that other adaptation methods are insufficient to achieve the required quality. It does not reliably add new facts; RAG is used for that.
Less than is usually expected. To validate a use case, a representative sample of the documents or records the system will work with is enough. Full data preparation begins after the use case has been validated, not before, so you do not spend budget preparing data for an idea that may not work.
We ground responses in your sources with citations, test the system on a set of real questions before launch, and define refusal rules. Queries without a reliable source are escalated to a human. We continue tracking accuracy as a metric after launch, especially after model or data updates.
No. Our agency uses provider APIs that do not use your data to train models and mask personal data before transmission. If the data cannot leave your environment, we deploy open models in your cloud or on your own servers.
Typically, this includes an AI engineer for models and RAG, a data engineer for sources and pipelines, a backend developer for integrations, QA for quality evaluation, and DevOps for deployment and monitoring. All of them are mid-level and senior specialists. The team composition depends on the use case, and the team can start working within 5 days.
We provide services under three models. Dedicated Team - a separate team for a long-term solution. Team Extension - our engineers join your team when you have developers but lack GenAI expertise. Project-Based - a fixed scope, timeline, and budget for feasibility, a pilot, or a production system.
Traditional machine learning is used, among other things, to predict values and classify records. Generative AI models create text, responses, and structured data, making them suitable for work that was previously done by a person reading and writing. Many development projects combine both approaches.
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