Custom AI solutions
for your architecture
When ready no-code services are insufficient. We develop high-load AI modules, our own SaaS platforms, train open-source models (Fine-Tuning) and build a reliable backend infrastructure for your business.
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Development on Python, FastAPI, PHP 8.3+, Go and modern cloud stacks
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Fine-tuning and local hosting of models (Llama 3, Mistral, Whisper)
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Designing a scalable microservices architecture from scratch
Why is this critical right now?
Off-the-shelf services do not meet your needs
Typical no-code platforms have rigid limits, expensive subscriptions, and do not allow implementing your business logic.
Expensive API charges from OpenAI
With large volumes direct queries to GPT-4o become unprofitable. Optimization, caching and own models are needed.
Lack of security for cloud solutions
Enterprise clients require data storage strictly on their own infrastructure without transferring to third-party SaaS.
What does it give to business?
Why is the implementation of this technology taking off in the first months.
Own Digital Asset
You do not rent foreign software, but you own custom code, which increases the capitalization of your business.
Model Training (Fine-Tuning)
We train open neural networks on your data to gain expert knowledge in a specific narrow niche.
Up to a 10x reduction in cost per request
The use of compact quantized models and intelligent semantic caching reduces infrastructure costs.
On-Premise Deployment
We install models on your own GPU servers for 100% compliance with GDPR, HIPAA and NDA
High speed and microservices
We build lightweight asynchronous microservices, ready to handle millions of requests per day.
Support and SLA
Full cycle: from architecture development to CI/CD, error monitoring, and round-the-clock support.
How We Implement It
No magic. Just a clear engineering process
Technical discovery phase: requirement fixation, load analysis, selection of stacks and AI technological stack.
System architecture design: databases, API schemas, message queues (Redis / RabbitMQ).
Development of MVP (Sprints): iterative code writing with weekly demos for the client.
Training and integration of AI: model tuning, prompt development and vector index connection
Load testing: stress tests for response speed (Latency) and reliability under peak loads.
Release, repository transfer and SLA: deployment to production, transfer of rights and documentation
Frequently asked questions
To whom does the created code and intellectual property belong?
On 100% you are. After completion of the project all source codes, repositories, trained model weights and documentation are transferred to your company.
Can the model be run on our servers?
Yes. We select and deploy open LLM (Llama 3, Mistral, Gemma) on your private dedicated servers with NVIDIA graphics cards.
What technologies do you use in development?
Python (FastAPI, PyTorch, LangChain, LlamaIndex), PHP 8.3+, Go, PostgreSQL, Redis, Qdrant/Pinecone, Docker, Kubernetes.
How long does it take to develop custom AI software?
Working prototype (MVP) we launch in 3–4 weeks. Full-scale product — from 1.5 to 3 months depending on complexity.
Do you take projects on long-term support?
Yes, we provide continuous DevOps monitoring, model version updates and optimization of operation under increasing loads.
Ready for Implementation?
Describe your task. We will conduct a free audit and propose an AI solution architecture specifically for your business.