AI Development Services

We design and deploy artificial intelligence systems: from prototype to production-ready solutions. Our team combines expertise in machine learning, data engineering and MLOps to make AI work not in the lab, but in real business.
8+Years of work900+Completed projects100+In house employees19+Partners
Services we offer
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GPU-Accelerated LLM Deployment on Kubernetes
Complex
~5 days
Deploying LLMs on AWS: SageMaker, Bedrock, EC2
Medium
~3-5 days
Deploying LLMs on Google Cloud Vertex AI
Medium
~3-5 days
Deploying LLMs on Azure: OpenAI vs Machine Learning Endpoints
Medium
~3-5 days
How to Deploy Large Language Models on Yandex Cloud: Step-by-Step Guide
Medium
~3-5 days
Private LLM Hosting on Premises: Economical and Safe
Medium
~3-5 days
How to Fit a 7B Model in 8 GB RAM Without Quality Loss?
Complex
from 1 week to 3 months
GPU Autoscaling for AI Workloads: HPA, KEDA, Pre-Warming
Complex
~3-5 days
Configuring Load Balancing Between GPU Instances
Medium
from 1 day to 3 days
Monitor GPU Utilization and VRAM with DCGM Exporter
Medium
from 1 day to 3 days
LLM Acceleration: KV-cache and Semantic Cache Tuning
Medium
from 1 day to 3 days
Dynamic Batching for LLMs: GPU Acceleration
Medium
from 1 day to 3 days
LLM Rate Limiting: Avoid Costly Quota Mistakes and Cut GPU Costs by 40%
Medium
from 1 day to 3 days
RAG System Development: Retrieval-Augmented Generation for Business
Medium
from 1 week to 3 months
RAG Pipeline Architecture Design and Implementation
Medium
~3-5 days
Building RAG with Pinecone: pipelines, hybrid search, and case studies
Medium
from 1 week to 3 months
RAG Development with Weaviate Vector Database
Medium
from 1 week to 3 months
RAG with ChromaDB: From Prototype to Production
Simple
from 1 week to 3 months
RAG Development with Milvus Vector Database
Medium
from 1 week to 3 months
RAG Development with Qdrant Vector Database
Medium
from 1 week to 3 months
RAG with pgvector: Vector Search in PostgreSQL
Simple
from 1 week to 3 months
Hybrid Search with Elasticsearch: Combining BM25 and Vector kNN for RAG
Medium
from 1 week to 3 months
RAG with OpenSearch: Vector and Hybrid Search Implementation
Medium
from 1 week to 3 months
RAG with FAISS: Local Vector Search Without Cloud
Medium
from 1 week to 3 months
Hybrid Search Implementation (Vector + Full-Text) for RAG
Medium
from 1 week to 3 months
Implementing Multi-Query RAG to Improve Retrieval Quality
Medium
from 1 day to 3 days
HyDE for RAG: Turnkey Hypothetical Document Embeddings
Medium
from 1 day to 3 days
Enhancing RAG Accuracy with Automated Metadata Filtering
Medium
from 1 day to 3 days
Contextual Compression for RAG: Implementation and Optimization
Medium
from 1 day to 3 days
Achieve 19% Better Context Recall in RAG with Parent Document Retrieval
Medium
from 1 day to 3 days

AI Development Areas

AI Solution Development Stages

Latest works

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