AI and ML development by EltexSoft. LLM integration, RAG pipelines, AI agents, computer vision. Production AI for RiseMD, Snapwire. $50-99/hr.
EltexSoft is an AI agent development company headquartered in Los Angeles, with an office in Lisbon and senior engineers in Ukraine. 11 years in business. We build LLM integrations, RAG pipelines, AI agents, computer vision systems, and predictive analytics. Production AI clients include RiseMD (AI-powered healthcare marketing), Snapwire (ML image tagging and quality scoring for Fortune 500 photography marketplace), and Woodies Clothing (AI recommendations and demand forecasting). $50-99/hr.
What we ship
OpenAI, Anthropic, and open-source model integration into production applications. API design, prompt engineering, token optimization, rate limiting, cost controls. Not demos. Production systems with monitoring and fallback.
Retrieval-augmented generation with vector databases (Pinecone, Weaviate, pgvector). Document ingestion, chunking, embedding, retrieval, and LLM synthesis. Knowledge bases that answer from your data, not the model's training data.
Autonomous task execution, tool calling, multi-step reasoning, and human-in-the-loop validation. LangChain, CrewAI, and custom orchestration. Agents that do real work, not agents that demo well.
Image classification, object detection, quality scoring, and automated tagging. Built ML-powered image tagging and quality scoring for Snapwire (photography marketplace for Dell, Starbucks, Fortune 500s).
Demand forecasting, lead scoring, churn prediction, customer segmentation, and anomaly detection. Production ML models with proper feature engineering, monitoring, and retraining pipelines.
Semantic search, recommendation engines, and AI-assisted visibility (ChatGPT, Google AI Overviews, Perplexity). Built AI-powered search positioning for RiseMD's healthcare marketing platform.
The gap between an AI demo and a production AI system is enormous. The demo works on 10 examples in a Jupyter notebook. The production system must handle 10,000 requests per hour, stay within token budget, return answers in under 2 seconds, not hallucinate when the data is ambiguous, fall back gracefully when the model is down, and cost less than the business value it creates.
We build the second kind.
EltexSoft is an AI agent development company. 35-50 senior engineers. Headquartered in Los Angeles, with offices in New York, Lisbon, and Kyiv. Engineering team in Ukraine. Founded in 2015. We build LLM integrations, RAG pipelines, AI agents, computer vision systems, and predictive analytics as features inside production applications.
For our full AI service offering including generative AI development, see our AI development services page. This page focuses on AI/ML as an industry vertical.
RiseMD’s healthcare marketing platform uses AI for search positioning: optimizing dental and medical practice visibility not just in Google, but in ChatGPT, Google AI Overviews, and Perplexity. This is the new frontier of practice marketing. Patients are asking AI assistants “best dentist near me” and the answer comes from the model’s training data and real-time search, not from a Google Ads auction.
The AI layer also powers call grading and analysis: automated scoring of patient intake calls to identify which calls convert to appointments and why.
Results: $3.2M in production from $160K marketing spend. 20X ROI.
Snapwire was a photography marketplace connecting Fortune 500 brands (Dell, Starbucks) with on-demand photographers. Millions of images processed. AWS machine learning services handled automated image tagging (identifying objects, scenes, and themes) and quality scoring (evaluating sharpness, exposure, and composition). These scores fed the matching algorithm that connected photographers with brands.
The Snapwire engagement included 10 EltexSoft engineers for 2.5 years. The platform was later acquired by StudioNow.
Woodies Clothing uses AI for product recommendations based on purchase patterns and browsing behavior, customer segmentation for targeted campaigns, and inventory demand forecasting. These are not experiments. They are production features that affect conversion rate, return rate, and stock efficiency.
Connecting commercial LLMs (OpenAI, Anthropic, Google) to your application with proper engineering. API design with retry logic and fallback providers. Prompt engineering with version control and A/B testing. Token optimization to control costs. Rate limiting to prevent budget overruns. Monitoring dashboards showing latency, cost per request, and error rates.
The difference between a developer who calls the OpenAI API and an AI engineering team: the developer builds a feature. The team builds a system that runs the feature reliably at scale, handles edge cases, monitors costs, and degrades gracefully.
Your company has documents, knowledge bases, product catalogs, support tickets, and internal wikis. An LLM does not know any of it. RAG bridges the gap: your documents are chunked, embedded into a vector database, and retrieved at query time so the LLM answers from your data.
We build RAG with Pinecone, Weaviate, or pgvector. Proper chunking strategies (not “split on 500 tokens and hope”). Retrieval evaluation to measure whether the right chunks surface. Citation tracking so users can verify the source. And hybrid search (vector + keyword) because pure semantic search misses exact-match queries.
Autonomous systems that execute multi-step tasks: query a database, call an API, process the result, decide what to do next, and take action. Tool calling, multi-step reasoning, and human-in-the-loop validation for high-stakes decisions.
We build with LangChain, CrewAI, and custom orchestration depending on the complexity. The key design decision: when should the agent act autonomously and when should it ask a human? Getting that boundary wrong is how AI agents cause real damage.
Image classification, object detection, quality scoring, OCR, and automated tagging. Production deployments on AWS (Rekognition, SageMaker) or GCP (Vision AI, Vertex AI). Custom model training when commercial APIs do not fit the domain.
Demand forecasting, lead scoring, churn prediction, customer segmentation, and anomaly detection. Feature engineering from your production data. Model training, validation, and deployment with monitoring for drift. Retraining pipelines that keep models accurate as data changes.
LLMs: OpenAI (GPT-4o, o1), Anthropic (Claude), Google (Gemini, MedLM), Meta (Llama), Mistral. Deployment: Azure OpenAI (HIPAA-eligible), AWS Bedrock, Google Vertex AI, self-hosted (vLLM, Ollama). RAG: Pinecone, Weaviate, pgvector, LangChain, LlamaIndex. Agents: LangChain, CrewAI, custom orchestration. ML: scikit-learn, PyTorch, Hugging Face Transformers, XGBoost. Vision: AWS Rekognition, Google Vision AI, custom models on SageMaker/Vertex. Data: Python (FastAPI, Pandas, NumPy), PostgreSQL, Redis, Apache Airflow. Infrastructure: Docker, Kubernetes, GitHub Actions, Terraform.
Senior AI/ML engineer (dedicated): $50-99/hr.
By project type:
LLM integration (chatbot, Q&A, content generation): $20K-$60K, 1-3 months.
RAG pipeline with vector database: $30K-$80K, 2-4 months.
AI agent with tool calling: $40K-$100K, 3-6 months.
Computer vision system: $40K-$120K, 3-6 months.
Recommendation engine: $30K-$80K, 2-4 months.
Predictive analytics (forecasting, lead scoring): $20K-$60K, 2-4 months.
EltexSoft is a boutique AI agent development company. 35-50 senior engineers. Headquartered in Los Angeles, with offices in New York, Lisbon, and Kyiv. Engineering team in Ukraine. Founded in 2015.
Production AI clients: RiseMD (AI healthcare marketing), Snapwire (ML image processing for Fortune 500s), Woodies Clothing (AI recommendations). For our full AI service offering, see AI development services. We also build with Python, Django, Laravel, React, iOS, and Android.
5.0 Clutch rating across 30+ verified reviews. 200+ five-star Upwork reviews. Top Rated Plus and Expert-Vetted agency status (top 1%). Average client engagement: 3+ years.
30-minute technical call. Bring your AI use case, your RAG architecture question, or your ML pipeline requirements. We’ll tell you what we’d build and what we wouldn’t.
Field notes
How to choose an AI development company: nine checks on success metrics, evaluation harnesses, data boundaries, RAG and agents in production, cost and pricing.
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One business day reply. From an engineer, not a sales rep.