artificial intelligence

AI & ML Development Company

AI and ML development by EltexSoft. LLM integration, RAG pipelines, AI agents, computer vision. Production AI for RiseMD, Snapwire. $50-99/hr.

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EltexSoft is an AI agent development company based in Lisbon with 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

The Work

AI That Ships to Production. Not AI That Demos Well.

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 Lisbon, Portugal. 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.

Production AI We Have Built

RiseMD: AI-Powered Healthcare Marketing

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: ML Image Tagging at Scale

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: AI Recommendations and Forecasting

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.

What We Build in AI/ML

LLM Integration

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.

RAG Pipelines

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.

AI Agents

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.

Computer Vision

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.

Predictive Analytics

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.

The AI Stack We Use

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.

What It Costs

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.

Who We Are

EltexSoft is a boutique AI agent development company. 35-50 senior engineers. Headquartered in Lisbon, Portugal. 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.

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FAQ

Common questions

What does an AI agent development company build?
LLM integrations, RAG pipelines, autonomous AI agents, computer vision systems, predictive analytics, recommendation engines, and AI-powered search. We build AI as features inside larger products, not standalone AI demos.
Which AI models do you work with?
OpenAI (GPT-4o, o1), Anthropic (Claude), Google (Gemini, MedLM via Vertex AI), Meta (Llama), Mistral, and other open-source models. We deploy via Azure OpenAI (HIPAA-eligible), AWS Bedrock, Google Vertex AI, or self-hosted for sensitive workloads. We recommend the model that fits the task and budget, not the one with the most hype.
What is RAG and when do I need it?
Retrieval-Augmented Generation feeds your own data to an LLM at query time so the model answers from your documents, not from its training data. You need RAG when you want an AI that knows your products, your policies, your codebase, or your knowledge base. We build RAG with vector databases (Pinecone, Weaviate, pgvector), proper chunking strategies, and retrieval evaluation.
Have you built AI for production?
Yes. RiseMD uses AI-powered search positioning to optimize healthcare practice visibility in ChatGPT and Google AI Overviews. Snapwire used AWS ML services for automated image tagging and quality scoring across millions of photos. Woodies Clothing uses AI for product recommendations and demand forecasting. These are production systems, not proofs of concept.
How much does AI development cost?
An LLM integration (chatbot, document Q&A, content generation) costs $20K-$60K. A RAG pipeline with vector database costs $30K-$80K. An AI agent with tool calling and orchestration costs $40K-$100K. A computer vision system costs $40K-$120K. A recommendation engine costs $30K-$80K. EltexSoft charges $50-99/hr.
Can you build AI for healthcare?
Yes. We deploy on HIPAA-eligible infrastructure (Azure OpenAI, AWS Bedrock with Comprehend Medical, Google Vertex AI). PHI never touches public LLM APIs. BAA chain maintained from cloud to application. See our medical software development page for the full HIPAA compliance framework.
What about AI hallucination and accuracy?
RAG reduces hallucination by grounding responses in retrieved documents. We add citation tracking (the AI shows which document it used), confidence scoring, and human-in-the-loop validation for high-stakes decisions. No production AI system should operate without guardrails.
Do you build custom ML models or use APIs?
Both. For most business applications, fine-tuned or prompted commercial models (OpenAI, Anthropic) are more cost-effective than training from scratch. For specialized tasks (domain-specific classification, anomaly detection on proprietary data), we train custom models with scikit-learn, PyTorch, or Hugging Face Transformers.
How long does an AI project take?
An LLM integration MVP takes 1-3 months. A RAG pipeline takes 2-4 months. An AI agent system takes 3-6 months. A computer vision system takes 3-6 months. A recommendation engine takes 2-4 months. The timeline depends on data availability and quality more than on engineering complexity.
Who owns the AI models and data?
You do. Custom models, training data, embeddings, vector databases, and all code belong to you. When using commercial APIs (OpenAI, Anthropic), you own the application code and the data pipeline; the model itself remains the provider's. We structure the architecture so you can switch providers without rebuilding.

Tell us what you're building.

One business day reply. From an engineer, not a sales rep.

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