AI & High-Performance Software
StepTo is a 15-20 engineer team in Belgrade, Serbia, building LLM integrations, RAG systems, and workflow automation at 36-51% lower cost than Western Europe or the US.
Reviewed by Igor Gazivoda, Founder & CEO of StepTo · Published · Updated
What Do Our AI Engineers Build?
At StepTo, our AI engineers build practical, production-ready systems, LLM integrations, RAG pipelines, and automation agents, for companies that need measurable ROI, not a proof of concept that never ships.
StepTo has built software since 2014 with the same 15-20 engineer team and senior-level engineering standards that now shape our AI practice.
Whether you want to automate customer support, extract intelligence from thousands of documents, or build a new AI-first product, we provide the technical expertise to make it happen. We specialize in RAG (Retrieval-Augmented Generation) architectures, vector database implementation, and seamless API integrations. For a RAG-powered support automation project, we cut first-response time 70% and auto-resolved 38% of tier-1 tickets with no human touch, see our case study.
Based in Belgrade, Serbia, our AI developers combine deep technical knowledge with competitive rates. Where an LLM feature or automation pipeline has to be configured and iterated inside your own production environment, we can also deploy an engineer to your site, see forward deployed engineers.
StepTo AI expertise costs 36-51% less than Western Europe or US hiring. Our teams work in the CET timezone with 3-4 hours of daily overlap with the US East Coast, enabling real-time collaboration. To hire individual AI developers directly, see our AI developer hiring guide.
What AI Development Services Do We Offer?
StepTo's AI team delivers six core services: LLM & ChatGPT integration, RAG & vector database systems, custom AI agents, intelligent process automation, custom ML models, and AI analytics & insights.
LLM & ChatGPT Integration
Seamlessly integrate state-of-the-art language models, including the current OpenAI GPT and Anthropic Claude models and open-weight models such as Meta's Llama, into your existing applications or new products.
RAG & Vector Databases
Build Retrieval-Augmented Generation systems that allow AI to answer questions based on your private company data securely and accurately.
Custom AI Agents
Develop autonomous AI agents that can perform complex tasks, manage workflows, and interact with external tools and APIs.
Intelligent Process Automation
Automate repetitive business processes using AI to handle unstructured data, documents, and decision-making logic.
Custom ML Models
Train and deploy custom machine learning models tailored to your specific industry needs, from computer vision to predictive analytics.
AI Analytics & Insights
Extract actionable intelligence from large datasets using AI-driven analysis, sentiment tracking, and trend forecasting.
Which Tools Make Up the AI Stack StepTo Builds With?
Our stack spans LLM providers (OpenAI, Anthropic Claude, open-weight models like Llama and Mistral), orchestration frameworks (LangChain, LlamaIndex), vector databases (Pinecone, Milvus), and a Python/PyTorch/FastAPI foundation deployed on AWS Bedrock and Google Vertex AI.
How Do We Apply AI?
We apply AI across four recurring use cases: customer support automation, document intelligence (contract and invoice extraction), content & marketing AI, and internal knowledge search.
Customer Support Automation
AI-powered support systems that resolve common queries, triage tickets, and provide 24/7 assistance with human-like quality.
Document Intelligence
Automated extraction of data from contracts, invoices, and legal documents using OCR and LLMs for semantic understanding.
Content & Marketing AI
Systems for automated content generation, personalized marketing copy, and large-scale SEO optimization.
Internal Knowledge Search
Semantic search systems that allow employees to query internal documentation and wikis using natural language.
What Do Businesses Ask Before Automating With AI?
How can AI benefit my business?
AI can significantly reduce operational costs by automating repetitive tasks, improving decision-making through data insights, and enhancing customer experience with 24/7 intelligent support. The most impactful starting points are typically document processing (AI extracts and classifies data from invoices, contracts, or forms in seconds), customer support automation (on StepTo's RAG-based support project, the assistant auto-resolved 38% of tier-1 tickets with no human touch and cut first-response time by 70%), and internal knowledge search (letting employees query company documentation in natural language). Our approach starts with a discovery session to map your existing workflows and identify two or three high-ROI opportunities where AI delivers measurable value within 30–60 days. We evaluate each use case against effort, cost, and expected return before recommending a path forward. The goal is always practical AI, solutions that solve real business problems, not technology for its own sake.
Is our data secure when using LLMs?
Yes, by design. StepTo prioritizes data privacy by implementing secure architectures from the start. For cloud-based LLM providers, we use enterprise-grade API versions, such as Azure OpenAI Service or AWS Bedrock, where data is explicitly excluded from model training by contractual agreement. All data in transit is encrypted (TLS 1.2+), and we implement strict access controls and audit logging. For organizations handling highly sensitive data such as healthcare records, financial information, or proprietary intellectual property, we architect solutions using open-source models (Llama, Mistral) deployed on your own private infrastructure or VPC. This ensures 100% data sovereignty, your data never leaves your environment. We also implement content filtering, rate limiting, and prompt injection safeguards. All engagements include a security review of the proposed architecture before any implementation begins.
What is RAG (Retrieval-Augmented Generation)?
RAG is a technique that connects a Large Language Model (LLM) to your own live data rather than relying solely on the model's static training knowledge. When a user asks a question, the system first searches your document store or database for the most relevant passages, then passes those passages to the LLM as context to generate a grounded, accurate answer. This dramatically reduces hallucinations, the tendency of LLMs to confidently state incorrect information, because responses are anchored to your verified source material. RAG is the foundation of most enterprise AI applications: internal knowledge bases, customer support bots, contract review tools, and technical documentation assistants. It allows you to deploy powerful LLM capabilities without exposing proprietary data for model training. We implement RAG using vector databases such as Pinecone, Milvus, or pgvector, combined with orchestration frameworks like LangChain or LlamaIndex.
How long does it take to implement an AI solution?
A Proof of Concept (PoC) or MVP for an AI feature typically takes 4–8 weeks. This sprint-based approach lets us validate the technology choice, demonstrate real value, and gather early feedback before committing to a larger build. The PoC phase usually covers data ingestion, model selection, a working prototype, and a baseline accuracy evaluation. Full-scale enterprise integrations, including security hardening, system integrations, monitoring, and production deployment, typically take 3–6 months depending on complexity, the number of data sources, and your existing infrastructure. We structure every engagement around clear milestones and a working demo at the end of each sprint, so you have visibility into progress throughout. For organizations that need to move faster, we offer dedicated AI engineering teams that can run parallel workstreams to compress timelines without sacrificing quality.
How much does an AI or automation project cost?
Costs depend on scope and engagement model, but there are a few common starting points. A focused Proof of Concept (4–8 weeks, as described above) is the most common way to start and typically needs one or two AI engineers. For ongoing work, staff augmentation, adding a dedicated AI engineer to your existing team, starts from $4,500/month, while a small dedicated AI team of three engineers starts from $13,500/month. StepTo's individual rates range from $25-35/hour for junior engineers to $65-85/hour for a lead AI engineer, roughly 36-51% lower than hiring equivalent seniority in Western Europe or the US. Full-scale enterprise integrations typically run 3-6 months once security hardening, system integrations, and production deployment are included, and are scoped in detail after the discovery session once we understand your data sources and integration complexity.
Do I need a massive dataset to start with AI?
Not necessarily. Modern LLMs, the current OpenAI GPT and Anthropic Claude models and open-weight models such as Meta's Llama 3.1 (8B, 70B and 405B parameters), perform well on a wide range of tasks out of the box. Most business applications today leverage these pre-trained models through prompting and RAG rather than training custom models from scratch. For document processing, customer support, and knowledge retrieval use cases, you typically need a well-organized corpus of your own documents rather than thousands of labeled training examples. Fine-tuning, adapting a base model to your specific domain or communication style, can be effective with a small dataset: OpenAI's fine-tuning guide reports improvements from as few as 50-100 well-crafted examples and recommends starting with about 50. We start every project with an honest assessment of what data you have, what quality and volume is needed for your goals, and whether pre-trained models with RAG can meet your requirements without the cost and time of custom model training.
What engagement models do you offer for AI development?
We offer three primary engagement models for AI development. Staff augmentation places one or more dedicated AI engineers directly within your team, ideal when you have in-house product leadership but need deep AI and ML technical expertise. Project-based development delivers a defined AI solution end-to-end, from discovery through production deployment, with a fixed scope and agreed timeline, best for well-defined problems like a document extraction pipeline or a customer-facing chatbot. Strategic AI consulting is a shorter-term advisory engagement where our senior AI architects work with your leadership team to define your AI roadmap, evaluate vendors, and prioritize initiatives by business impact. All engagements begin with a two-week paid trial period so you can validate technical fit before committing longer term. Most clients start with a focused PoC and expand into a dedicated team model as the scope grows.
Ready to Harness AI?
Let's discuss how AI can transform your business. Our experts are ready to help you build intelligent, automated solutions.
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