I integrate large language models into your business applications -- the right model for each task, optimized for cost, speed, and accuracy.
Every business application can benefit from language AI -- but choosing the right model, building reliable integrations, and keeping costs under control requires deep technical expertise. GPT-4o is not always the answer. Sometimes Claude is better for long documents. Sometimes a EUR 0 open-source model outperforms a EUR 15/million-token API for your specific task.
I help businesses integrate LLMs into their existing software: CRM systems, helpdesk tools, e-commerce platforms, internal dashboards, and custom applications. The integration includes prompt engineering, error handling, cost optimization, and production monitoring -- not just an API call.
I am Kirill Strelnikov, a freelance AI integration developer based in Barcelona. I have built LLM-powered features for e-commerce chatbots, content generation systems, document analysis tools, and multi-model AI platforms. I work with GPT-4, Claude, Llama, Mistral, and any model that fits your requirements.
Product descriptions, email drafts, report summaries, marketing copy. LLM generates content following your brand voice and formatting rules. Human review optional. Batch processing for high-volume generation.
Extract structured data from unstructured text: invoices, contracts, emails, support tickets. LLM parses documents and outputs clean JSON for your database. 95%+ accuracy with validation rules.
AI-powered chat in your app, website, or messaging platform. Context-aware conversations with memory, tool use, and handoff to humans. Connected to your data via RAG for accurate answers.
Choosing the right model is the most important decision in any LLM project. Here is my practical comparison based on real production experience:
Cost optimization: Semantic caching with Redis reduces duplicate API calls by 30-50%. Model routing sends simple queries to cheaper models. Prompt compression reduces token usage without quality loss. Batch processing uses off-peak pricing for non-real-time tasks.
Reliability: Every integration includes retry logic with exponential backoff, fallback models (if GPT-4 is down, switch to Claude), request queuing for rate limits, and comprehensive error logging. Your application never breaks because of an API outage.
Monitoring: Real-time dashboards tracking latency, cost per query, error rates, and output quality. Alerts for cost spikes, quality degradation, and API issues. Full audit trail for compliance.
I analyze your application, data flows, and user needs. I identify which tasks benefit from LLM integration, select the optimal model for each, and estimate costs. Deliverable: technical specification with architecture and cost projections.
I design, test, and optimize prompts for each use case. This includes system prompts, few-shot examples, output formatting, guardrails, and edge case handling. Prompt quality determines 80% of output quality.
I build the integration layer: API wrappers, caching, rate limiting, error handling, model routing, and output parsing. All integrated into your existing application architecture with clean, documented code.
Systematic evaluation against a test suite covering your key scenarios. I measure accuracy, latency, cost, and edge case handling. Cost optimization phase typically reduces API spend by 40-70%.
Staged rollout with monitoring. I set up dashboards, alerts, and cost tracking. Post-launch support includes prompt tuning based on real usage patterns and model updates as new versions release.
Built a Telegram bot unifying multiple AI models in one interface. Users choose between GPT-4, Claude, and open-source models based on their task. Implemented credit-based billing, admin panel, and scalable architecture with task queues. The platform reached monetization within the first month.
Integrated GPT-4 into a clothing store chatbot with product catalog knowledge. The LLM generates personalized recommendations, answers sizing questions, and handles returns -- all grounded in actual product data via RAG. Automated 70% of support and increased conversion by 35%.
Fixed-price contracts. All prices include prompt engineering, integration development, testing, cost optimization, and 30 days of post-launch support.
Kirill is a freelance AI engineer in Barcelona specializing in LLM integration, RAG development, and AI agent development. He has integrated LLMs into e-commerce platforms, multi-model AI aggregators, customer support systems, and content generation tools. 15+ production projects delivered.
Stack: Python, OpenAI API, Anthropic API, LangChain, Django, PostgreSQL, Redis, Docker. Fixed-price contracts. English, Spanish, Russian.
Tell Kirill about your application and use case. He will recommend the right model, estimate costs, and provide a fixed-price proposal -- within 24 hours.
Book a free LLM consultation