Which LLM models do you work with?

I work with all major LLMs: OpenAI GPT-4o and GPT-4 Turbo, Anthropic Claude 3.5 Sonnet and Opus, Meta Llama 3, Mistral, and other open-source models. I help you choose the right model based on your quality requirements, latency needs, cost budget, and data privacy constraints.

How do you reduce LLM API costs?

I use several strategies: semantic caching (same questions get cached responses), model routing (simple queries go to cheaper models, complex ones to GPT-4), prompt optimization (shorter prompts = lower costs), batch processing for non-real-time tasks, and response streaming to reduce perceived latency. Most clients see 40-70% cost reduction after optimization.

Can you integrate LLMs with my existing software?

Yes. I integrate LLMs into any software with an API or database. Common integrations include CRM systems, helpdesk tools, e-commerce platforms, internal dashboards, and mobile apps. I build the integration layer that connects your existing systems to LLM APIs with proper error handling, rate limiting, and monitoring.

How long does LLM integration take?

A single LLM integration with prompt engineering takes 2-3 weeks. Multi-model platforms with routing and optimization take 4-6 weeks. Enterprise infrastructure with full monitoring and compliance features takes 8-12 weeks. Every project starts with a technical assessment and fixed-price estimate.

LLM Integration Services: GPT, Claude & Open-Source Models

I integrate large language models into your business applications -- the right model for each task, optimized for cost, speed, and accuracy.

01 / OVERVIEW

LLM Integration for Business Applications

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.

02 / WHAT I BUILD

LLM Integration Patterns

Content Generation

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.

Data Extraction & Analysis

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.

Conversational AI

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.

03 / MODELS

Models I Work With

Choosing the right model is the most important decision in any LLM project. Here is my practical comparison based on real production experience:

GPT-4o (OpenAI): Best general-purpose model. Excellent at following complex instructions, structured output, and code generation. EUR 2.50/M input tokens. My default recommendation for most business applications.
Claude 3.5 Sonnet (Anthropic): Best for long documents (200K context), nuanced analysis, and safety-critical applications. EUR 3/M input tokens. Preferred for document analysis, legal/compliance tasks, and content review.
Llama 3 (Meta, open-source): Free to run, full data privacy. Performance approaching GPT-4 for many tasks. Requires GPU infrastructure (EUR 100-500/month hosting). Best for high-volume tasks or strict data sovereignty requirements.
Mistral (open-source): Lightweight and fast. Excellent cost-performance ratio for simpler tasks like classification, extraction, and summarization. Can run on modest hardware.
Multi-model routing: I build systems that route each query to the optimal model based on task complexity, reducing costs by 40-70% while maintaining quality for complex queries.

04 / TECHNOLOGY

LLM Integration Stack

Python OpenAI API Anthropic API LangChain LlamaIndex vLLM Ollama Django FastAPI PostgreSQL Redis Celery Docker Prompt Engineering

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.

05 / PROCESS

How I Integrate LLMs Into Your Product

Use Case Analysis

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.

Prompt Engineering

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.

Integration Development

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.

Testing & Optimization

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%.

Launch & Monitoring

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.

06 / CASE STUDIES

LLM Integration Projects

Multi-Model AI Aggregator (Telegram Bot)

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.

Python OpenAI Claude Celery Redis

AI Chatbot for E-commerce

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%.

Django OpenAI API RAG E-commerce

07 / PRICING

LLM Integration Pricing

Fixed-price contracts. All prices include prompt engineering, integration development, testing, cost optimization, and 30 days of post-launch support.

Single Integration

€2,000 / from

One LLM, one use case

Single model integration
Prompt engineering
Error handling & retries
Basic caching
2-3 weeks delivery
30 days support

Multi-Model Platform

€5,000 / from

Multiple models, smart routing

Multi-model routing
Fallback chains
Semantic caching
Cost optimization (40-70% savings)
Monitoring dashboard
4-6 weeks delivery
30 days support

Enterprise Infrastructure

€10,000 / from

Full LLM gateway & compliance

LLM gateway with auth
PII detection & redaction
Audit logging
Custom model hosting
Evaluation pipeline
8-12 weeks delivery
60 days support

Frequently Asked Questions

Which LLM model should I use for my project?

It depends on your task, budget, and data privacy needs. GPT-4o is the best general-purpose choice for most business applications. Claude 3.5 Sonnet excels at long documents and nuanced analysis. Llama 3 is the best option when data cannot leave your infrastructure. I help you choose during the discovery phase -- often the answer is a combination of models with smart routing.

How much do LLM API costs add up to monthly?

Typical monthly API costs: EUR 20-100 for light usage (100-500 queries/day), EUR 100-500 for moderate usage (500-2000 queries/day), EUR 500-2000 for heavy usage. With proper caching and model routing, I typically reduce these costs by 40-70%. I provide detailed cost projections before development starts.

Can you integrate LLMs with my existing CRM/helpdesk?

Yes. I integrate LLMs with any system that has an API: HubSpot, Salesforce, Zendesk, Intercom, Freshdesk, and custom systems. Common use cases: auto-drafting ticket responses, summarizing customer interactions, extracting data from emails, and generating reports. See my CRM integration services for more.

What about data privacy and GDPR?

For strict data privacy: Azure OpenAI keeps data in your EU tenant, self-hosted Llama/Mistral keeps everything on your servers. I implement PII detection and redaction before any data reaches external APIs. All integrations include audit logging and data processing agreements. Your compliance team gets full documentation.

What happens when LLM APIs have outages?

Every integration I build includes automatic fallback. If GPT-4 is unavailable, the system switches to Claude or a local model. Request queuing handles rate limits gracefully. Cached responses serve common queries even during complete outages. Your users never see an error -- they get a slightly different model response instead.

08 / YOUR DEVELOPER

About Kirill Strelnikov

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.

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