AI Integration

AI that fits the way you work.

Connect models to your knowledge and everyday tools. Search, assistants and automation with measurable quality, clear boundaries and human review where it matters.

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What's included

The stack is selected for your requirements; these are options, not a requirement to use every tool.

Knowledge & retrieval

Prepare documents, preserve access rules and retrieve relevant context. Let answers refer back to their sources instead of relying on model memory.

Assistants & workflows

Connect models to product actions and external services. Validate structured output, handle failures and place approvals before sensitive actions.

Quality & operation

Evaluate representative tasks before release. Track response quality, latency and cost, with a fallback when the model cannot complete the request.

LLM integration (OpenAI, Anthropic, open-source)
RAG pipelines with vector databases
Semantic search (pgvector, Pinecone, Weaviate)
AI chat interfaces and streaming responses
Production monitoring: accuracy, latency & hallucination tracking
Automated knowledge base sync & document indexing
Multi-provider integration (OpenAI, Claude, Gemini)
Secure API gateway with authentication & rate limiting
Workflow automation for support & operations tasks
CI/CD pipelines for model & deployment updates

How it works

Identify where AI adds genuine value. Define evaluation criteria.
What you receive
A bounded use case, sample tasks and measurable acceptance criteria.
Choose models, design data flow, plan context management.
Build and test the AI pipeline with real data samples.
Connect to your existing application with proper UX for AI interactions.
Set up evals, monitor quality, iterate based on real usage.

What you receive

A bounded use case, sample tasks and measurable acceptance criteria.

Frequently Asked Questions

What AI services and APIs do you integrate?

I integrate OpenAI (GPT-4, Assistants API), Anthropic Claude, Google Gemini, Replicate for image generation, Pinecone for vector search, and LangChain for agentic workflows. I also build custom RAG pipelines and fine-tune models for domain-specific tasks.

How long does AI integration take?

A simple AI feature (chatbot, content generation) takes 2-4 weeks. A complex AI pipeline with RAG, custom embeddings, and multi-agent systems typically takes 6-12 weeks. Timeline depends on data preparation and infrastructure complexity.

Is AI integration expensive to operate?

I design cost-efficient AI architectures with caching, prompt optimization, and model selection strategies. Most integrations use API pricing models starting at a few dollars per 1M tokens, making it affordable for startups. I provide cost estimates before implementation.

Can AI replace existing manual workflows in my business?

Often yes. I analyze your current workflows and identify where AI can automate repetitive tasks, improve accuracy, or reduce costs. Common use cases include document processing, customer support automation, content generation, and data extraction.

Do you build custom AI models or only use existing APIs?

Both. For most use cases, existing APIs (OpenAI, Claude) are faster and cheaper to integrate. For specialized domains, I implement fine-tuning or train custom models. I always recommend the most cost-effective approach for your specific needs.

Let's work together

My colleagues are ready to help with your project. Send an inquiry to discuss the scope and availability.

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