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Artificial Intelligence

AI in production: chatbots in MoreCRM, image analysis in photo services. Systems that pay back in 4-6 months — not buzzword deliverables.

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12 Own AI products in production
3 LLM families in stack (GPT, Claude, Gemini)
4 -8 Weeks PoC to MVP
01

Chatbots and AI assistants (LLM, RAG)

02

Document analysis and data extraction

03

Recommendation and personalization systems

04

Predictive business analytics

05

Decision process automation

06

AI integration with existing systems

How we work

1 Use Case Identification
2 Proof of Concept
3 Training & Fine-tuning
4 System Integration
5 Monitoring & Iteration

Technologies

OpenAI LangChain Azure AI Python Vector DB Hugging Face

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FAQ

Questions we answer before you ask them.

It depends. Default: OpenAI / Anthropic / Gemini via API — fastest to ship, low operating cost. Local models (Llama, Mistral via Ollama / vLLM) when compliance demands it (public sector, sensitive data) or when volume makes API costlier than self-hosted infra.

Provider APIs (OpenAI Enterprise, Anthropic, Gemini) offer no-training modes and DPAs. For compliance-heavy projects we filter PII before sending + use a local model for sensitive data. Every AI integration goes through a privacy review before production.

Assist. Every AI system we ship has a human in the loop — the agent proposes, the human accepts (graceful degradation). That's how Textio (our AI product) works: it generates, an editor approves, the system publishes. If you want "AI that replaces" — we pass.

Operating cost = API tokens + infrastructure. For a mid-size system (10k queries/mo, GPT-4 class): PLN 200-800/mo. At scale (100k+/mo) we consider fine-tuning + caching; cost drops to PLN 100-300/mo. We model this before shipping.