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Notes from the work

What deploying AI and custom software inside a company actually looks like: what costs what, what breaks, and what to ask a supplier.

  1. How to choose a custom software supplier in SK/CZ, and what to ask

    Five areas to go through before signing: architecture, code ownership, GDPR, verifiable references and post-launch support. Plus the warning signs.

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  2. How AI automation works for law firms

    Contract review, precedent search, compliance monitoring and due diligence — and why in a legal setting it is the architecture that decides, not the model.

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  3. Security risks when deploying an LLM into company processes

    Six risks that come not from the model but from the architecture around it: data leakage, prompt injection, access separation, agent actions, hallucination and auditing.

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  4. How a multi-tenant vector database serves many clients at once

    One AI infrastructure for dozens of clients without their data mixing: metadata filtering, the architectural decisions that matter, and the isolation tests.

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  5. What custom software costs compared with off-the-shelf SaaS

    Cost compared over three to five years: when a subscription wins, when building your own wins, and the hidden costs on both paths.

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  6. What a custom AI agent costs to build for a B2B company

    What actually drives the price of an AI agent: scope of autonomy, integrations, RAG infrastructure, compliance, and the running costs suppliers rarely mention up front.

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  7. What an AI agent is and how it differs from a chatbot

    A chatbot answers the question; an agent completes the task. The architectural difference, three levels of autonomy, and when each is enough.

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  8. What RAG is and how it works in enterprise chatbots

    RAG explained for companies: how the model answers from your own documents, when it beats fine-tuning, and what decides the quality of a deployment.

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