Automate the routine.
Scale the results. Автоматизирую рутину.
Масштабирую результаты. Automatizo la rutina.
Escalo los resultados. Автоматизую рутину.
Масштабую результати.

Traditional automation is a fixed script: if X happens, do Y. Agentic workflows add a reasoning layer — the AI decides what to do next based on context. This means the same workflow handles edge cases and unexpected inputs without crashing.

The 5 workflows:

① Lead qualification pipeline — scores and routes inbound leads automatically. Saves sales teams 2–3 hrs/day of triage.

② CV screening agent — reads, scores, and ranks applications against job criteria. Reduces shortlisting from days to minutes.

③ Daily briefing digest — monitors news, mentions, and competitor activity. Delivers a structured summary every morning.

④ Email draft assistant — generates first-draft replies to common customer queries, ready for one-click review.

⑤ Meeting notes to tasks — transcribes, extracts action items, and syncs them to your project tool automatically.

The biggest mistake teams make is automating the wrong thing first. Start with the task your team complains about most — not the one that sounds most impressive.

RAG (retrieval-augmented generation) keeps your data outside the model. At query time, the system fetches relevant chunks and passes them to the LLM as context. The model itself never changes. Fine-tuning modifies the model's weights using your data — baking knowledge directly into the neural network.

Choose RAG when: your data changes frequently, you need source citations, budget is limited, or you want to audit what the AI retrieves.

Choose fine-tuning when: you need a very specific output style, data is static and well-structured, or response latency is critical.

Cost comparison (2025): Prompt engineering — ~$0. RAG setup — $50–500. Fine-tuning — $500–5,000+.

For 90% of business automation projects, RAG is the right starting point. It's faster to build, cheaper to maintain, and easier to debug when something goes wrong.

The four questions that matter:

① What triggers this process today? Email arrives? Form submitted? End of day? Knowing the trigger defines where automation begins.

② What does "done" look like? A row in a spreadsheet? A sent email? Without a clear output, there's no way to test success.

③ What are the exceptions? Every process has edge cases the team handles manually. These need to be mapped — or the automation will silently fail on 20% of cases.

④ Who approves what? Any step requiring a human decision needs an escalation path. Otherwise you've built a workflow that gets stuck.

Watch out for phrases like "it's simple, just automate it" (usually means they haven't mapped the process) and "the AI should know what to do" (means no clear output has been defined).

Spend 30 minutes mapping the current manual process before writing any workflow. Draw it as a flowchart — if you can't, the process isn't ready to automate yet.