Systems thatrun the work.Not chatbots that talk about it.

Most agency sites stop at presentation. A visitor still has to search for a contact, explain the same context again, and wait for someone to manually turn an unstructured message into a usable request. A site about automation should prove the idea in the interaction itself.

We designed and developed the complete multilingual site, then connected its AI chat and audit form to separate self-hosted n8n workflows. The interface passes language, source, session and message context through webhooks; the workflows validate and structure each request, maintain the conversation path, update the operating records and send the right alert. The visitor sees one simple interface — underneath it runs custom routing, memory and business logic.

A B2B company received inbound leads, but qualification was manual and inconsistent, response speed depended on how busy managers were, nobody analysed dialogue quality, and the CEO assembled funnel reports from the CRM by hand.

Three n8n workflows working as one closed loop. Workflow 1 qualifies leads via BANT — through a website chatbot or a voice AI agent — scores them and creates the CRM record. Workflow 2 analyses every dialogue daily: agent mistakes, the customer's real problem, successful patterns. Workflow 3 runs every Friday and delivers a structured funnel report to the CEO in Telegram.

An online home goods store handled all customer emails in one shared inbox, sorted by hand. VIP customers got buried between spam and routine questions, requests had no owner, and overdue tickets surfaced only when the customer was already unhappy.

AI classifies every incoming email — category, priority, responsible owner — and explains its decision in a few sentences, so the manager sees the logic. The ticket lands in a single database and the owner gets an instant Slack notification. SLA control runs alongside: every ticket has a deadline, and overdue ones escalate on their own — owner, then team lead, then director.

Consistent social content requires the same production loop every time: find an angle, write the caption, create the visual, review both, publish and prevent accidental duplicates. Doing it manually is slow; publishing AI output without review puts the brand at risk.

The Telegram bot turns a short brief or an uploaded image into an English caption, hook, visual headline, alt text and a premium 4:5 image. Redis stores the draft and its revision state. The owner can publish, rewrite the caption, regenerate the image, rebuild everything, edit or cancel. Only an explicit Publish action starts the official Instagram API flow; ownership checks, revision guards and publishing locks protect the process.

Turning long-form text into publish-ready audio meant manual TTS runs, manual audio stitching, manual file naming and archiving — every single episode.

A multi-stage n8n workflow operated entirely through a Telegram interface: it accepts source text, prepares it for generation, generates speech via Gemini TTS, buffers and merges audio chunks with ffmpeg, names the output consistently, delivers the finished audio file back to Telegram, and stores the final package in Google Drive. Now on its tenth production iteration (v10).

Customer calls and feedback keep accumulating, but nobody has time to listen to hundreds of recordings or read every message. The same failure can repeat for months while the owner only discovers it by accident — after customers have already been lost.

Recordings are transcribed with speaker diarization, then every call, review, form response and message is tagged by an LLM: topic, sentiment, real intent and outcome. Each interaction becomes a structured database row linked to its source. A chat layer answers natural-language questions with counts and real quotes; red-flag triggers send immediate context to a messenger, while scheduled comparisons surface recurring problems and what to fix first.