What we’ve shipped.

An established construction firm runs its entire operation in Monday.com — six years of activity, hundreds of project items, thousands of update entries with mixed business and technical content. The team can no longer tell who owns what, what’s overdue, or what mattered last quarter. Salesforce and Sales Auto Pilot integrations exist but don’t help with the buried information.

Pulled six years of project data from Monday via n8n. Ran it through five parallel client-tuned AI nodes for extraction and structuring. Pushed the result back into Monday: actionable next-step items with owners and deadlines, plus a generated summary document on every project element to accelerate onboarding for new joiners.

Audio tech company runs everything in Monday.com — but webshop activity (Unas.hu) was entered manually into Monday. Wholesale order grouping and verified dispatch happened entirely outside Monday because of platform limitations.

Built an n8n integration syncing Unas to Monday. Significant data cleansing required — Unas API delivers data in non-standard XML, requiring a bespoke parser. Added a custom admin board in Monday where the company head coordinates and dispatches orders with a few clicks, orchestrating the underlying n8n flows from a single Monday control board.

Industrial woodworking tool manufacturer ran full project management in Monday but hit subscription limits — items spilled over, archives became unsearchable. Field sales and service staff couldn’t find customer info on site. Older staff struggled with mobile Monday admin, so daily admin lagged and miscommunication grew.

Built a parallel database in n8n Data Tables holding the full data model with cleaner business entity relationships than the native Monday structure. Monday showed leadership a simplified view. Sales and service staff queried info via a free-text chat interface from outside Monday, and could log their day’s activity in natural language — n8n AI agents inserted the right admin items into the right Monday tables and confirmed by email.

A mid-market advertising agency processes high volumes of vendor invoices monthly, arriving by email in varying formats and languages. Each one required manual entry into the bookkeeping software: invoice number, dates, amounts, VAT codes, vendor info — followed by attaching the PDF. Monotonous, time-intensive, error-prone — especially for cross-border and reverse-charge VAT cases.

Built an n8n-based pipeline using OCR plus AI extraction at 99% accuracy. Auto-files the PDF, pushes invoice header data into the ERP via API. The bookkeeper reviews, categorises, and finalises. The system never finalises automatically — that decision stays human.

A corporate group running hotels in Budapest and across the country faced daily reconciliation pain. Cashier records had to be matched manually against statements from 5–6 banks and card processors — every transaction, every day. The reconciled data then had to be assembled by hand into an import file for the bookkeeping software.

Built an n8n-based system that takes over the full reconciliation. Reads the cashier record and all bank statements, matches transactions with intelligent pairing logic, and generates the import file directly. The bookkeeper just loads the finished file.

A 31-company corporate group needed monthly visibility into the full portfolio’s financial position. Until now, the controlling team processed each company’s general ledger one by one, manually filtered relevant items against a defined chart of accounts, and assembled the executive report by hand. 4–6 hours every month, error-prone, often delivered late to decision-makers.

Built an n8n-based pipeline that processes all 31 ledgers automatically against the chart of accounts and produces a single consolidated, readable executive report. Refreshes monthly. Added an interactive dashboard visualising the most important financial indicators.

Plant performance and daily traffic figures were assembled manually from Excel sheets every day. Staff spent significant time gathering, formatting, and laying out the reports. Leadership tried to read trends from raw tables — slow, hard to scan, error-prone.

Built an automated reporting system that pulls current data from existing sources at scheduled intervals, processes it, and produces visually formatted reports — no human assembly. Reports go out automatically, ready for leadership review.

Monthly executive reporting required days of manual work pulling data from multiple sources — financial statements, revenue figures, market data, customer feedback. The aggregation and analysis was repetitive and time-intensive. Worse, manual processing kept the analysis shallow.

Built an automated reporting system that collects data from all sources. AI analysis surfaces trends and patterns in customer feedback and market data, then everything is assembled into a formatted, executive-level monthly report.

Certain roles attract high volumes of applications, and manual CV review can take days. HR spends most of their time on initial screening rather than interviews and selection. Strong candidates can slip through manual review, and scoring is subjective and hard to reproduce.

An AI-based recruiting assistant that processes applications automatically. Extracts relevant CV data, scores and ranks candidates against the position requirements, produces a summary report for HR. Human decision-making stays — AI handles only the pre-screen.

New hire onboarding is a recurring challenge as companies grow. Internal processes, systems, and policies live scattered — in documents, in emails, in the heads of experienced colleagues. New joiners’ questions consume existing team time, and the quality of onboarding depends on who happens to be available.

A knowledge-base AI assistant integrated into the company’s internal system as a chat surface. Trained on company documents and policies. New joiners can ask in natural language, get consistent, knowledge-base-grounded answers. Always current as the documents update.

Visitors to the company website got customer support responses only during business hours. Repetitive questions tied up support capacity. Response times ran from hours to days. Out-of-hours leads went unanswered — and easily lost.

An AI chatbot integrated into the company website, trained on services and product information. Answers visitor questions in natural language, hands off to a human when needed, captures lead data for sales.

Leaders and decision-makers can’t read every project status update daily, but they need fast, contextual answers without manually browsing entries.

An embedded AI assistant processing real-time data: trigger recognition (e.g., emoji-based categorisation — 🔴 = critical, ✅ = done), natural language search, structured answer generation. The user asks, the AI answers from all available data — including non-English content.

Hundreds of legal questions across multiple Google Sheets, thousands of pages of legal text across documents. Answers had to be matched precisely, in audit format. A full legal team’s worth of work.

An n8n-based pipeline reads questions from the sheets one by one, searches vectorised documents for answers, and inserts the result back into the original sheet next to the question with the source location.

In regulated industries — cosmetics, food, pharmaceuticals — skipping or reordering manufacturing steps creates product safety and legal risk. Human judgement is not a reliable gatekeeper.

Multi-step manufacturing workflow where every state transition (material issue → filling → labelling → packaging → QC) requires role-based approval. The system enforces order, blocks invalid transitions, validates business rules — for example, a 60-day maturation requirement: if someone tries to use an immature batch, hard block, no override.

In complex systems, database inconsistencies — NULL fields, orphan records, constraint violations — accumulate quietly. They surface only when they cause business issues.

Scheduled health check runs twice daily, 8+ consistency checks. Automated HTML email alerts on failure. Separate daily 6am system health check, weekday 7am QC summary covering open approvals, expired processes, QC status.

In manufacturing environments, every material movement must be tracked under GMP — but manual entries are error-prone, slow, and don’t scale. One manufacturing step can trigger multiple warehouse movements.

Event-driven system where one production action automatically triggers all related warehouse movements (e.g., material issue W01→W04, scrap W04→W09, finished product W06→W07). Generic audit trigger on 51 tables — every INSERT/UPDATE/DELETE auto-logged: who, what, when, old value, new value.

In regulated industries — real estate, construction, finance — document format, content, and completeness must meet strict requirements. Manual document generation creates errors and inconsistency.

A structured-input-schema-driven system that validates required fields, generates content with AI, applies logical completeness checks, and exports to Word and PDF — enforcing a unified format organisation-wide.

Companies often receive non-uniform data — PDFs, Excel files, free text, scraped URLs. Manual processing is slow, error-prone, and doesn’t scale.

Automated pipeline accepts raw sources, normalises fields to a configurable schema, AI-enriches (description generation, categorisation, missing field filling), exports to system-compatible format. UI side for mapping configuration and preview.

Companies often run multiple workspaces, accounts, or external systems in parallel. Data is scattered, manual switching is constant, and there’s no unified view.

Recurring pattern: a single user connects multiple external accounts/systems. The system stores credentials per source, the backend uses the correct token per API call. Data appears grouped by source on a unified surface.