Problem → automation → result

Enquiries arrived in a shared inbox. Someone re-typed each one into the CRM, judged whether it was worth pursuing, then wrote a reply from memory. Anything after 4pm waited until the next morning.

Form webhook into an n8n workflow: an AI step reads the enquiry, scores fit against their criteria, creates the CRM record with notes, sends a personalised reply, and posts the summary to the team channel.

A property law firm assembled every residential lease by hand: picking the right legal clause variant for rent adjustment, cosmetic repairs, subletting and liability from memory, then retyping the landlord, tenant and property details into a fresh document each time. Under deadline, a missed clause update or an inconsistent variant was an easy and costly mistake to make.

A two-stage web intake now splits the work by who actually owns each decision. Clients fill in the property and tenancy facts through a form, which emails a summary straight to the firm, while the reviewing lawyer separately selects the legal clause variants for rent adjustment, repairs, liability and subletting. Both feed a lawyer-approved Word template with conditional clause blocks built on Azure Functions, and the finished, ready-to-sign contract is generated and stored automatically.

A German law firm's website could only take in new-client enquiries in German, so anyone who reached out in another language got a slower, less complete response, if they got one at all. Every enquiry still had to be read, judged for whether it was worth taking on, and replied to by hand before a single consultation ever happened.

A multilingual AI chat widget now guides every prospective client through a structured intake conversation in their own language, auto-detected from their first message, supporting German, English, Turkish, Arabic and Russian. It extracts the case details, accepts uploaded documents, and submits everything to a lawyer's review portal, sending the client an immediate confirmation and the lawyer a case notification by email.

Front-desk staff spent the day on opening hours, pricing, availability and booking changes. That interrupted the work they were actually hired for. After hours, calls went unanswered.

A voice agent trained on their own answers and price list, connected to the booking calendar. It answers, books or reschedules, and hands anything unusual to a human with the context attached.

Google Ads, GA4 and the CRM each reported different lead counts. Budget decisions were made on numbers nobody trusted, and blocked browser tracking was quietly dropping conversions.

Server-side GTM container with GA4 and ads conversions fired from the server, lead IDs carried through to the CRM, and qualified-stage events sent back to the ad platforms automatically.

Product, licensing and compatibility questions arrived by email, web chat and a help desk. Each agent answered from their own notes, so replies varied and new staff took weeks to get accurate.

An assistant grounded in their own documentation and past resolved tickets drafts a reply with source links the moment a ticket lands. Agents edit and send; anything below a confidence threshold is routed untouched to a specialist.

Delivery notes and invoices arrived as PDFs and scans in a mailbox. Staff opened each one, typed reference numbers, quantities and totals into the operations system, and mismatches surfaced only at month-end reconciliation.

An intake workflow extracts fields from each document, validates them against open orders, writes matched records straight into the system and puts anything ambiguous into a short review queue with the original attached.

Quotes were sent the same day, but the follow-up sequence lived in the owner's head. Busy weeks meant sent quotes were never chased, and there was no view of which jobs were still open.

When a quote is sent, a sequence starts: a reminder, a check-in, and a final message, each stopping the moment the client replies or the job is marked won or lost. Every touch is logged against the record.

Every account manager exported numbers from analytics and ad platforms, pasted them into a template, wrote commentary and emailed each client. Formats drifted, figures were occasionally stale, and the whole team lost the start of each month.

A scheduled workflow pulls each client's metrics into a standard report, fills the template, flags month-over-month movements worth commenting on, and delivers a draft to the account manager to review and send.

Customer details lived in the CRM, the invoicing tool and a support platform. An address change had to be made three times, and whichever system was missed became the one someone quoted from.

A synchronisation layer with a defined source of truth per field: changes propagate through webhooks, conflicts are logged rather than silently overwritten, and a nightly check reports records that drifted.