Proof, not promises.

Problem

The ops team hand-keyed supplier invoices from a dozen vendors, each with a different PDF layout, into spreadsheets. It was a standing weekly job and unit prices were always days out of date.

Build

An Apps Script watcher pulls each invoice PDF from Gmail and passes it to a Document AI parser that reads any layout and returns structured line items. Per-supplier tabs auto-populate with SKU, pack price, and unit cost, with dedup and a daily summary email.

Result

Invoice entry stopped being a manual job and the master price list refreshes on its own. What used to be a recurring weekly chore now runs unattended, with the team looking only at what the parser flags.

Problem

New enquiries and post-meeting follow-ups were tracked in someone's head and a messy spreadsheet. Leads went cold because nobody had time to chase them consistently.

Build

A workflow automation captures every enquiry into one pipeline, then an AI step drafts a tailored follow-up email per contact and schedules a timed nurture sequence. Replies pause the sequence and flag the contact for a human, so nothing double-sends.

Result

New contacts get a first touch and a follow-up cadence without anyone remembering to send one, and replies pull a human in instead of continuing the sequence. Chasing stopped depending on someone's memory.

Problem

Salesforce opportunities and the billing system drifted apart, so every month-end meant a manual reconciliation to find mismatched accounts, amounts, and close dates before reporting could be trusted.

Build

A scheduled integration reconciles Salesforce against the billing system on a nightly run, matching records, flagging discrepancies, and auto-correcting the safe ones. An exceptions report surfaces only the handful of rows a human actually needs to review.

Result

Month-end stopped being a full manual reconciliation and became a review of a short exceptions list. Reporting starts from data the team trusts instead of data they first have to repair.

Problem

Every Shopify order was triaged by hand: tagging, routing to the right fulfilment path, flagging risky or high-value orders, and copying details into a fulfilment sheet. It was slow and error-prone at volume.

Build

A webhook-driven automation reads each new Shopify order, tags and routes it by rules, flags exceptions for review, and writes clean rows into the fulfilment and reporting sheets. A weekly reporting job rolls up volume, exceptions, and turnaround with no manual export.

Result

Routine orders flow through untouched and the team looks only at the exceptions that need a decision. The weekly ops report is generated by the same job instead of being assembled by hand.

Problem

Support drowned in repeat questions already answered in the docs, while genuinely hard tickets waited behind them. Canned macros did not help because customers asked in their own words.

Build

A retrieval agent grounded in the product docs, changelog, and past resolved tickets. It answers in the help widget with citations, and hands off to a human the moment confidence drops or the question touches billing or account access. Every answer is logged with the sources it used.

Result

Documented questions get answered by the agent whenever they arrive, so the team spends its day on tickets that need a person. Escalations arrive with the conversation history already attached.

Problem

Portal enquiries, web forms, and WhatsApp messages landed in different places. Response time depended on who was free, and the slowest leads went cold before anyone called them.

Build

Every source feeds one GoHighLevel pipeline. A first reply goes out automatically on the channel the lead used, qualifying questions run automatically, and viewings are booked straight into the agent's calendar. Unqualified leads keep a long nurture sequence instead of being dropped.

Result

Speed to first contact stopped depending on who was at a desk. Enquiries arriving overnight or at a weekend get the same automatic first reply as the ones arriving mid-morning.

Problem

Month-end meant exporting from the accounting system, the bank, and two spreadsheets, then rebuilding the same charts by hand. By the time the pack was ready the numbers were already stale.

Build

Scheduled pipelines pull from each source nightly, reconcile them against a single chart of accounts, and publish a live dashboard plus a formatted PDF pack. Variances outside a threshold are flagged with a note rather than left for someone to spot.

Result

Reporting went from a manual rebuild to a scheduled job, and the leadership team reads current numbers instead of last month's.

Problem

New patients filled in paper and PDF forms that staff re-typed into the practice system. Handwriting, missing fields, and insurance details made it slow and caused avoidable rework.

Build

A digital intake flow with validation at the point of entry, document parsing for uploaded insurance cards and referrals, and a structured write into the practice system. Anything ambiguous is queued for a human rather than written automatically. Access is scoped and every write is logged.

Result

Intake data lands complete and structured on the first pass, and staff review exceptions instead of re-typing every form.

Problem

The team rebuilt the same performance report for every client each month by pulling numbers from several ad and analytics platforms. It scaled with headcount, not with revenue.

Build

A web app that connects each client's ad, analytics, and CRM accounts, refreshes on a schedule, and gives every client a branded login with their own live dashboard. Commentary is written once by the account manager instead of rebuilt alongside the charts.

Result

Monthly reporting stopped being a production line. Clients check results whenever they want rather than waiting for a slide deck.