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AI automation case studies

Our Work: real projects, measurable operational change.

Explore how teams changed the work itself, from manual invoice entry to investor research, with the workflow before, the system we built and what improved after launch. Then see our solution showcases: step-by-step workflows for clinics, property agencies, manufacturers, finance teams and more.

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Headline resultsFrom the client projects on this page
1AutoCount invoice entryAccounting firm, 1,500 invoices a month100+ hrsrecovered a month
2Investor researchCapital-raising team, per profile~15 → ~1 minresearch time
3Target desk researchPrivate credit firm, per target firm~7 minunattended run
4Guessed email addressesCompany intelligence, by design0evidence only
How each figure was measured is shown on its case study
Our Work

Client projects, documented end to end.

Each project shows the challenge, the system we built, the results with how they were measured, and the software it runs on. Client names are withheld unless the client has approved them.

Invoices passing checks and filing into a ledger grid: AI invoice entry into AutoCount for an accounting firm
Accounting firm · AutoCount

How an Accounting Firm Saved 100+ Hours a Month with AI Automation

Challenge
Around 1,500 supplier invoices a month were keyed into each client’s AutoCount book by hand, at about 5 minutes each.
Solution
An AI workflow that reads each invoice, suggests coding from that client’s own booking history, checks for duplicates and wrong-client invoices, and creates a draft in AutoCount after an accountant approves it.
Results
100+ hrsof keying recovered a month
~5 → <1 minper invoice, including review
1,500invoices processed a month
System & industry
Malaysian accounting firm. AutoCount Cloud, n8n, a review dashboard and AI document reading.
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Investor profiles, a few highlighted in blue, linked to a paper plane: AI investor sourcing and outreach platform
Capital markets · Research & outreach

How a Capital-Raising Team Cut Investor Research from 15 Minutes to 1

Challenge
About 15 minutes of manual LinkedIn research per investor profile, spreadsheets rebuilt for every raise, and unverified emails that bounced.
Solution
A research and outreach platform that sources, enriches and scores investors, verifies emails and prepares personalised outreach for the team to approve and send.
Results
~15 → ~1 minresearch per investor profile
92%deliverability on the platform dashboard
7,968investor profiles sourced and reusable
System & industry
Boutique capital-raising firm. n8n, AI agents, email verification and a live sourcing and outreach dashboard.
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Office towers with deal, hiring and growth signals around one highlighted firm: AI company intelligence for a private credit firm
Private credit · Company intelligence

How a Private Credit Firm Researches a Target Desk in About 7 Minutes

Challenge
A new fund needed the people on specific desks inside 63 target firms, not company-wide contact lists, and guessed emails would harm its sender reputation.
Solution
A company intelligence tool that finds the people on a chosen desk, screens them against the team’s brief, ranks them by seniority and records the evidence behind each email, next to the firm’s recent deals and open roles.
Results
~7 minper target firm, unattended (average)
193qualified contacts in the first seven runs
0guessed emails, by design
System & industry
Kuala Lumpur private credit firm. n8n, OpenAI, LinkedIn data, contact providers and Google Sheets.
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How we report results

Every figure says where it came from.

A dashboard next to a big number can easily mislead. On each case study, figures carry one of these labels, with the method and timeframe in a short evidence note under the results.

Measured

Taken from the system

Counts, run times and costs recorded by the live system or its logs.

Calculated

Worked out, with the sum shown

Derived from stated inputs such as volume and time per task, with the arithmetic on the page.

Client estimate

The client’s own framing

How the client describes the impact, labelled as theirs rather than presented as a measurement.

Illustrative

Recreated interfaces

Screens rebuilt for the page with sample names or data, so confidential client records stay private.

Why names are withheld. Our clients’ names and their customers’ data stay private unless the client approves otherwise. The workflows, systems and figures describe the real engagements.

What the projects share

Automation that people stay in charge of.

Different industries, the same approach: take the repetitive work out, keep the decisions with the team.

01

Human approval where it matters

Invoices become drafts only after an accountant approves them. Outreach is approved before it is sent. Every contact gets a person’s review.

02

Built around existing tools

AutoCount, Google Sheets, email and LinkedIn data the teams already used stay in place. The automation connects them.

03

A record of what happened

Activity logs, review history and source links are kept, so the team can see why the system suggested what it did.

Have a similar process?

Let’s map your workflow before anything is built.

Tell us where your team spends time on repetitive work. We start with a free discovery consultation, then prepare a tailored initial plan and proposal once we understand your process. Projects are quoted individually after discovery.

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