Reliable workflows. Resilient agents. Real numbers. Automating since 2009.

We build AI systems that ship. 31 so far.

Custom AI agents, workflow automation and digital transformation for Australian businesses and AI startups — delivered by the engineers who built 31 production systems, with the numbers to prove it.

systemize360 — shipping log

Proof, by industry

Numbers first. Adjectives never.

Healthcare & Revenue Cycle12 projects
000/mo
invoices automated, zero touches

Compliance-heavy, paper-heavy, and allergic to downtime. We automated the back office across 300+ facilities without touching clinical systems.

  • 300+ facilities served
  • 25+ workflows automated
  • PII redaction on autopilot
Staffing & Recruitment7 projects
0 wk → 0 min
prospect research cycle

Margins live and die on speed-to-candidate and speed-to-client. We automated both ends of the desk.

  • 100+ candidates screened, scored and shortlisted
  • 24/7 AI sales training inside MS Teams
  • Onboarding hand-offs, automated end to end
Fintech & Collections6 projects
00% → 00%
propensity-to-pay model accuracy

Thousands of accounts, one question: who will actually pay. We answer it daily, with a model that retrains itself weekly.

  • Thousands of accounts scored daily
  • 6 discrepancy types caught by automated reconciliation
  • Full ERP replacement with on-prem AI
Telecom4 projects
000+ hrs/wk
engineering time saved by one KPI alerting tool

Where we learned that systems must not fall over. Fifteen years at Ericsson, automating a national network's worst chores.

  • 35+ daily engineering tasks automated
  • 70+ automated network audit checks
  • Nationwide KPI analytics and alerting

Agentic AI — our flagship work

20 AI agents in production. Yours would be #21.

These twenty agents run today on our own platform — triaging inboxes, scoring CVs, scanning for vulnerabilities, writing LinkedIn posts. They are the proof. The offer is what comes next: custom agents designed around your team's exact workflow — your inbox, your CRM, your compliance rules, your definition of done.

Email → TaskMeeting → TaskVirtual Project ManagerATS / CV ScoringInterview ScorerBDM ProspectorWebsite Vulnerability ScannerNetwork Threat AnalyzerAI CoachGit CoderDeep ResearcherSocial Media ManagerNews Feed CuratorKnowledge Base RAGVoice HubYouTube SummariserVoice NotesLinkedIn ReaderLinkedIn WriterLinkedIn Hunter

Services

Five ways we work.

For SMB owners, operators of legacy systems, and startup founders. Different problems — the same standard: it ships, it runs, it gets measured.

AI strategy & consultation

Fractional CTO-level advice: AI roadmaps, build-vs-buy decisions, vendor and model selection — and honest answers about what will not work for you.

e.g. adoption roadmap prioritised by payback period

Custom AI agents

Agents built for your workflow, not a template. Discovery, guardrails, evaluation and deployment — the same discipline behind our 20 production agents.

e.g. prospecting agent: 1 week of research → 5 minutes

AI adoption & workflow automation

You don't need a data science team. We find the one process where AI pays for itself first, and build that.

e.g. invoice processing: ~500/month, hands-off

Digital transformation of legacy systems

We replace decade-old software without stopping the business — including on-premise AI when your data cannot leave the building.

e.g. full ERP replacement with on-prem language model

Staff augmentation

Embedded AI, automation and GTM engineers, aligned to Australian hours, weekly or monthly. Senior people only.

e.g. a fractional AI engineer inside your standups this month

Featured case studies

Three, in depth.

BDM Lead Intelligence Engine — screenshot
open full document (PDF) →
S360-003Sales & Marketing

BDM Lead Intelligence Engine

Business development managers spent an entire week manually researching prospects — finding companies, identifying decision makers, verifying contact info, and personalizing outreach.

Built a 5-stage AI pipeline that automates the full prospecting workflow: company discovery, key people identification, contact verification (hand-rolled SMTP/DNS verification at zero third-party cost), deep research on prospects (bio, hobbies, interests, icebreakers), and correlation scoring against services offered..

prospect research time
before
1 week
after
5 min
TypeScriptNode.jsPostgreSQLOpenAIClaude
Read the case study →
PayPulse — screenshot
open full document (PDF) →
S360-010Healthcare & Revenue Cycle

PayPulse

A debt collection company had a high-cost, low-accuracy solution to predict/prioritize which accounts/debtors they need to call from thousands of accounts, leading to wasted collection efforts on low-likelihood accounts.

Built a self-learning ML ensemble (XGBoost, LightGBM, RandomForest, CatBoost) trained on years of the client's historical account outcomes and demographic data, so scores reflect how similar accounts actually behaved.

prediction accuracy
before
62%
after
87%
PythonSQL ServerXGBoostLightGBMCatBoost
Read the case study →
S360-012Healthcare & Revenue Cycle

Monthly & Weekly Invoices Automation

Generating and distributing invoices to 300+ hospital facilities was a massive manual effort: three staff spent five full days each cycle formatting, attaching and emailing ~500 invoices, and clients did not receive them until the 10th of the month.

Automated the full invoice lifecycle: refresh, export, formatting with mapped recipients, subject lines, bodies and attachments.

~500 invoices / month
before
5 days x 3 staff
after
zero touches
Power AutomatePower BISharePointSQL Server
Read the case study →

The wall

The work. All of it.

31 delivered projects. No mockups, no concepts — things that ran in production.

S360-001Cross-Industry

Systemize360.ai

20-agent AI SaaS platform that automates entire business operations

20 AI agents
874 emails auto-triaged
Next.jsTypeScriptPostgreSQL
case study →
S360-004Healthcare & Revenue Cycle

AI Accounts Analysis

Voice/text to SQL querying without exposing raw data

waiting on the data team → seconds
plain-English data queries
PythonOn-Premise LLM (30B)SQL Server
case study →
S360-005Staffing & Recruitment

AI Sales Training Simulator

LLM-powered coaching app inside MS Teams for unlimited sales practice

24/7 AI coaching
Inside MS Teams
Power AppsPower AutomateOpenAI Assistants
case study →
S360-006Sales & Marketing

AI Sales Agent

From hours of manual research to personalized outreach in seconds

Hours → seconds
Hyper-personalized emails
PythonOpenAIWeb Scraping
case study →
S360-007Operations

Virtual PM

AI project manager that auto-joins meetings and answers status questions

Auto-joins meetings
Instant status answers
PythonDeepgramRecall API
case study →
S360-002Cross-Industry

AI Agents Hub

Enterprise-grade multi-agent orchestration platform with 15+ specialized agents

15+ agents
5 business domains
Next.jsTypeScriptPostgreSQL
case study →
S360-013Healthcare & Revenue Cycle

Data Team Task Automations

25+ automated workflows saving 15+ hours per week

manual → 15+ hrs/week saved
25+ workflows automated
Power AutomatePythonSQL Server
case study →
S360-017Staffing & Recruitment

1:1 Meetings Automation

Structured performance reviews with automated scheduling and archiving

Automated cadence
Adaptive cards in Teams
Power AutomateMicrosoft TeamsSharePoint
case study →
+20 more →

How we work

No retainers before results.

The pilot is fixed-price and ends with working software — if it doesn't earn the next phase, we shake hands and you keep the code.

01

Scope call

20 min · free

You describe the process that wastes the most time. We tell you honestly whether AI fixes it.

↳ you can walk away here
02

Paid pilot

2–4 weeks · fixed price

A working slice of the real system, on your data. Ends with software, not slides.

↳ you can walk away here
03

Production build

scoped from the pilot

Hardening, integration, evaluation and deployment — measured against agreed numbers.

04

Handover & support

ongoing, optional

Documentation, training and support. Your team owns it; we stay reachable.

Straight answers

Questions we actually get.

?What does Systemize360 do?

We design and build AI systems for businesses: custom AI agents, workflow automation, machine-learning models and digital transformation of legacy software. We have delivered 31 production systems across healthcare, recruitment, fintech and telecom, and we run a live platform with 20 AI agents in production.

?Who do you work with?

Three kinds of clients: Australian small and mid-size businesses that want AI doing real work, established businesses running on legacy systems that need modernising, and AI startups that need senior engineers who ship. We also embed AI, automation and GTM engineers into existing teams on a weekly or monthly basis.

?How does an engagement start?

With a free 20-minute scope call. If there is a fit, we run a fixed-price pilot of two to four weeks that ends with working software. You can walk away after the call or after the pilot — and you keep the code.

?Can AI run without sending our data to the cloud?

Yes. We have deployed on-premise language models for a US healthcare business so that voice and text queries never left their network. If your data cannot leave the building, we design for that from day one.

?What if AI is not the right fix for our problem?

Then we say so on the scope call. Some processes are better fixed with a spreadsheet, a policy change or ordinary software. Selling you an AI system that will not pay for itself costs us more in reputation than it earns in fees.

?Do you offer staff augmentation?

Yes — embedded AI engineers, automation engineers and GTM engineers, aligned to Australian business hours, engaged weekly or monthly. Senior people only, with founder-level review on every engagement.

Leadership

Founder-led, from scope call to handover.

Mohsin Mahmood

Mohsin Mahmood

Co-founder & CEO
email support@systemize360.au · mohsin.mahmood@systemize360.au
phone +61 470 515 726
address 17 Forwood Street, Monash ACT 2904, Australia
abn 73 876 600 459
promise We reply within one business day, AEST.

Tell us the process that wastes the most time.

We'll tell you in 20 minutes whether AI actually fixes it — and we'll say so if it doesn't.