AI That Cuts Opex, Not Another Transformation Program
Automate the work that slows your company down
Explore Where AI Delivers ROI
AI That Targets Costs - Not Hype
Most companies experimenting with AI are asking the wrong question.
They ask: “How can we use AI?”
The better question is:
“Which operational cost should we remove?”
At Gradion, we build AI systems that automate real operational processes — the work currently consuming time, headcount, and budget inside your organization.
Across our live deployments, Gradion-built AI systems process more than 20 million operational tasks every month.
- Invoice processing
- Candidate screening
- Customer support workflows
- Supply chain operations.
These are not AI experiments.
They are production systems delivering measurable cost reduction.
If It Doesn't Save Money or Make Money, Don't Build It
Over the last two years, companies were sold large AI transformation programs:
• Six-month timelines
• Seven-figure budgets
• Endless strategy presentations about becoming an “AI-first organisation”
Most produced very little.
The lesson many organisations learned the hard way is simple:
AI only creates value when it is pointed at a specific operational process with a measurable outcome.
- Not innovation labs
- Not transformation theatre
- Not strategy decks
A clearly defined process.
A measurable result.
That is the only kind of AI work Gradion does.
What This Looks Like In Practice
Gradion builds AI agents that automate operational workflows already running inside your business.
Invoice & Document Processing
A manual team processes roughly 100 invoices per day.
An AI agent processes 10,000+ with higher consistency and a complete audit trail.
Customer Support Operations
Automated classification, resolution, and escalation.
Current live project targeting 80% reduction in handling cost.
Candidate & CV Screening
Manual review: ~300 CVs per recruiter per day
AI agent: in just minutes 700+ candidates evaluated using consistent criteria.
Supply Chain & Vendor Operations
Purchase order matching, invoice reconciliation, vendor workflows.
High-volume processes where manual errors and operational cost compound quickly.
The business case is simple:
Revenue scales.
Operational cost per transaction should not.
Every automated workflow widens that margin.
Proof in Production
Gradion systems currently process 20+ million operational tasks every month.
These are production workloads running continuously inside client systems.
Examples
Shopware
21-engineer AI product team embedded into the organization
~40% reduction in product development cost
procelo tosca
AI system enabling natural language queries across complex ERP databases
80%+ SQL accuracy within 8 weeks
Additional client references available under NDA.
How Engagements Start
Every engagement begins with identifying the operational bottleneck where AI creates the fastest ROI.
Your situation determines the entry point.
Situation | Where to Start | Typical Timeframe |
|---|---|---|
"We know AI matters but don't know where to begin" | AI Strategy & Readiness | 1–2 weeks |
"We have an idea but need to test feasibility" | Company Design Sprint | 2–4 weeks |
"We know exactly which process to automate" | Agentic AI Pilot | 4–8 weeks |
"We need a production GenAI application" | Generative AI Application Build | 6–12 weeks |
"We have models but can't run them reliably" | MLOps & AI Engineering | 4–12 weeks |
Before any system is built, Gradion performs a data readiness assessment.
AI built on fragmented data fails quickly.
We fix that first when necessary.
Global Engineering That Delivers
Gradion combines senior technical leadership with global engineering capacity.
- 320 engineers across Vietnam, Thailand, Egypt, Germany, and Singapore
- 1,000+ production systems delivered
- $10B+ GMV flowing through systems we built or maintain
- ISO 27001 certified processes
Our Germany–Vietnam delivery model creates a natural follow-the-sun engineering cycle, allowing continuous progress without 24/7 burnout.
This is how we move from idea to production AI systems quickly and reliably.
The Bottom Line
AI should not be a technology experiment.
It should be an operational improvement with measurable financial impact.
If an AI system doesn't reduce cost or generate revenue, it shouldn't be built.
Gradion focuses exclusively on the AI systems that do.
Start With the Process, Not the Technology
Tell us the operational workflow you want to improve. We will tell you whether AI can actually deliver measurable results, and how quickly.