We diagnose your AI transformation,
then build it
When consulting and delivery are split, strategy stays on paper. STAGE sets the direction through AX consulting — and the same team ships the system through SI.
Adoption was fast. Outcomes weren't.
Most companies have already adopted AI. Few have carried it as far as the P&L. Independent research keeps reaching the same conclusion.
of companies scrapped most of their AI initiatives in 2025 — up from 17% a year earlier.
S&P Global, 2025use generative AI but see no material effect on company-wide earnings. Workflow redesign was the biggest driver of impact.
McKinsey, 2025In the same MIT study, companies that brought in a specialist partner succeeded about 67% of the time — three times the rate of those that built it alone.
MIT, 2025Deciding what to do, and actually making it.
The two tracks can run on their own or back to back. Diagnosis only, or all the way through to a system in production.
AX Strategy Consulting
We decide where AI goes, why, and in what order.
We don't start from the tool. We start from your goals and your work, find where AI turns into an outcome, and set the sequence.
- AI maturity assessment — strategy, data, people, governance
- Use-case discovery and ROI-based prioritization
- Data and infrastructure readiness review
- Governance, security and risk policy
- Enablement and change management
Workflow Automation Design
We remove repetitive work so people can focus on judgment.
We start by questioning what should be automated at all. We observe the work, select candidates, then design, build and land it.
- Process diagnosis through observation and interviews
- Candidate selection by frequency, duration and rule-clarity
- Method design — rule-based, RPA or AI agent
- Build and integration with your existing systems
- Exception handling, monitoring and handover
Four stages, four lenses.
Every engagement moves through diagnosis, design, build and scale — and at each stage we check the same four lenses. Nothing quietly gets skipped.
| Lens \ Stage | 01 Diagnose | 02 Design | 03 Build | 04 Scale |
|---|---|---|---|---|
| Strategy & priority | Surface candidates | Lock priorities | Set success metrics | Company-wide roadmap |
| Workflow | Analyze current process | Design intervention points | Implement the workflow | Port to other teams |
| People & org | Assess capability | Redefine roles | Train real users | Tie to evaluation |
| Data & infra | Review data readiness | Design integration | Connect systems | Quality monitoring |
The work you can hand over.
These are the areas we deal with most often. Anything else goes through the same observation and diagnosis to judge whether it can be automated.
Documents & reporting
Recurring report drafts, meeting notes, contract review support
Customer support
Inquiry triage, first-draft replies, repetitive questions
Data handling
Spreadsheet consolidation, system-to-system transfer, integrity checks
Finance & settlement
Expense evidence review, billing reconciliation, anomaly detection
HR & hiring
Resume screening, onboarding material, internal Q&A
Sales & marketing
Lead triage, follow-up drafts, campaign reporting
Consulting that doesn't end at the report.
Most consulting hands over a deck and leaves. STAGE has built and operated systems for manufacturing, finance and the public sector since 2016 — so what we recommend is what we can build.
Diagnose
We read the field and the data, and define the real problem and the metric.
Design
We design the workflow and the architecture together, and validate the core hypothesis.
Build
The same team develops it and integrates it with your live systems.
Operate
We stabilize, measure impact and hand over a system your team owns.
The handover point between consulting and delivery is where most projects fail. We removed it.
Where does your organization stand?
Five questions is enough to see your current stage and what comes next. Answers aren't stored anywhere.
1. Is your AI goal defined as a concrete business metric — processing time, error rate, revenue?
2. How far has AI been absorbed into the daily work of the people who do the job?
3. Do your people have the skills and the authority to use AI tools?
4. Are the data and systems your AI needs to reference in order?
5. Do you have review, security and quality standards for AI use?
Not sure which work to touch first?
Tell us where you are in one line. A team that has actually built these systems will review it with you.
Start a project