AI PROJECT · COMMERCIAL INTELLIGENCE FRAMEWORK
The Project Behind
MAYA OS
An overview of the framework and steps MAYA OS uses to diagnose business problems before prescribing a fix. For the full experience and how to get started, visit the MAYA OS page directly.
Project status
Evolving Prototype
Business problem
Organisations already hold marketing, sales, and CRM data, but it sits in separate systems measured on metrics that do not agree. Meetings end in opinion rather than an auditable conclusion.
Why the problem matters
When nobody can see which stage is losing revenue, more advertising spend becomes the default answer — and the original problem is scaled up rather than fixed.
Who this is for
Designed for executives, business owners and teams across Marketing, Sales, CRM and Data who need a structured view of business problems before deciding what to address first.
Project purpose
Build a framework that forces diagnosis before prescription, with AI doing analysis and hypothesis generation while a human makes the decision.
Chanikan's role
Designed all five stages, the diagnostic question structure, and the human review gates; tested them against a self-created fictional dataset.
Tools and technology
- A large language model for analysis and drafting
- Google Sheets for the fictional dataset
- Self-designed prompt architecture templates
- A written review log
Workflow overview
Workflow
- Collect & Connect Data — gather what exists and normalise it into one measurement language
- AI Analysis — surface relationships, anomalies, and hypotheses worth testing
- Human Review — test hypotheses against real context and discard the irrelevant ones
- Action Plan — convert conclusions into sequenced priorities, owners, and measures
- Review Rhythm — set the cadence that keeps decisions continuous
Expected output
- A diagnosis report naming the stage where revenue leaks
- A hypothesis set ranked by likely impact
- A sequenced priority plan with named owners
Example application
Illustrative workflow example
A fictional revenue-leak diagnosis example
Input
- Fictional marketing and sales data
- Metrics from multiple systems
- Team meeting notes
AI-assisted process
- Normalise data into one measurement language
- Surface relationships and anomalies
- Hypothesise which stage is leaking revenue
Human Review
- Test hypotheses against real context
- Discard the irrelevant ones
- Record the reasoning for later audit
Output
- A sequenced priority plan with named owners and measures
Review by the accountable owner
AI never closes the loop on its own. Every hypothesis passes through the stage-three human review, where the reviewer must record why each one was accepted or rejected so the reasoning can be audited later.
Responsible AI and data boundaries
- Examples use public, sample, or synthetic data
- Customer data, personal data, or confidential data should never be entered into a public demo
- AI output must be reviewed before it is used
- This project demonstrates an approach — it is not a fully autonomous enterprise system
Current limitations
- No paying client deployments
- Tested only against fictional datasets; not validated at scale
- No automated integration with CRM or advertising platforms
- Output quality depends entirely on input data quality
Next development stage
Turn the diagnosis steps into reusable templates and test the framework in a small-group trial round using fictional data.
Chanikan's methodology
Chanikan Kanchanasalee
AI-Native Business Strategist specialising in Commercial Growth
Experienced in leading cross-functional work across Commercial Growth, Marketing, Sales, CRM, Data and Operations.