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Easy AI has built Revenue AI OS, an AI operating system that consolidates marketing, sales and after‑sales data on a single platform to support revenue growth, customer outreach and performance measurement. Mr. Kha Phan, Easy AI's co‑founder and CTO, said Revenue AI OS aggregates interaction histories so teams can see where customers click, what they view and how they behave, and that the system reduces response times from roughly 30 minutes–one hour to under 10 seconds while automatically handling 97% of conversations. Mr. Kha Phan cited Easy AI's internal performance logs and a case study with the Pico retail chain as the basis for those figures, which Báo Tuổi Trẻ reported, as first reported by Vietnam.vn.
People often think that applying AI is easy, but it's usually not that simple to make it actually work.
Mr. Kha Phan, CTO and co‑founder of Easy AI
Key takeaways
- Automation rate: Easy AI reports Revenue AI OS can automatically handle 97% of conversations in deployed cases.
- Customer satisfaction: Easy AI reports a satisfied or neutral rating from 94% of customers who interacted with the system.
- Response times: The company says response times that once averaged about 30 minutes to an hour can drop to under 10 seconds with the system in place.
- Deployment speed: After standardising its process, Easy AI aims to shorten deployment from 3–6 months to under two weeks.
- Evaluation window: Easy AI plans to evaluate a deployment's effectiveness approximately three months after implementation.
Table of contents
- Key takeaways
- How Revenue AI OS connects customer data across teams
- Automation in practice: the Pico deployment and performance claims
- Deployment, training and the technology trade‑offs
- Market focus, the Youth Startup Award and next steps
- How Revenue AI OS could help — and where it may stall
- What to be careful about
- Frequently asked questions
How Revenue AI OS connects customer data across teams
Easy AI developed Revenue AI OS to tackle fragmented customer records. Mr. Kha Phan described how the same customer's ad exposure, sales consultation notes and after‑sales logs typically sit in separate software, and the platform brings those touchpoints together so teams can see a single interaction history.
The system ingests clicks, page views and message threads and applies models that score behaviour and demand. That single view lets marketing, sales and service follow a customer's path and route conversations to the right staff member when human follow‑up is needed.
Easy AI says this gives businesses a measurable connection between customer data and revenue operations, letting them reduce repetitive tasks and free consultants to handle higher‑value interactions.
Automation in practice: the Pico deployment and performance claims
Easy AI cited a case study with the Pico electronics retail chain to demonstrate Revenue AI OS under real‑world load. According to Easy AI's deployment logs for Pico, the rollout absorbed very high request traffic during peak sales windows and the system was able to automatically handle 97% of conversations.
The company also reports that 94% of customers rated interactions as satisfied or neutral after the AI handled their requests. Easy AI presents those metrics as evidence that online consulting workload can be substantially reduced while preserving customer experience.
Mr. Kha framed automation as selective: the system escalates when it detects dissatisfaction or ambiguous inputs so staff intervene for complex or sensitive cases.
Deployment, training and the technology trade‑offs
Mr. Kha Phan said Easy AI pairs large pre‑trained models with smaller, task‑specific models to preserve response quality while keeping costs down. He added that Easy AI has developed a configuration layer customers can use to tune model usage and trade off latency, accuracy and expense.
Training the system is compared to onboarding a new employee: baseline knowledge is not enough and the platform needs guidance on each business's procedures. Easy AI acknowledges AI still gives incorrect answers when underlying data, such as a changed price list, is not updated.
Operationally, deployment used to take 3–6 months; after standardising workflows Easy AI aims to get Revenue AI OS up and running in under two weeks, with an effectiveness review approximately three months after launch.
Market focus, the Youth Startup Award and next steps
Easy AI is focusing Revenue AI OS on four industry groups: retail, healthcare, accommodation and spas, with electronics and home appliances singled out as a heavy user group. The company also uses the platform internally to spot operational issues and refine the product.
The team presented Revenue AI OS at InnoEx events and took part in the 7th Tuoi Tre Startup Award 2026, a programme run with the Ho Chi Minh City Youth Union and InnoEx. The awards accept applications through 15 October 2026 and have an awards ceremony scheduled in October 2026.
Easy AI told reporters it plans to continue product development in the four sectors and to expand into international markets by 2027.
| Metric | Before | After (Revenue AI OS) |
|---|---|---|
| Average online response time | About 30 minutes–1 hour | Under 10 seconds |
| Conversations handled automatically | Not automated at scale | 97% handled automatically |
| Customer satisfaction (rated) | Not provided | 94% satisfied or neutral |
| Typical deployment time | 3–6 months | Under two weeks (target) |
How Revenue AI OS could help — and where it may stall
The case for
- Faster responses and high automation rates could reduce online consulting headcount and operating costs, freeing staff for complex sales.
- A single customer view across marketing, sales and service should help measure conversion and attribute revenue to interactions.
- Standardised deployment under two weeks could speed adoption for multi‑store retailers facing scaling costs.
The case against
- Performance claims rely on company data from pilot deployments; errors occur when backend data such as pricing is out of date.
- Integration requires changes to employee habits and processes, a barrier that Easy AI acknowledges and says takes work to overcome.
- Balancing response quality and cost with off‑the‑shelf versus custom models may limit results for specialised or highly regulated sectors.
What to be careful about
- Reported metrics come from Easy AI's own deployments; independent validation of the 97% automation and 94% satisfaction figures is not provided.
- The solution depends on up‑to‑date business data; stale or inconsistent data (for example, a recent pricing change) can produce incorrect answers.
- Successful rollout requires staff training and process redesign; resistance or poor adoption could blunt expected time‑savings.
The bottom line
Easy AI positions Revenue AI OS as a bridge between customer data silos and revenue operations: the company claims large gains in speed and automation while acknowledging the work required to integrate the system into daily processes. Its headline figures — handling 97% of chats, 94% satisfied or neutral ratings, response times under 10 seconds and a push to shorten rollout to under two weeks — are anchored in company reporting and a Pico case study. Prospective customers and partners should seek independent validation of those metrics, confirm data‑update processes to avoid stale answers, and factor staff training into any deployment plan.
What to watch
- Watch for the Youth Startup Award application deadline on 15 October 2026, when the programme closes to entries.
- Watch for the Youth Startup Award ceremony scheduled in October 2026; no exact date has been given in the reporting.
- Watch for Easy AI's announced push into international markets in 2027, which the company says is a strategic target.
Frequently asked questions
What is Revenue AI OS and who built it?
Revenue AI OS is an AI operating system from Easy AI that connects marketing, sales and after‑sales data into a single interaction history. Mr. Kha Phan, Easy AI's CTO and co‑founder, described the product at InnoEx and in reporting by Báo Tuổi Trẻ.
How much customer interaction can the system automate?
Easy AI reports that Revenue AI OS can automatically handle 97% of conversations in its cited Pico deployment and that 94% of customers rated those interactions satisfied or neutral.
How long does deployment and evaluation take?
Easy AI says deployments previously took 3–6 months but the company now aims for under two weeks to get Revenue AI OS running and plans to evaluate effectiveness approximately three months after implementation.
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