AI systems running in the real world.

Quoting depends on one expert’s experience — slow, inconsistent, impossible to scale.

A quoting agent that runs from requirements to price: it parses customer requirements, drafts the BOM, combines supply-chain and historical data into a suggested price — then a human reviews and sends it.

1. Requirement parsing
2. BOM draft generation
3. Supply chain & history data
4. AI suggested price
5. Human review

Expert-level quotes in hours — generated by agents, approved by humans.

Sourcing raw materials locally in Indonesia costs more than it should.

A global sourcing agent for procurement teams: it searches suppliers worldwide, matches materials, compares prices and MOQs, calculates logistics costs, and outputs risk analysis with recommendations.

1. Global supplier search
2. Material matching
3. Price & MOQ analysis
4. Logistics cost analysis
5. Risk analysis & recommendation

Procurement decisions backed by global data — not just local quotes.

Content output is capped by headcount, and quality varies with every operator.

A content pipeline covering collection, AI generation, human approval, multi-account publishing and data feedback — upgrading manual output into a system that keeps improving itself.

1. Material collection
2. AI generation
3. Human approval
4. Multi-account publishing
5. Data feedback loop

From manual production to a self-improving content pipeline.

AI that matters to families can’t live only inside an app.

Software and hardware as one product: voice interaction and multi-model dialogue, combined with educational content generation, cry detection, sleep sensing and in-house sensor hardware.

1. AI companion robot
2. Voice interaction · multi-model
3. Educational content generation
4. Cry detection & sleep sensing
5. Smart sensor hardware

AI beyond the screen — in toys, bedrooms and daily routines.