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We offer two primary models. Project-based engagements have a fixed scope, timeline, and deliverable set — ideal for building a new dashboard, data pipeline, or AI application. Monthly retainers provide ongoing support for data operations, analytics maintenance, model monitoring, or embedded advisory. Most new clients start with a 2–4 week paid Discovery Sprint (requirement interviews, architecture design, and a formal spec) before committing to full implementation. This keeps scope tight and prevents cost overruns on both sides.

Timelines vary by scope, but here are reliable benchmarks: BI dashboards and reporting suites run 4–8 weeks from requirements sign-off to production handover. Data engineering projects (pipelines, lakehouses, streaming) typically take 8–12 weeks. AI and LLM applications — RAG chatbots, multi-agent systems, recommendation engines — usually require 8–16 weeks depending on integration complexity. Every engagement begins with a Requirements Specification document that locks scope and sets a milestone-by-milestone delivery schedule, so both sides always know exactly where we are and what comes next.

Yes, always. We sign a mutual NDA before any discovery conversation that involves proprietary data, business logic, or competitive context — before you share a single spreadsheet. For projects that handle personal data (customer records, transaction logs, health data), we also execute a Data Processing Agreement (DPA) compliant with applicable privacy regulations. We treat client data with the same rigor we'd expect if the roles were reversed: minimum access, encrypted transfer, no data retained beyond the engagement unless explicitly agreed.

Yes — and it's often the most effective model. We can operate in three modes depending on your situation. Embedded: we join your team's sprint cadence, attend standups, and work directly in your repos and cloud environments. Independent delivery: we work autonomously with defined review gates and handoff checkpoints, minimizing the overhead on your team. Paired upskilling: we build alongside your engineers or analysts, transferring knowledge through code reviews, documentation, and workshops so your team owns the system after we're gone. Most engagements blend all three at different phases.

Focused analytics work (a dashboard or reporting layer) typically starts around NT$300K–500K. Mid-scale data engineering or ML projects run NT$600K–1.2M. Full-stack builds — data infrastructure plus an AI application layer plus training — can reach NT$1.5M–3M+. We always provide a fixed-price proposal after the Discovery Sprint, so there are no open-ended hourly billings or surprise overruns. If budget is a constraint, we can also scope a phased delivery where Phase 1 delivers standalone business value and later phases are optional extensions.

We've delivered production systems across 10+ industries: finance and investment management, digital advertising and performance marketing, supply chain and retail, online gaming and gambling, enterprise SaaS, healthcare operations, and manufacturing. Cross-industry experience is a genuine advantage — patterns that are standard in one domain (real-time event streaming from fintech, probabilistic simulation from gaming, RAG retrieval from SaaS) are often novel and high-impact when applied elsewhere. We don't start from scratch in a new domain; we adapt battle-tested solutions.

Every engagement ends with a structured handover: documented architecture, runbooks, a performance baseline report, and a live walkthrough with your team. For the 30 days following delivery, we provide complimentary bug-fix support for any issues directly attributable to our implementation. Beyond that, clients can move to a monthly support retainer for ongoing monitoring, model retraining, or incremental feature work. We also offer a quarterly health-check service for data infrastructure — particularly useful for teams that want expert oversight without a full-time data hire.