Three Platforms. One Architecture.

Wit™ discovers. Daw™ supports. Malee™ connects. All three built in Si Racha, Thailand.

WizEmp constellation diagram: three proprietary AI platforms arranged around the central methodology. Wit (วิท) at top: Discovery. At right: the governed build of the data architecture. Daw (ดาว) at bottom: Support 24/7. Malee (มาลี) at left: First Contact. All developed in Si Racha, Thailand, Eastern Economic Corridor. — Wit (วิท) discovers. Daw (ดาว) supports. Malee (มาลี) connects. Three platforms around one governed build, developed in Si Racha, Thailand.

Platform Architecture

Three platforms. One connected architecture.

Malee, Wit, and Daw form a continuous chain from first contact through discovery and delivery to ongoing support. Between discovery and support, the WizEmp team builds the governed data architecture that every report draws from.

Malee

First Contact

→

Wit

Discovery

→

Build

Governed Delivery

→

Daw

Support

Malee also runs first-line business operations · discovery, support, and BD across Thai, English, and Japanese

Wit

วิท

WizEmp's smart discovery process powered by AI

Structured anonymous stakeholder interviews across all personas simultaneously
Async voice-first, bilingual display (detected language and English together)
Produces the Shield of Truth, Data Dictionary, and Metrics Dictionary
Structured alignment process for genuine agreement, not forced consensus

Wit runs the discovery process that every WizEmp engagement begins with. Structured interviews, conducted anonymously and asynchronously, surface what traditional workshops miss. Participants speak in their own language, on their own time, from any device. The output is a verified requirements document that the entire project builds on. Developed and maintained by the WizEmp team in Si Racha.

Read the full Wit™ page →

Daw

ดาว

WizEmp's 24/7 first-line Power BI support agent

Available from MVP delivery. Support starts at UAT, not after go-live
Context-aware: understands your project's requirements, data model, and known risks
Handles first-line questions autonomously; complex issues escalate with full context to a WizEmp expert
Multi-channel: text and voice

Daw activates when the first users see the reports. Daw activates at MVP, during UAT. Not after go-live, not after training ends. Daw understands the project context: what was agreed during discovery, how the data model works, and where the known risks sit. When a user asks "how do I filter this by region," Daw answers with context specific to that workspace. When something is beyond first-line scope, the escalation to a WizEmp expert includes the full diagnostic picture.

Malee

มาลี

WizEmp's multilingual AI assistant

First point of contact: email, LINE, WhatsApp, phone
Thai, English, and Japanese, switching automatically
Lead qualification, follow-up sequences, discovery call scheduling
Transfers Power BI technical questions to Daw immediately

Malee handles the business side. Inbound enquiries, prospect qualification, calendar coordination, follow-up sequences, all running continuously across Thai, English, and Japanese, switching based on the contact's preference. When someone asks a Power BI question, Malee routes it to Daw. When someone wants to discuss a project, Malee qualifies the conversation and schedules the meeting. The boundary is clean: Malee handles business operations, Daw handles technical support.

Discovery Without Dependency

The methodology works independently of the BI platform.

Wit's structured elicitation process produces a Shield of Truth, a Data Dictionary, a Metrics Dictionary, and a Breeze Blueprint. These are verified business requirements, not wireframes tied to a specific vendor. The output is a precise description of what the organisation needs, agreed upon by the people who will use it.

Some organisations will want WizEmp to build the full implementation in Microsoft® Power BI®. Others already have their own BI developers working in Qlik, Tableau, Looker, or another platform. They need the requirements process, the structured discovery that prevents the build from answering the wrong question, and they will handle the implementation themselves.

That is by design. Good requirements do not belong to a vendor. The Shield of Truth™ is platform-agnostic because the decisions it captures are about the business, not about the technology. An organisation that runs Wit™'s discovery and then builds in Tableau has requirements that were verified the same way, with the same rigour, as one that builds in Microsoft® Power BI®.

Engineering Principles

What the platforms are built on.

Human-in-the-Loop

Verification is the gate. Wit synthesises interview data, but the facilitator decides what it means. Daw answers first-line questions, and unresolvable issues go to a human with full context. The AI is a working partner, never an autonomous decision-maker.

Data Sovereignty

Sensitive information never leaves your infrastructure. On-premise AI processing keeps your data where you say it lives. PDPA compliance is engineered into the architecture, not retrofitted as a policy document. When a Thai government entity asks where data lives, the answer is specific.

Continuous Evolution

Every engagement is designed to make the platforms smarter. Problems discovered become patterns Wit recognises faster. Support edge cases become knowledge Daw draws on. The technology never freezes. Twenty-five years of BI work, including the failures, are encoded into how these platforms operate.

Thai Engineering

Built locally, in Si Racha. The AI agents, workflows, voice synthesis, automation architecture: all developed and maintained where it was created. As WizEmp trains its Thai engineers, they will contribute to the platform every engagement runs on.

These principles grew from specific intellectual foundations: constraint theory, systems thinking, lean operations, and structured consensus methods, that are part of what the Thai team learns on every engagement. Read more on the Thai Talent page.

Technology Principles

What the infrastructure is built for.

The design priorities behind the three platforms.

Data Sovereignty

Processing stays where the data lives. On-premise AI capabilities ensure that conversation handling, intent classification, and synthesis happen without data leaving the client's jurisdiction.

Voice-First Interaction

Thai and English natural voice for support and business operations. Stakeholders speak; the platform listens, transcribes, and responds in the same language.

Power BI Native Integration

Direct integration with Power BI service for workspace management, refresh orchestration, and semantic model operations.

Self-Managed Infrastructure

Dedicated infrastructure, fully managed by the WizEmp team. No third-party platform lock-in. Full control over uptime, security, and deployment cycles.

Workflow Automation

All platform automation runs through governed orchestration managing data flows, agent routing, and integration logic. The coordination layer is proprietary.

Multi-Channel Communication

Inbound and outbound across text, voice, and phone. Prospect conversations, support interactions, and scheduling handled across channels and languages.

Naming Convention

Thai names. Thai meanings.

Every WizEmp agent follows the same naming pattern. Thai origin. Single syllable where possible. A meaning that fits the role. The names are not branding exercises: they reflect where the technology comes from and the language of the team that builds it.

Agent	Thai	Meaning	Role
Wit	วิท	Knowledge	Discovery and elicitation
Daw	ดาว	Star	24/7 client support
Malee	มาลี	Flower	Business operations and communications

This is technology built for the specific problems of ASEAN enterprise BI. Not generic SaaS repackaged for a regional market. Not an international vendor's platform with a Thai reseller sticker on it. Three platforms developed in Thailand, encoding over twenty-five years of lessons from the kind of failures that most consultancies prefer not to talk about.