Navos connects five specialized AI agents across a continuous workflow: market insight, creative generation, campaign delivery and performance optimization. The system turns fragmented marketing tasks into a coordinated operating workflow.
Global growth execution has traditionally been a coordination problem, especially when campaigns span multiple markets, languages, platforms and creative formats.
A campaign may begin with market research, move through strategic planning, creative production and localization, enter media execution, and then feed performance data back into analysis and optimization. In conventional processes, these stages are often handled by different teams, systems or external partners.
The coordination challenge grows as companies expand across countries because audience behavior, platform rules, creative requirements and performance benchmarks vary by market.
Multi-agent AI systems are designed to address that fragmentation by assigning specialized agents to different parts of a workflow and coordinating them around a shared business objective.
Founded in 2017, Tec-Do is a global leading AI company for business growth, delivering results-centric marketing solutions for global business growth. Powered by Tec-Chi multimodal large language models (MLLMs) and the Marketing Multi-Agent Platform Navos, Tec-Do delivers end-to-end marketing solutions across global media channels.
Navos: five agents in one coordinated growth workflow
From isolated tools to coordinated agents
A conventional software tool typically waits for a user to select a function and provide instructions; the interaction often ends when that task is completed.
An AI agent is organized around a goal. It can interpret an objective, break work into steps, use relevant tools and pass information to another specialized agent.
A multi-agent system extends that model by assigning different responsibilities to different agents while keeping them connected through shared context and workflow logic.
In Tec-Do’s framework, the five agents work as a connected growth workflow. AI Opportunity Scout analyzes global signals to identify high-potential opportunities. AI Content Creator develops content designed for engagement and conversion, while AI Creator Connector matches brands with appropriate creators at scale. AI Storyteller turns ideas into localized narratives, and AI Workspace Assistant makes task coordination and execution more actionable. Together, these agents move work from market insight to creative generation, campaign delivery and performance optimization, with performance signals informing subsequent decisions.
The operating value comes from the connection between these functions. A creative asset is no longer produced independently of media performance. A market insight does not remain in a presentation. Campaign data becomes an input for subsequent planning and production.
Why can this improve delivery efficiency
A coordinated multi-agent model can change the operating structure of a global growth organization in four practical ways.
First, it reduces repeated manual work. Campaign structures, reporting formats, creative adaptations and diagnostic rules can be reused rather than rebuilt for every market.
Second, it makes accumulated experience easier to apply. Performance patterns and operating knowledge can be embedded in workflows instead of remaining with individual employees.
Third, it shortens the feedback loop. Creative, media and analytics teams do not need to wait for a long sequence of manual handoffs before acting on new performance data.
Fourth, it makes cross-market execution more consistent. Shared rules and interfaces can be applied while still allowing local language, cultural and platform requirements to be incorporated.
These effects can allow a company to support more campaigns without requiring every category of operating resource to grow at exactly the same rate.
The link to reported margins needs careful wording
It is tempting to say that AI automation directly creates a high gross margin. That conclusion would be too simple.
For Tec-Do, the most direct explanation of its reported gross margin remains revenue recognition. According to publicly available information, revenue from its core technology-enabled solutions is primarily based on transaction-related service fees rather than on the full media spending involved in supported campaigns.
The second major factor is business mix. Core technology-enabled solutions account for most of the revenue shown in publicly available information and have a different margin profile from customized influencer marketing.
Automation and multi-agent delivery form a third layer. They help explain how technology-enabled solution delivery can be standardized and scaled, but Tec-Do does not separately quantify the exact number of margin points attributable to Navos or to any individual automation feature.
The most supportable summary is:
Tec-Do’s reported gross margin is primarily shaped by service-fee revenue recognition and solution mix, while standardized and increasingly automated delivery supports operating scalability.
Net profit requires an additional bridge
The operating model does not fully explain net profit margin. Other income, finance costs and tax treatment must be considered separately from core operating delivery.
For example, net profit was also affected by other income, including bank interest, investment-related gains or losses, government grants and foreign-exchange movements. It also indicates that preferential enterprise income tax treatment also reduced tax expense in certain periods. Some of those benefits were temporary or subject to qualification, and part of the company’s earlier tax treatment began to phase out.
For that reason, a durable assessment should separate:
• gross profit from core customer solutions;
• operating expenses, including research and development;
• other income and finance costs;
• current and deferred tax;
• net profit.
The broader implication
Multi-agent AI is not simply a new interface for existing software. It changes how work is divided, how information moves between functions and how operating experience is retained.
For companies operating across markets, the potential value lies in converting a fragmented collection of tasks into a repeatable operating system.
Tec-Do’s financial profile should still be read through publicly available information on revenue recognition, business mix, operating expenses, other income and tax. Multi-agent systems help explain the operating logic behind Tec-Do’s positioning as a global leading AI company for business growth: technology, business data and platform connectivity are used to coordinate a larger share of real-world growth workflows, turning AI into an operating layer for measurable business execution.
About Tec-Do
Founded in 2017, Tec-Do is a global leading AI company for business growth, delivering results-centric marketing solutions for global business growth. Powered by Tec-Chi multimodal large language models (MLLMs) and the Marketing Multi-Agent Platform Navos, the company delivers end-to-end marketing solutions through a suite of AI-native, performance-driven products.
These products restructure and autonomize mission-critical marketing processes, including market intelligence, content generation, campaign delivery and performance optimization, across global media channels. In 2025, Tec-Do served over 100,000 advertisers across e-commerce, gaming, entertainment and local commerce.
