TaskiLi Wants to Turn an AI Marketplace Into an Execution Engine

As AI moves from recommending what to do toward coordinating how work gets done, TaskiLi is building around a larger question: can a service marketplace become infrastructure that connects people, AI and software to an outcome?

Traditional marketplaces solved discovery. A customer searched for a provider, compared profiles and hired someone. AI is now entering that workflow itself. Upwork, for example, allows AI tools to access its marketplace through an MCP server, enabling agents to create job posts, identify talent and prepare offers. The company has also integrated hiring into AI workflows such as ChatGPT. The marketplace is becoming less of a destination and more of a layer inside the workflow. TaskiLi is positioning itself around that next step.

TaskiLi’s current platform says it covers more than 4,100 service categories and 200 million taskers, with Texa AI supervising hiring. The company says the platform is live on Android and web, with iOS coming, and that beta testers are already booking tasks. These figures are company claims rather than independently verified marketplace measurements.

The more consequential question is what happens after the match. LINIS, the parent company and venture builder behind TaskiLi, describes the platform as an AI-powered system for managing complex workflows rather than simply automating individual tasks. Its roadmap points toward infrastructure spanning inputs, systems, governance, memory, telemetry, economics and learning. That changes the unit of value from a “service provider” to an “execution workflow.”

The Marketplace Is Becoming an AI Interface

This shift is visible across the broader freelance economy. Upwork’s 2026 research found that AI-augmented professional services grew 72% year over year, with earnings increasing 22%, while more complex AI-assisted work saw earnings rise 45%. Meanwhile, generative AI and creative production contract starts grew 90%, while per-contract earnings fell 13%.

As AI makes basic production cheaper, value increasingly moves toward people and systems that can apply judgment, integrate tools and deliver outcomes. Upwork’s more recent marketplace data reinforces this pattern. Jobs requiring people to improve AI-generated work have increased 70% year over year, while such postings in software development have grown more than eightfold since 2023. AI can produce a first draft, but businesses still need people to evaluate, refine and operationalize the result. For marketplaces, the question is therefore no longer only who can find talent fastest. It is who can coordinate the chain from request to reliable result.

A real-world task rarely ends when someone is hired. It involves defining the requirement, establishing terms, exchanging information, executing the work, handling exceptions, validating the output, processing payment and learning from the outcome.

This is where TaskiLi’s roadmap becomes significant. Its planned infrastructure around governance, memory, telemetry and learning points toward a system designed to retain operational context rather than treating every transaction as an isolated marketplace interaction.

That ambition also creates a harder trust problem. As AI agents gain the ability to initiate transactions and hire people, marketplaces need stronger controls around identity, authorization, quality and accountability. A 2026 study of 303 bounties on RentAHuman found that 32.7% originated through programmatic channels such as APIs or MCP, and identified misuse involving credential fraud, impersonation, reconnaissance and other abuses. The finding does not mean agentic marketplaces are inherently unsafe, but it illustrates why supervision cannot be secondary.

TaskiLi’s emphasis on Texa AI supervision therefore points toward a critical layer of an execution platform: determining not just who can do a task, but whether the work was done appropriately.

From Product Launch to Commercial Validation

While conversing with AsiaTechDaily, Radhouane Alaadeen K, Founder and CEO of TaskiLi and LINIS, outlined a phased roadmap for turning the platform into a commercial system:

“For TaskiLi, the next 12 months are mainly about turning what we have already built into a complete commercial platform and proving the business model. In the first 3 months, the priorities are AI Fabric, client / tasker chat, structured gigs and agreements, payments, the 15% TaskiLi transaction fee, AI Outputs, launch, and commercial validation. From months 4 to 9, we will build deeper platform infrastructure around Inputs, Systems, Governance, Memory, Telemetry, Economics and Learning. From months 10 to 12, the focus moves toward deeper AI Supervision, expansion of Texa AI Lab, human + AI workflows, stronger orchestration, and scaling the platform. Over 12 to 24 months, that foundation allows us to expand the service network, enter additional markets, support larger and more complex projects, and develop business and enterprise use cases. LINIS has a broader role. It is the parent company and venture builder behind TaskiLi, with TaskiLi as its flagship venture. The longer term LINIS strategy is to build and scale additional technology ventures alongside TaskiLi. So I see the next phase as: Build the foundation -> launch -> prove commercial demand -> improve the execution engine -> scale TaskiLi -> build the next LINIS ventures.”

The sequence is revealing. TaskiLi is not describing scale as the immediate objective. It first wants to establish the transaction layer, workflow infrastructure and commercial model, then use operational data and AI capabilities to expand.

The Bigger Opportunity: Owning the Outcome

TaskiLi is entering a market where marketplaces, AI agents and workflow software are increasingly converging. Agent-native platforms such as Taskin are building interfaces through which AI workflows can request bounded human work and receive structured results. Established marketplaces such as Upwork are exposing talent networks directly to AI tools. TaskiLi’s differentiation will depend less on having a large directory of taskers and more on whether it can make fragmented capabilities work together reliably.

That is a harder product to build. It requires marketplace liquidity, payments, structured agreements, AI matching, supervision, governance and a feedback loop that improves execution. If TaskiLi can deliver on its roadmap, its marketplace would not simply help users find someone to perform a task. It would aim to become the layer that determines what needs to be done, who or what should do it, how the work is coordinated and whether the outcome is ready. That is the larger transition underway in AI marketplaces: from finding the right capability to orchestrating the work itself.

Quick Takeaways

• TaskiLi is moving beyond AI-powered matching toward becoming an execution layer that coordinates people, AI and software.
• The marketplace model is changing: AI is increasingly becoming the interface through which users discover talent, initiate work and manage workflows.
• Execution is the bigger challenge: Matching a user with a tasker is only the beginning. Defining requirements, agreements, payments, supervision, quality control and learning are equally important.
• Human + AI collaboration is gaining value: As AI makes basic tasks cheaper, complex work requiring judgment, domain expertise and orchestration can become more valuable.
• Trust and supervision will be critical: AI-driven marketplaces need mechanisms for verification, governance, identity, authorization and accountability.
• TaskiLi’s roadmap is deliberately phased: The company plans to establish its commercial and transaction infrastructure first, then build deeper systems around governance, memory, telemetry, learning and AI supervision.
• The 12-to-24-month ambition goes beyond the marketplace: TaskiLi plans to expand its service network, enter additional markets, support larger projects and develop business and enterprise use cases.
• The larger opportunity is outcome ownership: The potential shift is from marketplaces helping users find someone to platforms helping users get something done.