The update reflects an important change in how Collty approaches collaboration. Instead of treating a project as a list of tasks assigned to individual users, the platform now helps organizations design the complete working structure around a project: define what needs to be done, determine which teams and roles are required, combine internal and external resources, add AI agents, and coordinate delivery within one environment.
A New Generation of the Collty AI Engine
The latest version introduces an expanded AI engine that supports project creation, team assembly, role definition, task planning, and project analysis. The engine is being developed to work not only with isolated profile keywords, but with a broader combination of professional, project, and collaboration data available within Collty. When analyzing a project, the AI can consider its objectives, industry, expected deliverables, required capabilities, duration, complexity, and preferred team structure. It can then help identify the necessary areas of expertise, define relevant roles, recommend specialists, and suggest how different people, teams, and AI agents could be combined. This moves the platform beyond conventional profile matching. Collty is being designed to evaluate not only whether an individual specialist appears relevant, but also whether the proposed combination of participants can collectively cover the needs of a project. As more real work is managed through the platform, the engine will increasingly be able to use project history, team membership, professional relationships, assigned roles, and collaboration patterns as additional signals. The updated engine also provides the foundation for future predictive tools. These will help users identify missing expertise, unrealistic team structures, potential workload problems, dependencies between teams, and other risks before they affect project delivery.
Creating Teams Manually or with AI
Users can now create teams directly from specialist profiles. A team can be assembled manually when the founder or manager already knows which people should participate, or with AI assistance when the required combination of specialists is not yet clear. In the manual scenario, users can review relevant profiles, select specialists, define their roles, and invite them to join the team. This makes it possible to recreate an existing professional group inside Collty or build a new team from trusted collaborators. In the AI-assisted scenario, the user describes the purpose of the team, the type of project, the expertise required, and any important constraints. Collty then analyzes the available information and proposes a possible team composition. The recommendation can be reviewed and adjusted before invitations are sent, so the final decision always remains with the user. This approach is intended to reduce the time spent searching for people individually and comparing disconnected profiles. The platform helps users think in terms of the capabilities of the whole team rather than evaluating each specialist in isolation.
Permanent Teams and Teams Created for a Specific Project
The new version distinguishes between permanent teams and temporary teams assembled around a particular project. This gives users more flexibility in organizing professional relationships and reflects the way agencies, consulting groups, startups, and distributed specialists already work. A permanent team can continue operating across multiple projects. It may represent an agency, a consulting practice, a development group, a marketing team, a design studio, or another established professional unit. The team maintains its own structure and can be invited to participate in new projects without being assembled again each time. A project-specific team is created for a defined objective and period. It can bring together specialists who have not previously worked as one formal group but whose combined expertise is suitable for the assignment. After the work is completed, participants can continue collaborating, return to their existing teams, or use the same structure for another project. A specialist can therefore maintain an individual professional profile, belong to one or more permanent teams, and participate in additional project teams when their expertise is required. Collty is designed to support this more dynamic model of professional work rather than forcing every user into a single fixed organization.
Hiring Complete Teams Instead of Individual Specialists
Companies can now work with complete teams as independent professional units. Instead of hiring several specialists separately and then trying to organize them into a functioning group, a company can discover a team that already combines the required capabilities. A team can present its members, shared expertise, industries, services, and previous experience through a dedicated team profile. Companies can evaluate the team as a whole while still reviewing the background and proposed role of each participant. This is particularly useful for projects that need to begin quickly. A ready-made team may already have an established working structure, defined responsibilities, and experience collaborating across previous assignments. Companies can therefore reduce the time spent on separate searches, interviews, onboarding, and initial coordination. The team composition does not need to remain fixed. A company can hire an existing team and then add internal employees, external specialists, another team, or AI agents where additional capabilities are required.
Combining Internal Teams, External Specialists, and AI Agents
The updated platform supports hybrid project structures that combine several types of participants. A project can involve a company’s internal employees, one or more internal departments, independent specialists, external agencies, ready-made teams, and AI agents. This allows companies to use Collty not only as a hiring platform but also as an environment for organizing their existing workforce. An internal team can create and manage its own project while bringing in external expertise only where it is needed. An agency can use its permanent team as the project core and add temporary specialists for particular workstreams. AI agents can also become part of the same project structure. Depending on their role, they may support planning, research, analysis, documentation, proposal preparation, task creation, progress reviews, and routine coordination. Their responsibilities can be connected to the same project, team, and task structure used by human participants. The objective is not to place AI in a separate window outside the real workflow. Collty is developing a model in which people and AI agents can operate within the same project architecture, with clear responsibilities, dependencies, and areas of work.
Multiple Teams Within a Single Project
Collty now supports projects that involve several teams working toward one shared objective. This is important for larger initiatives that cannot be managed effectively as one flat group of participants. For example, a product launch may require separate teams for product development, design, marketing, public relations, research, finance, and legal work. Each team may have its own members, tasks, responsibilities, and internal workflow, while its results remain connected to the overall project. The platform allows these teams to be represented separately without losing visibility across the larger initiative. Project managers can understand how responsibilities are divided, where deliverables depend on one another, and which teams are involved at each stage. This structure is also useful when several external providers are working for the same client or when an internal company team coordinates work with agencies and specialist groups. Instead of managing each team through a separate system, the organization can connect them within one project environment.
Internal Projects and Client Projects
The new version introduces a clearer distinction between internal company projects and client-facing projects. The two scenarios may involve similar tools, but they require different relationships, workflows, and responsibilities. An internal project is created for work performed within a company or organization. It may involve existing employees, internal teams, AI agents, and selected external specialists. Companies can use this model for product development, research, transformation programs, marketing initiatives, operational improvements, and other internal work. A client project is created when an agency, consulting group, team, or independent specialist is delivering work for an external customer. The project can include the service provider’s existing team, additional specialists recruited through Collty, subcontracted teams, and AI support. This distinction allows Collty to support both sides of project-based work. Companies can organize their own teams and initiatives, while agencies and specialists can manage multiple client engagements and expand their capacity when new expertise is required.
AI-Assisted Project Creation
Users can now create a project from an initial idea, business objective, or incomplete brief with the support of Collty’s AI tools. They do not need to begin with a fully prepared scope, task list, or predefined team. The user describes what they want to create, change, research, or achieve. The AI then helps turn that description into an initial project structure by identifying possible workstreams, roles, stages, deliverables, tasks, milestones, and areas of expertise. The proposed structure can be reviewed and edited before the project begins. Users can remove unnecessary elements, add their own requirements, change the sequence of work, and adjust the recommended team composition. This feature is intended to make the earliest stage of a project more practical. A founder may understand the product they want to build without knowing which specialists are required. A company may know the business result it wants to achieve but not yet have a clear implementation roadmap. Collty helps convert that initial intention into a working project model that can then be developed together with the selected team.
AI Agents for Project and Team Operations
The latest version expands the role of AI agents within Collty. Rather than using AI only to generate isolated pieces of text, users will be able to assign AI support to defined areas of a project. An agent may assist with preparing project plans, creating and updating tasks, analyzing information, drafting proposals, summarizing project activity, organizing documentation, identifying dependencies, or supporting recurring management processes. Different agents can be configured for different professional functions and project contexts. AI agents can be connected to teams and projects alongside human participants. This allows their work to remain visible within the broader delivery structure instead of being separated from the people responsible for the final result. The long-term direction is to give companies and teams the ability to design hybrid operating structures in which human specialists remain responsible for expertise, judgment, relationships, and key decisions, while AI agents support analysis, coordination, and repetitive operational work.
New Opportunities for Specialists to Form Teams
The update also expands what individual specialists can do inside Collty. A user is no longer limited to creating a profile and waiting for a company to discover it. Specialists can create their own teams, invite people they trust, join teams formed by other members, and participate in temporary project groups. They can also develop an idea together, look for complementary expertise, and build a professional unit that can later be hired by companies. This creates a different model from traditional freelance marketplaces, where professionals are usually presented as isolated service providers competing for individual contracts. Collty allows specialists to increase their collective capabilities by combining expertise and offering a more complete project solution. For professionals who already work together informally, the platform provides a way to formalize the team, present its combined capabilities, and manage projects through a shared environment.
Invitation-Based Growth and Trusted Professional Relationships
Collty is currently operating through invitation-based registration. Each invited member can recommend and invite a limited number of people they know, trust, or have worked with before. The invitation system is not only a mechanism for controlling beta access. It also helps establish meaningful professional relationships inside the platform. An invitation from a former colleague, teammate, partner, or client provides a more useful initial signal than an anonymous connection request. These relationships will contribute to the development of Collty’s Collaboration Graph. Over time, the graph can represent how specialists, teams, projects, roles, and professional connections relate to one another. This creates a foundation for recommendations based not only on self-reported information, but also on evidence generated through real collaboration. Users who contribute to the network by inviting trusted specialists can receive additional AI tokens. These tokens can be used for team assembly, project creation, analysis, proposal preparation, and other AI-assisted functions within the platform.
Building an Evidence-Based Collaboration System
The broader purpose of the update is to make professional experience inside Collty increasingly connected to real work. Profiles and resumes remain useful, but they provide only a self-reported description of a person’s capabilities. As projects are created and delivered through Collty, the platform can develop a more structured view of professional activity: which projects a specialist participated in, which role they performed, which teams they joined, who they worked with, which capabilities were used, and how responsibilities were distributed. This does not mean reducing professional performance to a single public score. The objective is to create a richer evidence layer that can help the AI understand different types of experience and working relationships in context. For companies, this can eventually improve the quality of team recommendations and reduce uncertainty when selecting specialists. For professionals, it creates an opportunity to build credibility through real project participation and trusted collaboration rather than relying only on polished profile descriptions.