Obriy AI Raises USD 500K to Build Multi-Agent AI Workforce Platform

• Kyiv-based Obriy AI attracted USD 500K of Pre-Seed from funding from N1 Investment Company
• The startup’s SURE platform deploys multi-agent AI employees across enterprise workflows
• The solution focuses on traceability, autonomy, and human-in-the-loop supervision systems
• With the new investment, Obriy AI targets European expansion and USD 1M ARR before the upcoming Seed round

This April, the Ukrainian multi-agent AI workforce platform Obriy AI raised its USD 500K Pre-Seed round. N1 Investment Company became the sole investor of the round.

Founding Team and Naming Choices

Obriy AI was started in 2025 by three co-founders bringing complementary backgrounds:

• Viacheslav Shestakovskyi (CEO) is a serial entrepreneur. Before Obriy AI, he built and exited businesses across several industries. This experience came with a recurring and very personal frustration: no matter the business, scaling quality customer communication is brutally hard. Getting every team member to represent a brand the right way was a problem that never fully goes away.
• Dmytro Nosach (COO) spent 15 years in IT leadership. His experience as a team manager in big tech made him the perfect operational leader for the team as he brought a clear vision of what products the team could actually build. 
• Andrii Bobrov (CTO) is a solutions architect with 16 years of experience who had worked together with Mr Nosach at GlobalLogic, which provided for a solid and battle-tested technical foundation. His strong focus on security and wholesome reliability ensure that Obriy AI’s products are suitable for companies of any scale.

‘When AI started showing real capability, I saw a direct answer to something I’d been fighting for years. What convinced us we were the right team? We had the business pain, the product vision, and the architectural depth all in one room. That’s rare, and we knew it,’ Mr Shestakovskyi shares.

‘Obriy’ is the Ukrainian word for ‘horizon.’ For the team, this name signifies how AI as a technology provides the ability to look beyond the horizon and envision things that seemed out of reach just a few years ago. The team named its flagship product SURE — as in ‘sure, I’ll help you’ — a confident, always-ready AI employee that gets things done without hesitation. Jointly, this naming reflects what the team strives to build: a company that expands the possible and a product that straightforwardly delivers on the promise.

Multi-Agent Workforce Vision: AI Employees VS Traditional Automation

To recap, Obriy AI builds a multi-agent AI workforce platform called SURE that lets organizations deploy AI agents to handle customer interactions and internal workflows. These agents connect to enterprise systems like CRMs, ERPs, and knowledge bases to automate tasks and coordinate with human teams across channels. The platform is designed to help companies scale operations and decision-making through coordinated, task-specific AI agents.

Mr Nosach explains where Obriy AI draws the line between an AI employee and an automation tool.: ‘A traditional automation tool executes a predefined workflow: ‘if this, then that.’ It is powerful when the process is stable, structured, predictable, and more importantly, has proper processing pipelines with structured data, e.g. CRM, ERP systems etc. But it usually fails or requires manual intervention when the input is incomplete, the requests are ambiguous, the context changes, information is provided in a chaotic way or a decision requires interpretation.

An AI employee built on the SURE platform is not just a workflow runner; it can comprehend multimodal data and combine it into a structure, interpret context, ask clarifying questions, change its own context, use knowledge sources to enrich its context with more relevant data, call business systems through APIs to get more data, generate decisions first and based on these decisions decide which next step is most appropriate within the boundaries defined by the company.’

Putting it briefly, automation tools complete tasks, while AI employees handle responsibilities.

If a business process automation can move a ticket from one status to another when a form is submitted, a SURE AI employee is designed to understand what the customer is asking, check the knowledge base, identify missing information, ask the customer for clarification, retrieve data from CRM or ERP, prepare a response, escalate when confidence is low, leave a full explanation for a human operator, and communicate it to a customer with the company-specific tone of voice where it’s hard to tell if it’s AI agent or a human operator.

As such, the difference is not only technical, but also operational, and the distinction matters for adoption and trust. A simple automation tool is trusted because it is predictable, while an AI employee is trusted because it is transparent, bounded, measurable, and supervised.

‘SURE is designed around that principle. It allows companies to define the AI employee’s role, knowledge, permissions, escalation rules, quality metrics, and level of autonomy. This helps customers move from simple automation to AI-driven work without losing control over critical business processes,’ Mr Nosach states.

Autonomy, Trust, and Market Education

According to Mr Bobrov, one of the main trade-offs was deciding where autonomy creates the most value and how to coordinate it effectively. In SURE, agents were designed to be autonomous within their domains, but that autonomy operates within a clearly defined system context. Rather than having agents act independently across the entire workflow, Obriy AI chose to focus on enabling strong task-level autonomy while keeping the overall flow well-orchestrated. This approach allows agents to make decisions within their scope, while the system ensures alignment, efficiency, and consistency across interactions.

Mr Nosach points out that most Obriy AI’s customers don’t want a technical explanation of multi-agent systems at the beginning. What they need is to understand what work can be improved, what risks are controlled, and how the result will be measured. Further, trust comes gradually. Teams are more willing to adopt multi-agent systems when they can first see them operating as a copilot, not as a fully autonomous replacement. The system can prepare answers, analyze cases, summarize calls, check data, or recommend next actions, while humans review the output. As such, Obriy AI’s market education is practical: the product demonstrates real workflows, proves value in a controlled pilot, and then increases autonomy only after the customer already has confidence.

AI Workforce Success and Accountability

Obriy AI evaluates the success of an AI agent workforce as a combination of business impact, operational quality, and control. Mr Nosach specifies that while latency, accuracy, and cost savings are important, the Obriy AI team also looks at whether the system can be trusted inside real business operations. The less obvious but very important metric here is decision traceability. Success is defined as a workforce of agents that is fast, accurate, cost-efficient, explainable, and safe to supervise.

Accountability becomes critical in multi-agent systems, especially in regulated environments like legal aid. In this concern, each SURE agent operates within a clearly defined scope, and all its actions, inputs, decisions, outputs are traced and attributed with context. This creates a structured chain of responsibility, even when multiple agents contribute to a single outcome. The orchestration layer makes it possible to audit how the final outcome was produced step by step.

In regulated scenarios, Obriy AI additionally enforces such guardrails as validation layers, human-in-the-loop checkpoints, and policy constraints to ensure that outputs meet compliance requirements before they are finalized. Having accountability built into the architecture through traceability, scoped responsibility, and verifiable decision flows allows the system to remain both autonomous and auditable.

Funding and Strategic Expansion

The recent USD 500K funding from N1 Investment Company is meant for Obriy AI to accelerate product development, expand the engineering and commercial teams, and scale internationally. Mr Shestakovskyi describes it as a strategic bridge round ahead of the startup’s Seed raise planned for autumn 2026.

‘Obriy is a prime example of a Ukrainian technology team building a global product even under the most difficult conditions”, says . “For N1, this deal is not just an investment, but a long-term partnership and a belief in the potential of Ukrainian AI companies,’ N1 Investment Company’s CEO Kyrylo Medvediev comments. 

‘It was important for us to find not just an investor, but a partner who is strategically involved in product development. N1 understands the AI market and our vector. This is exactly the synergy that will allow us to scale globally significantly faster, Mr Shestakovskyi adds.

On the commercial side, Obriy AI’s 12-month target is USD 1M ARR built with a concentrated portfolio of enterprise clients across the specific European markets identified by the team. The CEO emphasizes one of SURE’s meaningful technical advantages being how well it handles non-common languages, making it a natural fit for markets that global competitors tend to deprioritize. Obriy AI targets several enterprise-level clients in these markets.

‘On the product side, customer support remains our entry point, but the roadmap goes further. Every client engagement teaches us how to automate more complex workflows. By the end of this period, we expect SURE to be running AI employees across sales, analytics, and operational processes, not just support queues,’ Mr Shestakovskyi adds.

When the time comes for the Seed round, Obriy AI expects to walk in with proven European traction, a repeatable go-to-market motion, and a product that enterprise clients in multiple countries have already validated at scale.

Obriy AI’s approach reflects a shift from narrow automation toward accountable, multi-agent systems that can participate in real business operations rather than just execute fixed workflows. By emphasizing traceability, scoped autonomy, and human-in-the-loop control, the company is directly addressing the trust and governance barriers that have limited enterprise AI adoption so far. It’s possible to envision SURE defining a new category of ‘AI employees’ that make advanced AI practical, auditable, and scalable across complex organizational environments.