Vessla, a Stockholm-based property technology startup founded in 2026, has raised $3.3 million in an oversubscribed pre-seed funding round led by Seedcamp. The company is developing AI agents designed to automate property operations using the software systems real estate businesses already rely on.

The round attracted backing from Inception Fund, Tiny VC, operators from Lovable and OpenAI, Lukas Saari of Tandem Health, Fredrik Hjelm of Voi and Pit, and several angel investors.

Vessla's long-term ambition is to build an operating system for entire property portfolios, connecting leasing, building operations, and investment planning through a shared AI layer. Rather than replacing existing software, its agents are designed to work across connected systems and assist with the coordination of everyday property tasks.

Vessla's $3.3 Million Pre-Seed Funding

Seedcamp led the oversubscribed $3.3 million round, bringing together investors with experience in early-stage technology, AI, and enterprise software.

Other backers include Inception Fund and Tiny VC, alongside individual operators associated with Lovable, OpenAI, Tandem Health, Voi, and Pit.

The funding comes as Vessla begins developing its approach to automating coordination across property businesses. The company has not disclosed its valuation or provided a detailed breakdown of how the capital will be allocated.

Its investment thesis centres on applying AI agents to complex operational workflows where businesses must coordinate information across multiple teams, software products, and approval processes.

Why Property Operations Are Difficult to Automate

Property operations involve coordination among tenants, property managers, suppliers, and finance teams. Employees frequently need to collect information from different systems, verify details, follow organisational rules, and ensure that the next steps in a process are completed.

These tasks can be repetitive, but they are not always straightforward. Different properties and organisations may have their own workflows, software configurations, and approval requirements. Exceptions can also require human judgment.

Vessla is developing a common AI layer that works with existing property software. Its agents collect relevant information, follow organisation-specific rules, and assist with tasks through connected systems.

The company does not intend to eliminate existing software or remove people from decisions requiring judgment or approval. Instead, it aims to automate parts of the coordination process while keeping humans involved where responsibility and decision-making matter.

This approach targets a practical challenge in enterprise AI: making agents useful in real business environments rather than limiting them to demonstrations or isolated tasks.

Building an AI Operating System for Property Portfolios

Vessla's broader objective is to create an operating system that links leasing, building operations, and investment planning across entire property portfolios.

Property teams would set priorities and make important decisions, while AI agents help coordinate execution and identify opportunities to increase income or reduce costs.

Connecting these functions could help property businesses coordinate activities across their existing systems rather than treating each operational area separately.

However, delivering this vision will require reliable integrations, access to relevant business information, rigorous testing, and clear limits on what agents can do independently.

The company has not disclosed specific customer names, quantified savings, or commercial performance figures in the supplied announcement. Demonstrating measurable outcomes will be important as it moves from product development toward broader adoption.

A Founding Team With AI and Enterprise Experience

Vessla's founding team brings experience spanning the space industry, AI consulting, enterprise technology, and software development.

Kasper Janehag began his career working on NASA's Mars 2020 mission before moving into AI, where he led major projects at QuantumBlack, McKinsey's AI arm.

Chief scientist Arash Dabiri has developed and tested agentic AI applications at Andon Labs and QuantumBlack. CEO Orman Gazvini has advised major software companies at McKinsey and worked on strategy and mergers and acquisitions at Pophouse.

Other members of the founding team have experience at Legora, Stanford, and Netlight.

The founders believe the challenge is not simply building an AI agent that can complete a task in a controlled setting. The harder problem is ensuring that it can work reliably across complex business systems, handle exceptions, and operate within clear boundaries.

Janehag has emphasised the need for deep integrations, sufficient context, rigorous evaluation, and limits on agent actions. Gazvini has highlighted the potential for AI to change property maintenance, costs, leasing, and rental income while preserving the importance of human judgment and responsibility.

Why Seedcamp and Inception Fund Are Backing Vessla

Seedcamp partner Felix Martinez pointed to the founding team's experience building advanced AI applications and deploying them in high-stakes enterprise environments.

Inception Fund founder and partner Oliver Molander highlighted how quickly AI technology is advancing and the difficulty businesses face in keeping their internal systems up to date.

His view is that vertical AI companies can create value by continuously translating new AI capabilities into practical applications for specific industries.

For property businesses, this could mean adopting improved AI capabilities without having to rebuild their entire technology stack whenever underlying models change.

Vessla's opportunity lies in combining this technical progress with the operational knowledge required to work across property software, business rules, and real-world processes.

Competition in AI-Powered Property Technology

Vessla is entering a market where several companies are applying AI to property operations.

New York-based Uniti raised a $12 million Series A led by Pathlight to support agents handling leasing, maintenance, and collections for self-storage and multifamily housing operators.

ProptechOS provides an agentic software suite for real estate companies, while EliseAI has raised a $250 million Series E and operates in AI-powered real estate and healthcare workflows.

Although these companies have overlapping interests, their products, target customers, and operating models are not identical.

The broader investment environment also points to growing interest in AI-native property technology. The source article cites the Center for Real Estate Technology & Innovation for an estimate that AI-native proptech companies raised around $4.5 billion in venture financing in 2025, with their share of overall proptech venture financing increasing by 42%.

AI agents are also being applied to operational work in other sectors, from hotel reception to business-to-business invoice management.

For Vessla, the challenge will be to establish a distinct position by demonstrating that its platform can coordinate complex property workflows reliably and deliver measurable customer value.

What Comes Next for Vessla?

With $3.3 million in pre-seed funding, Vessla is working toward its ambition of connecting leasing, building operations, and investment planning through AI agents.

Its approach allows property companies to retain their existing software while adding a layer intended to coordinate tasks across systems.

The next important milestones will be commercial: naming customers, demonstrating measurable savings, and showing that its agents can handle real operational workflows safely and consistently.

Competition from companies such as Uniti and EliseAI means Vessla will need to establish its value as the market develops. Its technical expertise and enterprise experience provide a foundation, but customer adoption and proven results will ultimately determine whether its vision can scale.

The latest round gives the Stockholm startup early backing as it works to turn AI agents into a practical operating layer for property businesses.