About TrustArk
We are building TrustArk to help teams connect the evidence behind their commitments with the people responsible for them. Our ambition is practical: make recurring trust work easier to understand, coordinate, and review without surrendering human judgment.
In development. These are proposed workflows we are shaping with design partners, not a list of capabilities available today.
Help growing companies know what supports their claims, see what is missing, and make informed decisions about the work ahead.
We are starting with growing US startups and startups expanding into the US, alongside companies across Europe, the Middle East, and Africa. Supporting underserved communities is a commitment across those markets. We want the practical realities of small teams and cross-border operations to shape the product from the beginning.
Our team has interviewed hundreds of customers to understand the problem behind the evidence chase.
Not more AI. More structure.
Those conversations shape our focus: clear business context, defined requirements, accountable owners, and evidence that people can review. AI should support that structure, not replace it.
Our initial industry focus
Our approach starts with the company profile, operating region, size, and the frameworks it needs to demonstrate. That context gives continuous evidence collection and owned Kanban tasks a clear purpose, rather than another generic checklist.
The TrustArk Completeness Agent is the part of this approach focused on missing information and owner follow-up. We want gaps to become visible, accountable work, while people retain the decision about the evidence they receive.
The planned Trust Harness brings six questions to that work: source, ownership, freshness, completeness, traceability, and review. These checks are a design approach, not a certification or a promise that an organization meets any requirement.
We are looking for design partners to challenge these ideas with real workflows. Their feedback can help us understand where collection, coordination, and accountable review need the most attention.
Explore our approachA look at the workspace
Early views of the approach we are building: framework selection, connected sources, and a control matrix that keeps gaps visible.
Our founding team
Our founders bring experience across enterprise technology, AI/ML, banking, healthcare, product, and operations. Their work spans North America, Europe, and Africa, informing how we think about growth and local context.
Enterprise technology & global operations
Experience in enterprise technology, startup building, and product delivery across North America, Europe, and Africa.
Bringing cross-market operating experience to the problems growing companies face when earning trust.
AI engineering & data intelligence
AI/ML and data engineering experience across adtech and healthtech, building intelligent, data-driven systems.
Shaping an approach to automation that values useful outputs, clear boundaries, and human oversight.
Banking & multi-market healthcare
Platform engineering experience across banking and healthcare, including systems serving multiple African markets and work in Scandinavian banking.
Bringing cross-border platform experience to the design of evidence workflows with local context.
Product, analytics & African market operations
Entrepreneurship, product, operations, analytics, and go-to-market experience across African markets.
Keeping the practical needs of underserved businesses close to how we build and collaborate.
Building across markets informs the questions we ask: who owns the evidence, what does it mean in this context, and who needs to review it? We want to combine that experience with the perspectives of founders, operators, and communities whose needs are too easily missed.
Our values
We want to understand the market, customer, and operating context behind a request, rather than assume evidence means the same thing everywhere.
Our design puts responsible owners and human review alongside automation. A suggestion should not quietly become an accepted conclusion.
We believe a useful status needs a source, a defined scope, and an explanation of what is still missing.
We distinguish what we are building from what is available, and the evidence we have from the conclusions it cannot support.