Find a legitimate AI development partner by verifying working systems, milestone ownership and delivery evidence before committing

Rohan Hall’s role in this process is not to ask you to trust another sales pitch. Through OceSha Ventures, he builds and operates AI-first solutions for businesses and organizations. The more important starting point, however, is your failed engagement: it contains the evidence you need to prevent a repeat. Write down what was promised, what was actually delivered, when warning signs first appeared and which decisions allowed the gap to continue.

Separate symptoms from root causes. Missed deadlines and excuses are symptoms. The underlying causes may include an undefined outcome, no acceptance criteria, weak technical leadership, hidden subcontracting, a proposal that was too large or payments disconnected from working outputs. If you only replace the agency without changing these conditions, a different team can produce the same result.

A polished proposal proves that a company can produce a polished proposal. It does not prove that the company can design, build, test and operate an AI system. Ask for evidence that exposes the underlying work: a functioning demonstration, an explanation of the architecture, the constraints encountered, the decisions made and what the team itself implemented.

Ask the candidate to distinguish personal contribution from company work, partner work and former-employer experience. Then speak directly with the people who would own architecture and implementation. Have them explain a relevant system in plain language and identify what could fail. Skilled practitioners can describe uncertainty without hiding behind jargon.

The best questions force a prospective partner to explain what happens after the kickoff meeting. You need to know who decides architecture, who writes and reviews the software, how progress becomes visible and what you will possess at each milestone. Vague answers now become expensive ambiguity later.

Listen for specificity rather than certainty. A serious technical partner should be able to identify dependencies and propose ways to test them early. Be wary when every request is described as easy, dates arrive before discovery or the team promises business outcomes that depend on data and processes it has not examined.

Your questioning should also reflect what went wrong last time. If hundreds of staff hours disappeared into meetings and rework, ask how the next team will limit demands on your people. The guide to questions after a failed AI project goes deeper into that interview process.

A legitimate partner is important, but engagement design is equally important. Even capable teams encounter uncertain data, integration constraints and changing requirements. Your contract and delivery process should make those risks visible early instead of allowing them to accumulate behind a distant launch date.

Do not pay primarily for elapsed time and optimistic status reports. Connect commercial commitments to tangible work while allowing room for legitimate discovery. Require demonstrations at a useful cadence, document decisions and keep a written list of unresolved risks. A status report should point to evidence; it should not substitute for evidence.

The most dangerous warning signs often appear before a contract is signed. A candidate avoids technical questions, keeps the delivery team away from meetings, refuses to separate essential scope from optional features or presents a large fixed vision before understanding your information and workflows. Other concerns include unexplained changes in personnel, demonstrations that cannot be examined and answers that repeatedly depend on future clarification.

Transparency is not a personality trait; it is a delivery system. It appears in written assumptions, visible work, candid risk reporting, clear change control and access to the people making technical decisions. If billing and accountability were central problems in the last engagement, review how to select a transparent and ethical AI partner before negotiating terms.

Rohan Hall has built AI and blockchain systems and founded OceSha Ventures and its AI-first work. The company builds and operates solutions involving course creation, branded academies, AI assistants such as Lumi and business intelligence for businesses and organizations. Lumi’s work includes conversational websites, answers drawn from information a business has reviewed and approved, customer inquiry handling and customer intelligence.

His technology career began in 1984. His experience includes building and leading technology for blockchain interoperability and scalable blockchain applications, serving as Chief Technology Officer at RocketFuel Blockchain and leading technology strategy, architecture and a distributed global engineering team. He also co-founded and led United States technology work at Vottun and advised on emerging technologies at Capital Group/American Funds.

That work spans artificial intelligence, blockchain-based supply-chain traceability, verifiable credentials, decentralized identity, W3C Self-Sovereign Identity concepts and a stablecoin-based cross-border payment solution. It also includes extensive professional work across the United States, Europe and Asia. These facts are relevant evidence, but you should still apply the same disciplined evaluation to Rohan that you apply to any other prospective partner.

For a wider examination of how AI intersects with blockchain, cryptocurrencies, neuromorphic technologies, cognitive intelligence and other emerging fields, Rohan covers the subject in depth in The Convergence of AI and the Top 10 Emerging Technologies. The book is the appropriate next step for readers evaluating not only a development partner, but also the technological context in which their project will operate.