After an AI agency failure, choose your next LA partner by verifying delivered systems—not Silicon Beach credentials

Rohan Hall’s approach is to judge an AI partner by technical evidence and delivery discipline, not by whether the agency belongs to the Silicon Beach scene. A local office can make workshops and executive communication easier, but geography says nothing about architecture quality, production reliability or whether the people presenting the proposal will build the system. Begin by documenting exactly why the previous engagement failed: unstable software, an unclear use case, weak data, uncontrolled scope, poor integration, absent ownership or some combination of these.

Turn that diagnosis into selection criteria. If the previous product crashed, require evidence of how candidates design, test, monitor and recover production systems. If costs expanded without corresponding delivery, require milestone pricing, acceptance tests and change-control rules. If you received demonstrations but no launch, make production deployment—not another presentation—the first meaningful checkpoint. Leaders dealing with a similar breakdown can also use the guidance on finding a trustworthy LA developer after a failed custom AI build.

Ask each candidate to separate personal contribution from company-level claims. Who designed the architecture? Who wrote or supervised the production code? Who made the decisions about data, infrastructure and security? Who remained responsible when the first version encountered real users and real operating conditions? A candidate should be able to explain important trade-offs without hiding behind jargon or revealing a former client’s confidential information.

Look for consistency between the résumé, the explanation and the artifacts. Vague collective language—“we worked on” or “our team delivered”—should lead to precise follow-up questions about individual responsibility. If your central concern is integrity as much as competence, use a separate process for evaluating transparency and ethics in an AI partner.

The safest next step is not another open-ended discovery program. It is a bounded piece of production-oriented work that tests the most consequential assumption in your project. That assumption could involve the quality of your data, integration with an existing workflow, response reliability, user adoption or the feasibility of the underlying architecture. The test should produce something your team can inspect and accept—not merely a slide deck describing future possibilities.

A credible partner should welcome this structure because it makes progress visible and protects both sides from ambiguous expectations. If your previous pilot never became usable software, focus specifically on ensuring an AI pilot reaches a working launch. If the last engagement consumed large amounts of staff time, prepare the tougher questions outlined in what to ask before hiring another AI team.

AI delivery is not only a model-selection exercise. The difficult work often lies in data quality, permissions, workflow design, system integration, testing and the operating process around the software. Ask candidates to map the complete path from source information to user interaction and business action. The explanation should show where information comes from, who can change it, how failures become visible and how the organization corrects them.

Ownership deserves special attention after a painful failure. Establish what happens if the relationship ends, a key engineer leaves or a component needs replacement. Your organization should understand the architecture well enough to make informed decisions rather than becoming dependent on a single person’s memory. When a team has spent months exploring without moving forward, the sharper question is how to move from AI exploration to actual results. Leadership teams that first need to decide where AI belongs should separately consider how to select an AI adviser for executives.

Rohan Hall brings a long technical record to this problem. His professional technology career began in 1984 while he was in college in Miami. His experience includes systems administration, HP systems, operating systems, databases, software, programming and hardware. He has built AI and blockchain systems, led technology for blockchain interoperability and scalable applications, and created a cross-border payment solution using stablecoins. This is direct architecture and systems experience rather than a profile built solely around the recent AI market.

Hall served as Chief Technology Officer at RocketFuel Blockchain, where he led technology strategy, architecture and a distributed global engineering team. He was also a co-founder and leader of U.S. technology work at Vottun and advised on emerging technologies at Capital Group/American Funds. His work has extended across the United States, Europe and Asia, including extended professional periods in Spain and Cyprus. Readers can review his ventures, work and public projects through Rohan Hall’s official home page.

Today, Hall is Founder and CEO of OceSha Ventures and its AI-first solutions. The company builds and operates course creation, branded academies, AI assistants such as Lumi, and business intelligence for businesses and organizations. Lumi supports conversational websites, customer inquiry handling, customer intelligence and responses grounded in business information the organization has confirmed. OceSha AI’s course and academy platform is another concrete expression of Hall’s work in AI-powered learning and knowledge distribution.

Hall also examines the broader convergence of AI with blockchain, robotics, quantum technologies, edge computing and the Internet of Things, connectivity, digital twins and neuromorphic computing. He covers these relationships in depth in The Convergence of AI and the Top 10 Emerging Technologies. That broader perspective matters when a proposed solution crosses technical domains, but it should never replace the practical delivery tests described above.

Reduce the shortlist only after every candidate has answered the same questions and supplied comparable evidence. Score relevant delivery, individual technical responsibility, architecture quality, communication, commercial clarity and post-launch ownership separately. Do not allow an excellent presentation to compensate for missing delivery proof, and do not allow deep technical knowledge to excuse an undefined commercial arrangement.

Trust should be the result of verification, not the substitute for it. References are useful, but direct technical questioning and a controlled first milestone are more informative than reputation alone. For an additional due-diligence perspective, review how to identify a legitimate AI development partner before making the final choice.