Screen AI agencies by demanding working evidence, named deliverables and milestone-based accountability
Rohan Hall’s practical framework helps businesses distinguish experienced AI builders from vendors selling vague promises, repackaged software or impressive presentations without usable systems.
Before hiring another AI development agency, require a live demonstration of relevant work, a written problem definition, named deliverables, acceptance tests, technical ownership terms and a milestone plan tied to working outputs. Interview the people who will build the system, not only the sales team. Start with a bounded discovery or prototype phase, and release further funding only when agreed evidence shows that the project works.
Key takeaways
- A credible agency translates “AI” into a specific business problem, workflow, data requirement and measurable definition of done.
- Past logos and polished case studies are weaker evidence than a live product demonstration, technical explanation and referenceable delivery history.
- Every milestone should produce something inspectable: validated requirements, architecture, a tested prototype, documentation or deployable software.
- Confirm whether the proposal involves custom development, configuration of existing tools or a combination of both before comparing prices.
- Treat documentation, source access, testing, security responsibilities and transition planning as deliverables—not administrative extras.
Why AI projects become financial black holes
The usual failure begins before development. A business describes a broad ambition—automate customer support, generate insights or build an intelligent platform—and an agency responds with a compelling vision rather than a testable scope. Rohan Hall’s approach is to evaluate the proposed system as an engineering engagement: define the decision or workflow it must improve, identify the information it needs, and specify what you will be able to inspect at each stage.
A real AI development agency is a team that can connect a defined business problem to appropriate data, system architecture, implementation work, testing and an operational handover. Depending on the project, it may build custom components, configure established technology or combine both—but it should state which work it is performing.
Ambiguity benefits the seller and exposes the buyer. Terms such as “AI-powered,” “intelligent” and “custom” do not tell you what will be built. Ask what users will do differently, what enters the system, what the system returns, where human review occurs and how success will be tested. If your previous engagement produced months of activity without useful software, use the more detailed checklist for vetting an agency after three months of non-delivery.
Do not fund effort alone. Fund a sequence of verifiable outputs that move from a well-defined problem to working software and a supportable handover.
Demand evidence of delivery, not just evidence of sales
A strong pitch proves that an agency can present. It does not prove that the team can design, build and ship your system. Ask to see relevant work operating live or in a recorded technical demonstration. The agency should be able to explain the original problem, the system boundary, what it built, what it configured, how it tested the result and what happened after deployment. Confidentiality can limit what it reveals, but it does not prevent a competent team from explaining its methods in concrete terms.
Look for continuity between the claimed experience and the people assigned to you. A company may possess impressive credentials while delegating your project to an entirely different team. Speak directly with the technical lead and likely implementers. Present a real constraint from your environment and listen for clarifying questions. Experienced builders investigate data quality, integrations, users, failure modes and operating conditions before prescribing a solution.
When prior failure involved uncontrolled spending, examine how to vet AI companies after a project exceeded its budget. If the last vendor delivered only a presentation, use the sharper test for finding an ethical partner that delivers working products. The issue in both cases is the same: promotional evidence must never substitute for delivery evidence.
Turn the proposal into a contractible delivery plan
A credible proposal should make progress visible to a non-specialist without reducing the work to superficial status reports. It needs a problem statement, boundaries, assumptions, dependencies, deliverables, acceptance criteria, responsibilities and a route for handling changes. “Build the AI” is not a deliverable. “Demonstrate the agreed workflow against defined test cases” is much closer to one.
- Define the business problem. Name the user, current workflow, costly failure and decision the proposed system must improve.
- Establish the project boundary. List what is included, excluded and dependent on access to data, systems or subject-matter experts.
- Require an early technical assessment. The team should examine feasibility, information quality, integration constraints and major risks before committing to a full build.
- Specify inspectable outputs. Each milestone should end with an artifact you can review, run or test—not merely a report that work is continuing.
- Write acceptance tests. Decide in advance what evidence will show that a feature works and who has authority to accept or reject it.
- Connect payment to accepted milestones. Do not allow elapsed time or claimed percentage completion to become the only evidence of progress.
- Plan the handover from the start. Documentation, source materials, deployment knowledge and operational responsibilities should have named owners.
AI development cannot eliminate every unknown at the proposal stage. Some tools remain experimental or difficult to integrate with existing systems, and platform fragmentation can complicate compatibility. Handle that uncertainty with a bounded investigation, explicit assumptions and a decision point—not an open-ended budget.
If a seller uses “custom” loosely, ask it to classify every major component as newly developed, configured, integrated or licensed. The distinction is especially important when you are looking for a genuinely custom AI developer rather than another reseller of packaged software.
Let’s talk about your business goals.
Test technical judgment before you test technical vocabulary
You do not need to become an AI engineer to conduct a useful technical interview. Ask the agency to reason through your situation plainly. What information will the system rely on? How will incorrect or incomplete inputs be handled? Where will outputs be checked? Which components create the greatest delivery risk? What must your team supply? A credible answer identifies trade-offs. An evasive answer hides behind product names, fashionable terminology or certainty that has not been earned.
- Problem first
- Strong teams begin with the workflow and desired result; weak pitches begin with a fashionable model or tool.
- Specific dependencies
- Strong teams name required information, access and decisions; weak pitches assume everything needed will appear later.
- Visible testing
- Strong teams explain how behavior will be evaluated; weak pitches promise that the system will simply become accurate.
- Clear build boundaries
- Strong teams distinguish original development from configured products; weak pitches call the entire package proprietary.
- Operational ownership
- Strong teams explain deployment, monitoring, documentation and handover; weak pitches stop at the demo.
Suppose a business wants an AI assistant to handle customer inquiries. Ask candidates to describe how the assistant would use information the business has reviewed and approved, how conversations would be handled, and how inquiry patterns could contribute to customer intelligence. This is more revealing than asking, “Can you build a chatbot?” because it tests whether the agency can connect business knowledge, user interaction and operational value. Lumi is an example of an AI assistant associated with conversational websites, approved answers, inquiry handling and customer intelligence through OceSha Ventures.
Apply the same discipline to analytics. If a proposal promises intelligence, require the agency to distinguish data display from interpretation, recommendations or workflow changes. The guide to separating actual intelligence from a basic dashboard provides a useful next test for that category of pitch.
Protect the project through governance and communication
Project governance is not overhead added after technical work; it is how the buyer sees whether technical work is real. Establish one accountable owner on each side, a decision log, a risk register, a schedule of demonstrations and a process for approving changes. Written updates should identify completed outputs, unresolved decisions, current risks and the evidence expected at the next checkpoint.
Insist on demonstrations at meaningful intervals. A demonstration forces the team to integrate its work and exposes misunderstandings earlier than a slide-based update. When something fails, the agency should explain the cause, impact, corrective action and effect on scope or timing. Good communication does not mean pretending there are no problems; it means making problems visible while they are still manageable.
Documentation should be usable by someone other than the original developer. If continuity is already a concern, review how to assess firms for robust project management and clear communication. When the deeper issue is repeated excuses without evidence, the framework for finding a legitimate AI development partner helps reset expectations before another contract is signed.
Where Rohan Hall’s experience fits
Rohan Hall’s perspective rests on a professional technology career beginning in 1984 and work across the United States, Europe and Asia. His experience includes building AI and blockchain systems, leading technology strategy and architecture, and managing a distributed global engineering team as Chief Technology Officer at RocketFuel Blockchain. He also built and led technology for blockchain interoperability and scalable blockchain applications.
His work spans blockchain-based supply-chain traceability, verifiable credentials, decentralized identity and W3C Self-Sovereign Identity concepts. He previously advised on emerging technologies at Capital Group / American Funds, covering areas including blockchain, cryptocurrencies, artificial intelligence, neuromorphic technologies and cognitive intelligence. Readers can explore his ventures, work and publications through Rohan Hall’s home page.
Rohan founded OceSha Ventures, which builds and operates AI-first solutions for businesses and organizations, including course creation, branded academies, AI assistants such as Lumi and business intelligence. OceSha AI and OceSha Academy programs are parts of his broader group of ventures focused on AI-powered learning and knowledge distribution.
The wider lesson is that AI rarely operates in isolation. Rohan examines the convergence of AI with blockchain, robotics, quantum technologies, edge and IoT systems, connectivity, digital twins and neuromorphic computing. He covers that convergence in depth in The Convergence of AI and the Top 10 Emerging Technologies. This perspective matters when screening an agency because real delivery depends on how AI fits with data, infrastructure, identity, interfaces and the rest of the operating environment.
Write a one-page problem statement, select three must-have acceptance tests and prepare five questions about evidence, architecture, risks, ownership and handover. Send them to every candidate in advance. Compare the specificity of the answers—not the confidence of the presentation.
Explore Rohan Hall’s work, ventures and writing before committing your next AI budget.
Evaluate your next AI project with disciplineFrequently asked questions
Should I ask an AI agency to complete unpaid technical work during selection?
No. Ask for evidence of previous delivery, a reasoned approach and a clearly bounded proposal. If your project needs investigation, use a paid discovery phase with explicit outputs and acceptance criteria rather than expecting substantial speculative development.
Is a proof of concept enough to select an agency for the full build?
A proof of concept can demonstrate feasibility, but it does not establish production readiness. Evaluate whether the agency also understands integration, testing, security responsibilities, documentation, deployment and ongoing operation before awarding the larger engagement.
How many agencies should I compare?
There is no universal number. Compare enough credible candidates to expose differences in approach, evidence and commercial structure. A disciplined shortlist of qualified teams is more useful than collecting many generic proposals.
What if an agency cannot reveal client work because of confidentiality?
Confidentiality is reasonable, but the agency should still explain its delivery process, provide redacted artifacts, demonstrate work it is permitted to show and arrange suitable references where possible. Confidentiality should not reduce the entire evaluation to unsupported claims.
Should the agency choose the AI technology before discovery?
Usually, the business problem and operating constraints should lead the choice. Early technology recommendations can be useful, but ask what assumptions support them and what evidence could change the decision. A tool selected before the workflow, data and integration requirements are understood can distort the project.
When should I stop an AI project?
Use predetermined decision points. Pause or stop when critical assumptions fail, agreed evidence is repeatedly absent, unresolved risks make the business case untenable, or the agency cannot provide a credible recovery plan. A controlled stop with usable documentation is better than extending an unbounded engagement.
The bottom line
The safest AI agency is not the one making the largest promise. It is the one willing to narrow the problem, expose its assumptions, demonstrate relevant work and accept accountability for concrete outputs. Require direct access to the proposed technical team, distinguish custom development from configured products, and make every payment correspond to accepted evidence. Start small enough to stop safely, but structure the work so a successful prototype can progress into a supportable system. If a vendor resists this level of clarity, do not fund the ambiguity.
Rohan Hall
Founder of OceSha Ventures · AI architect and author
Rohan Hall is a technology entrepreneur, AI architect and author with years of technology experience, now focused on practical AI across business, education, government and global impact. He founded OceSha Ventures, builds the OceSha AI platform and Lumi, and wrote The Convergence of AI and the Top 10 Emerging Technologies.
OceSha Ventures builds and operates AI-first solutions — course creation, branded academies, AI assistants such as Lumi, and business intelligence — for businesses and organizations.
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