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An AI proof of concept (PoC) for a Singapore SME typically costs S$5,000 to S$15,000 and takes 3 to 6 weeks, covering a narrow, working demonstration of one use case using a limited data set, rather than a fully polished, production-ready system. Its purpose is to answer one question cheaply before you commit a much larger budget: does this actually work for our business?

What a proof of concept actually is (and isn't)

A PoC is deliberately narrow. It tests whether the core idea works technically and produces useful results, using a limited scope: one use case, a sample of your real data, and minimal integration with surrounding systems. It is not a production system. It typically won't have the full error handling, security hardening, or scalability of what you'd eventually deploy company-wide.

Confusing a PoC with a pilot or a full implementation is a common source of frustration. A PoC proves the concept works. A pilot (see below) tests it with real users in a live but limited setting. A full implementation rolls it out properly across your business.

PoC vs pilot vs full implementation

Stage	Purpose	Typical cost (SGD)	Typical timeline
Proof of concept	Prove the core idea technically works	S$5,000 - S$15,000	3-6 weeks
Pilot	Test with real users in a limited live setting	S$10,000 - S$25,000	6-10 weeks
Full implementation	Production-ready system, rolled out fully	S$20,000 - S$50,000+	8-16 weeks

Some projects skip the PoC and go straight to a pilot, especially when the use case is well-established elsewhere (a standard FAQ chatbot, for instance, doesn't usually need a separate PoC). PoCs are most valuable when the use case is genuinely uncertain: unusual data, an unproven approach, or a novel integration.

What should be included in a PoC

• A clearly defined success criteria, agreed before the PoC starts (not after, when it's convenient to redefine "success")
• A working demonstration using a sample of your real (or realistic, anonymised) data
• A written summary of results: what worked, what didn't, and what it would take to move to a full build
• An honest recommendation on whether to proceed, pause, or change approach

What's usually NOT included in a PoC

• Full system integration with all your production systems
• Complete error handling and edge-case coverage
• A polished user interface
• Security hardening for production-grade deployment
• Ongoing support (a PoC is time-boxed by nature)

If a vendor quotes PoC pricing but is promising production-grade robustness, either the price is unrealistically low or "production-grade" is being used loosely. Clarify this upfront.

How to define success criteria before starting

The single biggest driver of a useful PoC is agreeing what success looks like before you start, not after you see the results. Useful success criteria are specific and measurable: "the system correctly answers 80% of test queries drawn from real customer questions" is testable. "The chatbot should feel smart" is not. Write these down and agree them with your vendor in writing before the PoC begins.

What happens after a PoC

Three realistic outcomes:

• It works well. You move to a pilot or full implementation, using what was learned in the PoC to refine the approach and scope the next phase more accurately.
• It partially works. Some assumptions were wrong (data quality, a particular integration, the complexity of the actual conversations). This is valuable information, cheaply bought, that saves you from a much more expensive failure later. See why most AI projects fail for common failure patterns a good PoC can catch early.
• It doesn't work. The idea isn't viable as scoped, at least not with current data or systems. This is a legitimate and useful outcome. A S$8,000 PoC that saves you from a S$40,000 failed full build has done its job.

How to budget realistically

Beyond the PoC fee itself, budget time from your own team: someone needs to provide sample data, answer questions about the current process, and review results. A PoC that gets no input from your side tends to produce results that look fine in isolation but don't reflect how your business actually operates.

What data you actually need to provide for a PoC

One of the most common delays in a PoC timeline isn't the technical build, it's getting usable sample data from the client in the first place. A PoC generally needs a realistic sample of real (or realistically representative, anonymised where sensitive) data: a set of actual past customer enquiries for a chatbot PoC, a batch of real invoices for a document processing PoC, or genuine transaction records for an automation PoC. Data that's been artificially cleaned up or simplified before handing it over tends to produce PoC results that look better than what you'd see in production, which defeats the purpose. If you're planning a PoC, start pulling together representative real examples before the kickoff call, since this is often the single biggest time saver in the whole process.

Who should be involved from your side during the PoC

A PoC works best when at least one person from your team who actually understands the current process day to day is available for a few hours across the PoC period, not necessarily full-time, but enough to answer specific questions and review interim results. This is usually not the business owner alone; it's often more valuable to have the person who actually does the task manually today, since they know the real edge cases and workarounds that don't show up in official process documentation.

Should you skip the PoC and go straight to a full build?

This makes sense when the use case is well-proven (a standard customer service chatbot, a common invoice automation pattern) and the main uncertainty is really about your specific integration details rather than whether the core approach works at all. It's riskier when you're doing something genuinely novel for your business, working with messy or unusual data, or when the cost of a failed full build would be painful. When in doubt, the PoC's cost is usually cheap insurance against a much larger wasted spend. See our AI implementation roadmap service for how PoCs typically fit into a phased plan.

Common mistakes that undermine a PoC's value

The most common way a PoC ends up wasting money is when success criteria are defined loosely or not at all, so the results at the end can be interpreted either way depending on who's looking at them. A close second is testing with unrealistically clean or small sample data that doesn't reflect the messiness of real operations, which produces results that look great in the PoC and then disappoint at full scale. A third mistake is treating the PoC as a sales exercise to justify a decision that's already been made, rather than a genuine test that could reasonably conclude "this doesn't work as well as hoped." If you notice any of these happening in your own PoC, it's worth pausing to reset expectations before continuing.

How PoC findings should shape the next phase's scope and price

A properly run PoC should directly inform the price and scope of what comes next, not just serve as a generic "yes, proceed" signal. If the PoC revealed that data quality is worse than expected, the next phase's quote should include a data cleanup component that wasn't in the original estimate. If integration with a particular system turned out to be more complex than assumed, that should be reflected honestly in the follow-on pricing rather than absorbed silently by the vendor (which usually means it gets cut from scope elsewhere instead). A vendor who treats the PoC purely as a formality, quoting the exact same follow-on price regardless of what was learned, likely wasn't paying close attention during the PoC itself.

Who typically owns a PoC's outputs afterward

Clarify upfront whether the code, prompts, and configuration built during the PoC belong to you or remain the vendor's proprietary property, especially if you're considering switching vendors for the full build. Some vendors treat the PoC as effectively a paid sales exercise and don't expect you to reuse the underlying work elsewhere; others are happy to hand over what was built regardless of what you decide next. Neither approach is inherently wrong, but knowing which one you're dealing with avoids an awkward conversation later if the PoC succeeds and you want to shop the full build around to other vendors.

It's also worth asking whether the vendor who ran the PoC gets any preference or advantage in pricing for the full build. Some structure this fairly, crediting part of the PoC fee toward the next phase if you proceed with them. Others treat the PoC purely as a standalone, separately priced engagement. Neither is wrong, but knowing which applies helps you judge whether a follow-on quote from the same vendor is genuinely competitive or simply convenient.

Ready to see what AI can do for your business?

If you're unsure whether your use case needs a proof of concept first, we'll give you an honest read after a short scoping conversation, including telling you if you can skip straight to a pilot. Learn more via AI implementation roadmap or browse our full services. Request a quote or get in touch. WhatsApp +65 9184 9908 or email glenn@freemansland.co.

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