Derisking Complex and AI-Powered Features With Proofs of Concept

The Core Problem

Fixed-price billing requires accurate scope estimation.
Accurate scope estimation requires understanding what you are building.
Real-world applications have complex requirements while often lacking the clarity necessary for accurate scope estimation at the start.

This creates tension:

Clients want cost predictability.
Developers want scope clarity.
AI capabilities (or complex business logic) introduce uncertainty into both.

At the end of this article you can find an example based off a real-world scenario where I used a proof of concept to derisk and justify investment in an AI-powered feature that turned the tide for the project.

What a Proof of Concept Actually Does

A proof of concept (PoC) is a bounded experiment. It answers a specific technical question with minimal investment. It produces evidence for decision-making and estimation.

A proof of concept is not a "beta". It is a minimal software application that answers exactly one question:

Is building this feasible with what we have right now?

If the answer is no, then you spend 3 hours validating and scoping an idea instead of wasting 40 hours building something that was doomed to fail, required unforeseen work, or had prohibitive operational costs.

When to Use This Approach

Not every feature requires a proof of concept. The methodology applies when uncertainty is high and cost of failure is significant.

Indicators that a PoC is warranted:

The feature relies on integrating third-party software, hardware, AI models or other complex systems.
There is uncertainty about the technical feasibility or effort required for the feature.
The client or team lacks prior experience with the technology.

If the path from requirement to implementation is clear, build directly. If it is not, then validate first.

Practical examples:

You're building an application that uses AI to determine causes for plumbing issues through pictures and input sent by users.
You're building an application that manages money (invoices, subscriptions, marketplaces, etc) based on custom business logic you can't replicate through a standard native integration, such as Stripe.
You're building an application that will process a large amount of data and you need to be sure that it handles all possible formats.
You're building an application that integrates with a third-party system, especially if it's closed-source or the documentation is complex/unclear.

Anatomy of an Effective Proof of Concept

An effective proof of concept has four components:

Defined Question: What specific technical capability are you validating? The question must be answerable with a yes or no.
Representative Data: The PoC must use real-world inputs. Synthetic data hides edge cases that will surface during integration.
Success Criteria: What output quality or performance threshold constitutes success? Define this before building.
Integration Pathway: Document how the validated capability would connect to the production system. This prevents PoC code that cannot be integrated.

Example: Mid-Project AI Requirements

Consider a scenario. An IoT platform project is two weeks from a fixed deadline. The client requests AI-powered optimization of device parameters. The requirement has no technical specification.

Adding unvalidated scope to a fixed-price engagement creates two risks:

The feature may not be technically feasible within constraints.
The integration may require refactoring existing code.

The proof of concept approach addresses both:

Build the AI capability in isolation and validate it works with actual device data.
Design integration to avoid touching existing architecture.

The outcome in this scenario was delivery within original timeline and budget. The full pivot was executed within 2 weeks. Integration required no major refactoring of shipped code.

Example: Document Processing Automation

Consider another scenario. A compliance platform requires processing technical documents. Manual entry takes 2-4 minutes per document. The client asks if AI can accelerate this.

The domain is specialized:

Construction documentation contains technical terminology with legal implications.
Generic extraction would produce errors with real consequences.

A proof of concept was built using sample documents from actual workflows, leading to:

Stakeholder buy-in, justifying further investment into the project.
Processing time reduced to under 10 seconds.

The PoC investment was measured in days. The production integration was measured in weeks. But the integration proceeded with known performance characteristics - not assumptions.

Why This Enables Fixed-Price Work

Fixed-price billing fails when scope estimation fails. Scope estimation fails when requirements contain unresolved uncertainty.

The proof of concept removes uncertainty from the estimation equation. After validation, you are not estimating whether something works. You are estimating how long integration takes.

Integration estimation is a solved problem. Technical teams do it reliably. The difficulty is always in the unknown - and the PoC converts unknowns to knowns.

The Economic Logic

A proof of concept costs time and money. This investment must be justified.

The justification is insurance. A PoC that reveals technical infeasibility costs less than an integration that fails mid-implementation. A PoC that validates an approach costs less than rework after incorrect assumptions surface.

For AI features specifically, the value is higher. AI capabilities vary significantly based on data quality, domain complexity, and model selection. A working prototype provides evidence that no amount of documentation review can provide.

Implementation Guidance

Keep the PoC timeline short. Most PoCs I make take 2 to 3 days, with some being a few hours, depending on complexity. Particularly complex ones or multiple iterations can take up to 1 week.
Use production-representative data from day one. A PoC validated on clean sample data will fail on messy production data.
Document findings regardless of outcome. A failed PoC that explains why something does not work has value. It prevents the same dead end from being explored again.
Separate PoC code from production code. Avoid optimizing PoC code with production concerns. The code itself should be treated as disposable, but the architecture and general solution, e.g. interfaces used, should be reusable or easily translatable into production code. This is how I complete them in a short time.

Conclusion

Fixed-price delivery and complex/AI-powered features are not incompatible. The incompatibility is between fixed expectations and the volatility of uncertainty.

Proofs of concept resolve uncertainty before commitment. It converts estimation guesswork into informed planning. It enables evidence-based expectation setting on features that would otherwise be likely to throw the project off-track and overbudget.

The investment in validation pays for itself in predictability:

Clients get cost certainty and can make informed decisions.
Developers get scope clarity and can plan their work.
Projects get delivered on time and on budget.

If you want to see this in action, see my public implementation: