How to Build a Proof of Concept: Test Feasibility Before You Commit

When You Need a POC

A proof of concept is not a smaller MVP. It serves a different purpose. A POC answers one question: can this be built? The glossary definition of a POC clarifies exactly where it sits relative to prototypes and MVPs, which matters when you are trying to explain the distinction to non-technical stakeholders.

You need a POC when:

• The core technology is unproven for your use case
• Stakeholders need evidence before approving a budget
• Third party APIs or integrations have unknown limitations
• Performance requirements are uncertain (can it handle 10,000 requests per second?)
• AI accuracy for your specific domain is untested

You do NOT need a POC when:

• The technology is well understood (standard web apps, CRUD, auth, payments)
• You have built something similar before
• The risk is market fit, not technical feasibility

Step 1: Define the Question

A good POC tests one specific technical question. Not three. Not "is this product viable." One question.

Good POC questions

• "Can we extract structured data from scanned invoices with 95% accuracy using an LLM?"
• "Can our PostgreSQL database handle 5,000 concurrent users with sub 200ms response times?"
• "Can we integrate with the customer's legacy SOAP API and sync data in real time?"
• "Can an AI agent accurately classify support tickets into our 12 category taxonomy?"
• "Can we process and display real time video feeds from 20 cameras simultaneously in a browser?"

Bad POC questions

• "Can we build a CRM?" (Yes, obviously. The question is whether anyone wants yours.)
• "Is AI useful for our business?" (Too broad. Which task? Which metric?)
• "Should we use React or Vue?" (This is a preference, not a feasibility question.)

Define success criteria upfront

Before building, write down what "success" looks like:

Criteria	Target	Measurement
Accuracy	Above 90%	Test against 100 sample inputs
Latency	Under 500ms	Measure p95 response time
Cost	Under $0.10 per request	Calculate from API pricing
Reliability	99% uptime	Run for 48 hours continuously

If the POC meets all criteria, proceed. If it misses one, decide whether the gap is closable. If it misses several, the approach does not work.

Step 2: Scope the Minimum Test

The POC should be the absolute minimum code needed to answer the question. Nothing more.

What to include

• The core technical mechanism you are testing
• Enough data to make the test meaningful (not 5 examples; at least 50)
• Measurement code to capture success metrics
• A simple way to demonstrate results

What to exclude

• User interface (a terminal output is fine)
• Authentication
• Error handling for edge cases
• Deployment to production infrastructure
• Documentation
• Tests (the POC IS the test)

Example: AI invoice extraction POC

// This is POC code. It is intentionally minimal.
import Anthropic from "@anthropic-ai/sdk";
import fs from "fs";

const anthropic = new Anthropic();

interface InvoiceData {
  vendor: string;
  amount: number;
  date: string;
  invoiceNumber: string;
}

async function extractInvoice(imagePath: string): Promise<InvoiceData> {
  const imageData = fs.readFileSync(imagePath).toString("base64");

  const response = await anthropic.messages.create({
    model: "claude-sonnet-4-6-20250514",
    max_tokens: 1024,
    messages: [
      {
        role: "user",
        content: [
          {
            type: "image",
            source: {
              type: "base64",
              media_type: "image/png",
              data: imageData,
            },
          },
          {
            type: "text",
            text: "Extract: vendor name, total amount, invoice date, invoice number. Return JSON only.",
          },
        ],
      },
    ],
  });

  const text =
    response.content[0].type === "text" ? response.content[0].text : "{}";
  return JSON.parse(text);
}

// Test against 50 sample invoices
async function runPOC() {
  const samples = JSON.parse(
    fs.readFileSync("test-data/invoices.json", "utf-8")
  );
  let correct = 0;
  let total = 0;

  for (const sample of samples) {
    const result = await extractInvoice(sample.imagePath);
    const isCorrect =
      result.vendor === sample.expected.vendor &&
      Math.abs(result.amount - sample.expected.amount) < 0.01 &&
      result.invoiceNumber === sample.expected.invoiceNumber;

    if (isCorrect) correct++;
    total++;
    console.log(`${sample.imagePath}: ${isCorrect ? "PASS" : "FAIL"}`);
  }

  console.log(`\nAccuracy: ${((correct / total) * 100).toFixed(1)}%`);
  console.log(`Cost: ~$${(total * 0.003).toFixed(2)} for ${total} invoices`);
}

runPOC();

This POC answers: "Can we extract invoice data with acceptable accuracy?" in under 100 lines of code.

Step 3: Build Fast, Measure Everything

Build timeline

A POC should take 3 to 7 days:

Day	Activity
Day 1	Set up the test environment, prepare sample data
Day 2 to 3	Build the core mechanism
Day 4 to 5	Run tests, collect metrics
Day 6	Analyze results, identify gaps
Day 7	Write the findings report

If you are still building on day 5, the scope is too large. Cut it down.

What to measure

Every POC should capture:

1. Accuracy or correctness: Does it produce the right output?
2. Performance: How fast is it?
3. Cost: What will it cost at production scale?
4. Reliability: Does it work consistently or intermittently?
5. Limitations: What does it NOT handle?

Log all results. Do not rely on "it seemed to work." Numbers are what stakeholders need to make decisions. The go/no-go feasibility scorecard gives you a structured way to present these numbers as a weighted decision rather than a subjective judgment. After the POC succeeds, the MVP Cost Calculator helps you estimate what the full production build will cost before you commit.

Step 4: Test Edge Cases (Selectively)

A POC does not need to handle every edge case. But it should identify them.

Edge case categories

• Happy path: Normal inputs that represent 80% of real world usage → Test these
• Boundary cases: Extreme values, empty inputs, maximum sizes → Test a few
• Error cases: Invalid inputs, API failures, timeouts → Note them, do not build handling
• Scale cases: What happens at 10x or 100x expected volume → Estimate, do not test

Document every edge case you find, even if you do not handle it. This becomes the risk section of your findings report.

Step 5: Present the Results

The POC deliverable is not code. It is a decision document.

POC findings report structure

# POC Results: [What Was Tested]

## Question
The specific technical question this POC answers.

## Approach
What was built and how it works (2 to 3 sentences).

## Results
| Criteria | Target | Actual | Status |
|----------|--------|--------|--------|
| Accuracy | >90%   | 93.5%  | PASS   |
| Latency  | <500ms | 320ms  | PASS   |
| Cost     | <$0.10 | $0.003 | PASS   |

## Limitations
- Does not handle [specific case]
- Accuracy drops to X% for [specific input type]
- Requires [specific dependency or service]

## Production Estimate
- Development time: X weeks
- Monthly running cost: $X
- Key technical decisions needed before build

## Recommendation
PROCEED / PIVOT / ABANDON with rationale.

Presenting to non technical stakeholders

Lead with the business impact, not the technical details:

• "We tested whether AI can extract invoice data automatically. It can, with 93% accuracy. This replaces 15 hours per week of manual data entry. Building the production version takes 3 weeks and costs $5,000."
• Save the technical details for the appendix or follow up questions.

Step 6: Decide Next Steps

Based on POC results, there are three paths:

Path 1: Proceed to MVP

The POC met all success criteria. Move to building the full product.

• Rewrite the POC code properly (the POC itself is throwaway)
• Use POC findings to scope the MVP accurately
• Include edge case handling from the POC limitations list
• Follow our guide on how to build an MVP

Path 2: Pivot the approach

The POC partially worked. The concept is sound but the specific approach needs adjustment.

• Try a different model, API, or architecture
• Narrow the scope to the cases where it does work
• Run a second POC with the adjusted approach (3 to 5 more days)

Path 3: Abandon

The POC clearly failed. The technical approach does not work for this use case.

• Document what was learned (this knowledge has value)
• Evaluate alternative solutions (different technology, manual process, buy instead of build)
• Save the money you would have spent building something that does not work

All three paths are successful outcomes. A failed POC that saves you from a failed $50,000 project is a great investment.

DIY vs Hire an Agency

POCs are usually best built in house because:

• Your team knows the domain and data best
• Speed matters more than polish
• The knowledge gained from building the POC informs the MVP

Hire an agency for the POC when:

• The technology requires specialized expertise your team does not have (AI/ML, hardware integration, specific API)
• The stakeholder deadline is tight and your team is committed to other work
• You want an unbiased technical assessment (internal teams may be invested in a specific outcome)

At HouseofMVPs, we build proof of concepts in 5 to 7 days starting at $1,500. Each POC includes a working demo, test results, and a findings report with a clear recommendation. If the POC succeeds, we can transition directly into an MVP build.

Common POC Mistakes

Testing too many things at once. A POC should answer one question. If you are testing AI accuracy AND database performance AND third party API integration, you have three POCs, not one.

Using production quality standards. The POC is throwaway code. Do not spend time on error handling, documentation, or clean architecture. Speed to answer is all that matters.

Not defining success criteria upfront. Without predefined targets, you will rationalize mediocre results as "good enough." Set the bar before you start.

Skipping the edge cases analysis. You do not need to handle edge cases in the POC. But you need to document them so the MVP scope accounts for them.

Confusing a POC with a demo. A demo shows what the product could look like. A POC proves what is technically possible. If stakeholders want a demo, build a clickable prototype in Figma. Save the POC for technical questions.

For detailed guidance on transitioning from POC to MVP, read our blog post on POC to MVP without a rewrite. For understanding when you need which, see POC vs MVP: the real difference.