Building an AI Proof of Concept: Successful that Platforms Started Small and Scaled

You’ve got an idea to build an AI Platform. But how do you know it will actually work? What if you invest months of time and money only to find out that the idea wasn’t viable or the approach wasn’t right.

That’s where a Proof of Concept (PoC) comes in. It’s the smartest way to validate your AI idea before you commit to a full-scale product.

At Invozone, we have helped founders like you take an AI concept and turn it into a working PoC to a Scalable product—fast, cost-effectively, and risk-free. 

Yes, we got case studies to prove that.

Why Start with an AI Proof Of Concept?

First, investors don’t fund just an idea. Second, if you spend months building a full product, chances are someone else will launch it before you do. Why not be first to market with your idea? That’s exactly why a PoC (Proof of Concept) matters.

You have a problem you want to solve—we train LLM models to achieve 95-97% accuracy within a month. Once that happens, you can either start raising funds or pitch it to investors with real proof in hand.

Building an AI platform without validation is like setting sail without a map. A POC gives you a clear direction by:

✅ Minimizing Risk: Test your idea without committing a fortune.
✅ Validating Your Solution: Make sure it actually solves the problem.
✅ Attracting Investors: A working PoC can be your best pitch deck.
✅ Saving Time & Money: Avoid expensive mistakes and pivot early if needed.

And the best part? You don’t need a full tech team to get started—we’ll handle everything.

How to Pick the Right LLM Model for Your AI Proof of Concept

Choosing the right LLM for your AI PoC isn’t about picking the most powerful model—it’s about picking the one that actually works for your use case. Here’s how we do it at Invozone:

• Start With the Problem, Not the Model

Forget the hype. What do you actually need AI to do? Summarize documents? Answer customer questions? Detecting fraud? Start there. If you don’t define the problem first, you’ll waste time testing models that aren’t even built for what you need.

• Test Off-the-Shelf First

We always start by running quick tests with pre-trained models like GPT-4, Claude, Llama 3, or Mistral. No fine-tuning, no expensive training—just plug in your data and see what happens. If it works, great. If not, we move to customization.

• Fine-Tune Only If Necessary

If a pre-trained model isn’t hitting 95-97% accuracy, we fine-tune it. But here’s the thing—fine-tuning takes time and money. Instead of jumping straight to it, we first try prompt engineering, hyperparameter tweaks, and retrieval-augmented generation (RAG) to improve results without extra training.

• 4. Optimize for Cost, Speed, and Scale

Your PoC doesn’t need a massive, expensive model if a smaller, faster one does the job. We compare:
🚀 Accuracy – Does the model produce the right answers?
💰 Cost – How expensive is each API call?
⚡ Latency – How fast can it respond?

We run head-to-head tests to find the best balance before making a final choice.

• 5. Get to a Working PoC, Fast

A PoC isn’t about perfection—it’s about proving your idea actually works. Once we hit 95-97% accuracy, you have something to show investors or stakeholders. That’s when you can start raising funds or planning for full-scale development.

If you’re serious about AI, the worst mistake you can make is over-engineering in the early stages. Start small, move fast, validate the idea—then worry about scaling.

How We’ve Helped Founders Like You

How We Built the AI for Easyfill.ai (A Simple Story)

It all started with a simple question: Can AI make form-filling effortless? What began as a small PoC turned into a robust, scalable system that is now used by thousands of businesses. Here’s how we built Easyfill.ai, step by step.

Step 1: The Problem We Saw

Filling out forms is time-consuming, error-prone, and frustrating for both businesses and their customers. We wondered, “What if AI could do all the heavy lifting?”

Step 2: First Experiments

We began by teaching the AI to read forms, just like a human would. We tested the AI on various document formats, including PDFs, Word files, and scanned images, to see if it could extract details like names, dates, and addresses.

Step 3: Training the AI

At first, the AI made mistakes—mixing up addresses and missing fields. But we kept refining the model, training it on thousands of forms and teaching it to recognize patterns and avoid errors.

Step 4: Making It Smarter

We introduced smart rules, so the AI could identify specific fields. For instance, if a form asked for an email address, the AI knew to look for “@” and “.com.” This made the AI much more accurate.

Step 5: Turning PDFs into Smart Forms

Businesses use PDFs, but they’re not easy to edit. We built a tool that automatically turned PDFs into fillable forms, saving hours of manual work.

Step 6: Connecting Everything

We made sure Easyfill.ai could integrate with tools like Slack, Zapier, and WordPress. Now, businesses could collect data without copying and pasting.

Step 7: Testing in Real Life

We tested the system in real-world settings, gathering feedback from users. When we found issues, we tweaked the system until it worked flawlessly.

Step 8: Scaling Up—The Hero Moment

Once we proved that AI-powered forms could streamline operations, we scaled the platform to handle thousands of businesses and millions of forms. We optimized security and made it easy to add new features. Today, Easyfill.ai is used by over 2,000 businesses, transforming the way they fill out forms.

Step 9: The Final Result

Easyfill.ai saves businesses time, reduces errors, and integrates seamlessly with the tools they already use. What once took 20 minutes now takes just 1.

How Invozone Validated AI POC to Solve Recruitment Pain Points with HatchProof

Hiring can be a challenge. Resumes don’t tell the whole story, interviews are often unreliable, and finding the right cultural fit can feel impossible. That’s when we saw an opportunity for AI to make a real difference.

The Idea

What if AI could go beyond scanning resumes? What if it could understand candidates on a deeper level—how they think, work, and fit within a team?

Step 1: Building the Proof of Concept

Before diving into a full-fledged platform, we built a PoC. The AI analyzed behavior, compared candidates to existing team members, and generated job descriptions automatically.

Step 2: Testing with Real Users

We worked closely with hiring teams to refine the system. Their feedback was invaluable, helping us improve everything from data accuracy to smarter algorithms.

Step 3: Scaling Up

Once the PoC was successful, we scaled it. We built a platform where HR teams could interact with AI insights, handle large volumes of data, and integrate with existing tools.

The Result: Smarter, Faster Hiring

With HatchProof, hiring teams can now:

• Quickly find the right cultural fit
• Automate job descriptions
• Make data-driven decisions

We’ve taken AI from concept to real-world solution, and we’re excited about the future of AI-driven hiring.

Whether it’s improving efficiency, enhancing user experiences, or solving complex problems, AI is here to stay. If you’re ready to start your AI journey, we’d love to help. Reach out to us today, and let’s create something amazing together!

Common Concerns (And Why They Shouldn’t Hold You Back)

💰  How much will an AI PoC cost compared to a full product?
A POC costs a fraction of a full product. Instead of spending six figures on something untested, we help you build a lean version that proves your idea—without breaking the bank.

⏳ I don’t have time to build a PoC—can someone handle it end to end?
We handle everything end to end. You focus on your vision; we take care of development.

🤷 How do I know if AI is the right solution for my business?
That’s exactly why we start with a PoC! Instead of taking a blind leap, we identify use cases and validate your concept before you fully commit.

Want to See Your AI Idea Come to Life?

We’re taking on only three new PoC projects this month.

Let’s talk about yours. Book a free consultation, and we’ll:
✔️ Walk through your AI idea
✔️ Identify the best way to build a PoC
✔️ Share insights from successful startups we’ve helped