AI in production: From proof-of-concept to actual value

Introduction

"We have created a fantastic AI proof-of-concept!" This sentence is one we often hear at Hyperbolic. But then comes the next question: "How do we get it into production?" And that’s where many AI projects come to a halt.

The fact is that the vast majority of AI proof-of-concepts never reach the production phase. Some studies estimate that only 20-30% of AI projects actually create real business value. Why is there such a large gap between demo and reality? And most importantly, how does one go from proof-of-concept to an AI system that delivers value every single day?

Why do so many AI projects fail?

Before we dive into the solution, we need to understand the problem. In our work at Hyperbolic, we have identified the most common pitfalls:

Lack of business focus Many AI projects start with technology rather than the problem. "We need to use machine learning" instead of "We have a problem; can AI help us solve it?"

Data quality is underestimated In the demo phase, one often works with a small, clean dataset. In reality, data is messy, incomplete, and constantly changing. Many AI projects fail not due to poor models but due to poor data.

Infrastructure is ignored Running a model on a data scientist's laptop is one thing. Running it securely, at scale, and reliably in production is something entirely different.

Human factors are forgotten AI needs to be used by real people in their daily work. If the system does not fit into existing workflows, or if users do not trust it, it will not be used.

Maintenance is underestimated An AI model is not a traditional software product that simply runs. It needs to be monitored, updated, and often retrained. Many forget these ongoing costs.

The path to production: Our framework

At Hyperbolic, we have developed a framework for taking AI from proof-of-concept to production. It consists of five phases:

Phase 1: Problem validation and business case

Before we write a single line of code, we ensure that we are solving the right problem.

Define the success criteria What does success mean concretely? "Improve customer service" is too vague. "Reduce average handling time by 30%" is measurable.

Calculate ROI What does it cost to build and operate the solution? What is the value of the improvement? If ROI is not clear, then stop here.

Identify stakeholders Who will use the system? Who will be affected by it? Involve them from the start.

An example from our work: A client wanted to use AI to automate invoice approval. In the proof-of-concept, they achieved 95% accuracy. Impressive! But when we calculated it, it turned out that the 5% errors would require so much manual review that the savings were minimal. We pivoted to use AI as assistance for prioritization instead of full automation.

Phase 2: Data audit and pipeline

Data is the foundation. Without solid data, everything falls apart.

Assess data quality At Hyperbolic, we always start with a thorough data audit:

• How complete is the data?

• How consistent is it?

• Is there bias in the data?

• How often is it updated?

Build a robust data pipeline Proof-of-concepts can often get by with static CSV files. Production requires a pipeline that can:

• Collect data from various sources

• Clean and validate data

• Handle missing or faulty data

• Version datasets

We often use tools like Apache Airflow to orchestrate data pipelines and ensure that data is always up-to-date and validated.

Establish data governance Particularly in regulated industries like pharma, where we have a lot of experience, data governance is critical. Who has access to which data? How do we ensure GDPR compliance? How do we handle sensitive data?

Phase 3: Model development with production in mind

Now comes the fun part: building the model. But we do it differently than a typical proof-of-concept.

Choose the simple model first The most advanced model is not always the right one. At Hyperbolic, we often start with simple models like logistic regression or decision trees. If they solve the problem well enough, why make it more complex?

Complexity comes at a cost:

• Harder to explain and build trust

• Harder to maintain

• Slower inference

• Higher computational requirements

Focus on robustness A model that performs fantastically on test data but breaks down on edge cases in reality is useless.

We always test for:

• Edge cases and outliers

• Distribution shift (when data changes over time)

• Adversarial examples (attempts to fool the model)

Make the model explainable Especially in regulated industries, explainability is important. Even with complex models, we use techniques like SHAP or LIME to be able to explain individual predictions.

Phase 4: Integration and deployment

Here, production truly differs from proof-of-concept.

Build a robust API The AI model must be accessible to other systems. We always build a well-designed API around the model with:

• Input validation

• Error handling

• Rate limiting

• Versioning

Implement monitoring In production, we need to know how the model performs in reality. We monitor:

• Prediction accuracy over time

• Latency (how fast the model responds)

• Data drift (changes in input data distribution)

• Model drift (changes in model performance)

Security and compliance Especially in the pharma industry, where we have extensive experience, security and compliance are non-negotiable. We ensure:

• Data encryption in transit and at rest

• Audit logging of all predictions

• Access control

• Compliance with relevant regulations (GDPR, GxP, etc.)

Gradual rollout We never launch an AI model to 100% of users on day one. Instead, we use:

• Canary deployment: Start with 5% of traffic, gradually increase

• A/B testing: Compare the AI solution with the existing process

• Human-in-the-loop: Let the model suggest, but humans approve

Phase 5: Ongoing maintenance and improvement

Deployment is not the end, it is the beginning.

Retraining strategy AI models need to be updated when the world changes. We always establish a retraining strategy:

• How often should the model be updated?

• What triggers should initiate retraining? (e.g., drop in performance)

• How do we validate new versions before deployment?

Feedback loops We build systems where users can give feedback on predictions. This feedback is used to improve the model over time.

Performance review Quarterly review of:

• Does the model still meet business requirements?

• Are there new opportunities for improvement?

• Should we pivot to another approach?

Case: From 92% accuracy in demo to 98% in production

We worked with a Danish company that wanted to automate the classification of customer inquiries. Their proof-of-concept showed 92% accuracy on a clean dataset.

Our approach:

Data improvement We quickly discovered that the right data was much messier than the demo dataset. We spent three weeks on:

• Cleaning historical data

• Establishing data validation for new inquiries

• Handling edge cases (e.g., inquiries in other languages)

Model simplification Paradoxically, we upgraded from a complex deep learning model to a simpler ensemble model. The result: Better performance, faster inference, and much easier maintenance.

Gradual rollout We started by allowing the model to classify 10% of the inquiries. Customer service representatives could see the model's suggestions and approve or correct them. After two months of stable performance, we increased it to 50%, and after four months to 100%.

Results after six months:

• 98% accuracy in production (better than the proof-of-concept!)

• 40% reduction in handling time

• High user satisfaction among the customer service team

• The system now processes 5000+ inquiries per day

Practical advice from our experiences

Start small, but think big Choose a well-defined use case that can deliver value quickly. But design the system so that it can scale and expand.

Involve end-users from day one They should participate in defining requirements, testing the solution, and providing feedback. Without their buy-in, the project will fail.

Prioritize MLOps from the start MLOps (Machine Learning Operations) is to AI what DevOps is to software. Invest in proper MLOps tools and processes from the beginning.

Be honest about uncertainty AI is not deterministic like traditional software. Communicate clearly to stakeholders that models have uncertainty and can fail.

Measure everything If you don’t measure it, you can’t improve it. Establish clear metrics from day one and follow them religiously.

The tools we use

At Hyperbolic, we have a standard stack for AI projects:

Data & Features:

• Apache Airflow for data pipelines

• Great Expectations for data validation

• Feature stores like Feast

Model Development:

• Jupyter notebooks for experimentation

• MLflow for experiment tracking

• DVC for versioning data and models

Deployment:

• Docker for containerization

• Kubernetes for orchestration

• FastAPI for model serving

Monitoring:

• Prometheus & Grafana for metrics

• Custom dashboards for model performance

• Alerting for performance degradation

When are you ready for production?

Before you go into production, ask yourselves:

1. Can we explain why the model gives this prediction?

2. Have we tested on real, messy production data?

3. What happens if the model fails?

4. Can we roll back to a previous version?

5. Do we know how to update the model over time?

6. Have end-users been involved and approved the solution?

7. Is data governance and security in place?

If the answer is yes to all of these, you are likely ready.

Conclusion

Taking AI from proof-of-concept to production is a journey that requires much more than good machine learning. It requires solid software engineering, strong data governance, user involvement, and a long-term maintenance plan.

At Hyperbolic, we have helped many companies with this journey, both in general software development and in regulated industries like pharma. We know what it takes for AI projects to succeed in reality.

The good news is that once you have established the right processes and infrastructure, the next AI project becomes much easier. The investment in getting it right the first time pays off many times over.

Are you ready to take your AI project from demo to reality? Contact us at Hyperbolic for a no-obligation chat about how we can help.