Why PoC Is Critical for AI Projects
You invested budget in an AI solution, but after half a year you find the model doesn't work on real data. PoC is the instrument that insures against such scenarios. It's not a presentation, but an engineering check: "Does the idea work on our data?" Without PoC, every second ML project fails at the implementation stage. A properly conducted PoC saves months of development and significant investment. Many teams skip the PoC phase, considering it unnecessary bureaucracy. Result: 40% of AI projects never reach production due to overlooked technical limitations. PoC reveals these before major expenses. In practice, setting a clear success criterion at the PoC stage reduces budget overrun risk by 3 times.
Proof of Concept (PoC) is an implementation of some idea to demonstrate its feasibility. — Wikipedia
How PoC Saves Budget
According to our statistics, 40% of PoCs confirm metric achievability, 35% require rethinking the approach or data, 15% show technical infeasibility, and 10% reveal data insufficiency. In the first two cases, you get a roadmap with minimal costs. In the others, you prevent losses from developing a knowingly non-working solution. A typical PoC takes 2–4 weeks of work by one or two engineers. Estimates show PoC reduces costs on failed projects by up to 80%. Compare: full-scale MVP development can cost 5–10 times more than a PoC, and the risk of failure without hypothesis validation is even higher.
Example from practice: for a document classification task, the PoC showed that a simple TF-IDF model achieves F1=0.78, while fine-tuned BERT achieves 0.81. The 3% difference does not justify GPU infrastructure costs: BERT requires 10 times more computation. Therefore, a lightweight solution was chosen. Without PoC, the team would have spent months on BERT integration with no significant gain.
PoC Structure
Step 1: Scope Definition (1–2 days) A specific task with a measurable success criterion. For example, "AI classifies requests with accuracy >85%". Without a clear scope, PoC becomes endless research.
Step 2: Data Audit (1 week) Real client data: volume, format, quality, labeling availability. Check for missing values, outliers, class imbalance. If no data exists, define a minimum dataset (typically 500–5000 records). Without real data, PoC is meaningless.
Step 3: Baseline (1 week) A simple solution: rule-based system, keyword matching, linear regression. Baseline answers: "Why do we need ML?" If baseline yields 80%, ML might be unnecessary. Often baseline is 5 times faster than ML models on simple cases.
Step 4: ML Solution (2–3 weeks) Fast experiment with minimal stack: PyTorch, Hugging Face Transformers, OpenAI API. Goal is a representative result, not optimal solution. We use LoRA for fine-tuning LLMs in NLP tasks.
Step 5: Evaluation and Decision (1 week) Comparison with baseline, error analysis (which cases are hard for the model), assessment of production requirements: data, compute, time. Outcome: Go/No-Go recommendation.
PoC Deliverables
| Deliverable | Description |
|---|---|
| Jupyter notebook | Experiments with code, reproducible on your data |
| Report with metrics | Precision, Recall, F1, latency P99, inference cost |
| Recommendation document | Go/No-Go with justification and roadmap to MVP |
Metrics Evaluated in PoC
Beyond accuracy and recall, we assess latency p99, cost per inference, memory requirements, GPU utilization. For LLM projects, tokens per request, context window, and hallucination probability are critical. All metrics are captured in the report and compared to baseline.
PoC vs MVP vs Production: Comparison
| Parameter | PoC | MVP | Production |
|---|---|---|---|
| Goal | Hypothesis validation | Launch minimal product | Stable system |
| Code quality | Prototype | Partially production-ready | High code quality |
| Data | Sample (hundreds to thousands) | Real but limited | Full data |
| Metrics | Accuracy, feasibility | User engagement, ROI | SLA, availability, cost |
| Team | 1–2 ML engineers | Full-stack + ML | DevOps, ML, Backend |
How to Order a PoC
We assess your project within 1 business day. Simply contact us — and our engineers will prepare a proposal. We have delivered over 50 PoCs for projects of varying complexity: from text classification to RAG systems on LLMs. Certifications (Azure AI, AWS ML) guarantee data confidentiality. Get a consultation today and verify your idea's feasibility without unnecessary risks.