Business
Proof of Concept (POC)
What a proof of concept is and when you need one
Definition
A proof of concept (POC) is a small-scope build that tests whether a proposed technical solution works in practice. In software development, a POC typically takes 2-4 weeks and produces a functional prototype that demonstrates feasibility, performance characteristics, and integration viability. 1Raft offers free POCs so clients can evaluate results before committing budget.
How it works
A POC sits between an idea and a product. It strips the concept down to one or two core assumptions and builds just enough to prove or disprove them. If the POC succeeds, the team has validated architecture, identified risks, and produced reusable code. If it fails, the investment was weeks instead of months.
The scope matters. A good POC tests the riskiest assumption first. If the question is whether an LLM can extract structured data from messy PDFs with 95% accuracy, the POC builds that pipeline and nothing else. No login screens, no dashboards, no deployment infrastructure.
For buyers, a POC reduces procurement risk. Instead of evaluating vendors on proposals and references, you evaluate them on working software. The team that builds the best POC is usually the right team for the full build.
How 1Raft uses Proof of Concept
1Raft builds free proof of concepts for qualified projects. We scope the riskiest assumption, build a working prototype in 2-3 weeks, and present results. If the POC convinces you, we continue to full build. If not, you keep the code and walk away.
Related terms
AI/ML
Large Language Model (LLM)
A large language model is a neural network trained on massive text datasets to understand and generate human language. LLMs power chatbots, content generation, code assistants, and most modern AI products.
AI/ML
Prompt Engineering
Prompt engineering is the practice of crafting and optimizing the instructions given to a language model to get consistent, high-quality outputs. It is the most accessible and cost-effective way to improve AI application behavior without modifying the underlying model.
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Next Step
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We apply this in production across industries. Tell us what you are building and we will show you how it fits.