Bootstrapping my AI Development Startup with a Dataset Side Project (Drop 1 Launched)

Howdy All!

I'm a solo founder building DriftLogic, a small AI development studio focused on tools that analyze logic, narrative, and structure.

To fund the development of my main product (a tool called ClarityLens that analyzes argument structure in writing), I decided to launch a smaller, scrappier product first. One that solved a problem I personally ran into, finding high-quality argument labeled training data..

Most public datasets for argument mining:

• Are tiny, around 200-500 samples

• Come with restrictive licenses

• Are created for academic benchmarking not real-world model training

I needed more data to train my models, which made me think: "What if other AI/ML engineers are facing the same problem?"

So I built DriftData, a DriftLogic product of a growing collection of sythetic, QA reviewed datasets for training models in logical reasoning and argument structure. I just launched the first drop of DriftData this week, it contains:

• 1,500 Persuasive Essays

• Each annotated in JSON format with argumentation structure (Claim, Premise, Relationship)

• Created by a multi-agent generation pipeline of my design with automated QA + manual QA layers to ensure validity.

• Licensed for indie, research, or commercial use cases

Even though it began as a way to scratch my own itch, it also became the first revenue test for DriftLogic. If I can sell something useful, even in small volume, it helps fund the larger vision without VC or debt.

If it works, it will also power ClarityLens, a full SaaS for text decomposition and reasoning.