Building 134 Data Platforms with AI

TheDataProject.AI operates 134 data platforms across 17 domains, containing over 375 million searchable records. Building this at any traditional pace would require years and a large engineering team. We did it with AI.

The Mission

Government data is technically "public" — but practically inaccessible. It's buried in massive CSV files, locked behind clunky query interfaces, spread across dozens of agency websites, and formatted in ways that require technical expertise to parse.

Our mission is simple: take every significant public dataset and turn it into something a normal person can actually use. Search it, filter it, understand it, share it. No data science degree required.

The Stack

Every platform in our portfolio follows a similar architecture, refined over dozens of iterations:

• Data ingestion: Python scripts download, parse, and normalize raw data from government sources (HHS, OPM, USASpending, FEC, FDA, Census, and more)
• Processing pipeline: We clean, deduplicate, link entities across datasets, calculate derived metrics, and build search indexes
• Frontend: Next.js with TypeScript provides fast, SEO-friendly interfaces with instant search, filtering, and data visualization
• AI assistance: Claude helps write data pipelines, generate analysis code, build UI components, and draft content — accelerating development by 10-20x

How AI Changes the Equation

The traditional approach to building a data platform involves months of work: understanding the data schema, writing ETL pipelines, designing the database, building the API, creating the frontend, and writing documentation. For 134 platforms, that's years of engineering time.

AI collapses this timeline dramatically:

• Schema understanding: Feed Claude a data dictionary and sample records, and it can generate the entire ingestion pipeline in minutes
• Code generation: Standard patterns (search, filtering, pagination, visualization) are generated from templates and customized per dataset
• Analysis: AI can identify interesting patterns, outliers, and story-worthy findings in datasets we might not have time to manually explore
• Content: Each platform needs explanatory content — what the data means, how to use it, what to look for. AI drafts this, humans refine it

The result is that a platform that might take a team of engineers 3-6 months can be built in days. Not because AI replaces engineering judgment — but because it handles the repetitive 80% while humans focus on the critical 20%.

The Scale of the Data

Our platforms span an extraordinary range of public data:

• Healthcare: Medicaid payments, Medicare data, FDA approvals, hospital quality metrics
• Government: Federal workforce data, spending records, contract awards, grant distributions
• Elections: Campaign contributions, donor records, PAC spending, lobbying disclosures
• Education: School performance, university data, student outcomes, funding distribution
• Transportation: FAA data, vehicle safety records, infrastructure spending
• Finance: SEC filings, FDIC bank data, CFPB complaints, economic indicators

Why It Matters

Democracy depends on informed citizens. But information isn't useful if it's inaccessible. When Medicaid fraud detection data sits in HHS flat files that only data scientists can parse, the public can't hold providers accountable. When federal spending data requires SQL expertise to query, taxpayers can't see where their money goes.

Every platform we build removes a barrier between the public and their own data. That's not just a technical achievement — it's a democratic one.

Explore all 134 platforms at TheDataProject.AI.