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Platform Introduction

As finance and digital assets shift from traditional databases toward AI-driven, intelligent services, Primit is committed to evolving from scattered, fragmented market data into a standardized, full-category, AI-friendly data foundation. We hold to our founding mission — let ordinary people access professional-grade data, and make AI smarter — and set out to solve the "high cost, poor experience" problem that individual researchers and small institutions have long faced.

1. Origins: born from real pain points, bringing professional data to everyone​

As quantitative trading and AI in finance took off, demand for high-quality data surged — yet premium data sources stayed locked behind major vendors at steep prices, out of reach for individual participants, students, and early-stage teams. Scrappy data scraped from the web became the reluctant default, but it was riddled with mismatched fields, no standards, no quality control, and disorderly updates. That drove up the cost of manual cleaning and could not meet the hard requirements that AI modeling and strategy development place on data quality.

The Primit team recognized how data-disadvantaged these smaller players were, and set its core mission: let the industry's "underdogs" use professional data too. Through an open API, Primit was the first to open up market and derivatives data in a unified, standardized form for ordinary users — letting students and independent researchers build entry-level quant models and research projects with zero barriers.

2. The leap: from moving data to producing it (the traditional → AI-data watershed)​

Primit's transformation was not just product iteration. It was a rebuild of the underlying logic — from "data scraped off the web" to "self-produced, standardized data" — and a core upgrade to meet the intelligent-data needs of the AI era.

  1. A new production model: We abandoned unstructured, rule-less web scraping in favor of a full-pipeline governance system — community-assisted collection → standardized field definitions → ingestion and archiving → multi-tier quality checks → unified API output. Dirty data and missing values are removed at the source, sharply cutting the preprocessing cost of AI datasets and matching what machine learning and strategy development demand from a low-noise data source.
  2. Built with the community: Across release cycles, community developers took part in feature design and engineering, and practitioners contributed ideas on both requirements and technology. The platform became a product co-created by the whole community — a new operating paradigm for financial information services.
  3. Full-category coverage: The data footprint expanded from single-market quotes to a full matrix spanning market data, derivatives, on-chain, and macro — covering personal analysis, institutional research, academic work, and AI financial modeling in one place.

We believe data is a heavy asset, accumulated and verified row by row over time, requiring continuous investment and accountability — it cannot be "open-sourced" in the pure sense. Primit is therefore positioned as an open community, not an open-source project, clearing up a common misconception about "open data."

3. Professionalization: sustaining the platform, reinvesting in data and service​

Professionalization and commercialization are how Primit sustains its mission of universal access — not a turn toward profit-seeking. The server clusters, bandwidth, and year-round manual data verification are funded chiefly by user credits and modest paid tiers. The resilience that commercial revenue provides is exactly what keeps universal-access service running steadily over the long term.

All commercial proceeds are reinvested into three areas: expanding the data team to improve sources, iterating the quality-control system to raise accuracy, and optimizing the API architecture for batched access by large AI models. The result is a virtuous cycle — commercial sustainability → better data → open access for all — that protects the bottom line: free, open baseline data for ordinary users.

4. Into the AI era: a long-term commitment to the intelligent-finance data foundation​

Entering the AI-driven phase of finance, Primit was an early adopter of agent protocols such as Skills and MCP, and set a long-term roadmap: keep investing in automated collection plus refined, AI-friendly governance; keep broadening coverage and deepening granularity; and benchmark against first-tier commercial databases. We will work alongside quant practitioners, financial researchers, and AI-agent builders across the industry to keep squeezing redundant data-processing cost out of the analysis workflow, and — on a standardized, structured data foundation — lower the data barrier to deploying AI in finance.

From bridging the information gap in capital markets to powering intelligent finance, Primit stays the course, holding to its original vision: universal access to professional data, empowering an AI-driven future for finance.

Want to start building right away? Continue with General Info, Authentication, and Errors, or head to Market Data for the full API reference.