Entity Resolution Pipeline for API Data
TL;DR
Pre-built serverless entity resolution pipeline for data engineers and startup CTOs at early-stage companies (10-50 employees) that automatically merges duplicate records using fuzzy matching (names, addresses, IDs) and optimizes data for millisecond API queries so they can cut manual duplicate fixes by 10+ hours/week and ensure API responses load in milliseconds.
Target Audience
Data engineers and startup CTOs at early-stage companies (10-50 employees) who ingest 1M-10M+ rows/day from APIs and need fast, reliable entity resolution without $10k/year tools.
The Problem
Problem Context
Data teams ingest data from multiple APIs daily but struggle to combine records for the same entities (e.g., companies, people, addresses) because manual matching fails on messy real-world data. Their current PostgreSQL + TypeScript setup is too slow for search queries and requires hours of manual tuning.
Pain Points
Exact-match joins fail on typos and variations (e.g., 'John Doe' vs. 'Jon D.'). The API is slow because queries join too many tables. Backfills take hours, and there’s no audit trail for why entities were merged. Custom scripts are hard to maintain as data grows.
Impact
Slow APIs delay product launches, manual fixes waste 10+ hours/week, and unreliable merges create duplicate records. Teams lose trust in their data, leading to bad decisions. Startups hit scaling walls because their tech stack can’t handle 10M+ rows efficiently.
Urgency
Every day without a fix means more wasted dev time, more duplicate records, and slower product iterations. If the API is slow, customers leave. If data is inconsistent, analytics are useless. This is a direct revenue risk for data-driven startups.
Target Audience
Data engineers, analytics engineers, and startup CTOs who work with messy API data (e.g., financial records, HR systems, customer databases). Also affects mid-market companies with similar entity-resolution needs but no in-house data team.
Proposed AI Solution
Solution Approach
A pre-built, serverless entity resolution pipeline that automatically merges duplicate records using fuzzy matching (names, addresses, IDs) and optimizes data for fast API queries. Users connect their APIs, define matching rules via a UI, and get a search-optimized database with audit logs—all for under $100/mo.
Key Features
- API Mart Layer: Denormalized tables for instant search queries (no slow joins).
- Rule Audit Log: Tracks why entities were merged (e.g., 'matched on email + phone').
- Serverless Deployment: Runs on Railway/AWS for <$100/mo, scales with data size.
User Experience
Users connect their APIs via a UI, set matching rules (e.g., 'merge if names are 80% similar'), and get a fast-search API in minutes. The system handles daily data ingestion, merging, and optimization automatically. They can query merged entities instantly and audit changes via a dashboard.
Differentiation
Unlike open-source tools (manual setup) or enterprise tools ($10k/year), this is a *pre-built, affordable pipeline- optimized for search speed. It works with PostgreSQL (no vendor lock-in) and includes fuzzy matching + audit logs—features missing in DIY solutions. Costs 10x less than competitors.
Scalability
Starts at $49/mo for 1M rows, scales to $99/mo for 10M+ rows. Users add more APIs or matching rules without extra cost. The serverless backend auto-scales, so performance stays fast even as data grows. Teams can expand seats as their company grows.
Expected Impact
Teams save 10+ hours/week on manual fixes, APIs load in milliseconds (not seconds), and data is always consistent. Startups ship products faster, and mid-market companies avoid hiring expensive data engineers. The audit log ensures compliance and trust in the data.