Your Data. One Platform.
Built to Last.
Every data advantage starts with ownership. We pull unstructured data from any source on the web, normalize it across conflicting schemas, and build a proprietary database you control entirely. No vendor supply chain. No subscription that disappears. No third-party feed you are dependent on.
The Data Problem Most Businesses Have
Before building a data platform, it helps to be clear about which problem you actually have. Most businesses have one of three.
Your data is scattered across SaaS tools
Customer data in HubSpot. Transaction data in Stripe. Support data in Zendesk. Inventory in a spreadsheet. None of it talks to the rest, none of it is queryable in aggregate, and the moment any vendor raises prices or changes their API, you discover just how little you actually own. A proprietary data platform brings it all into one PostgreSQL database you control.
What you need does not exist in any vendor feed
You want data that no one is selling. Government contract awards cross-referenced against lobbying filings and stock trades. Every UFC fight result with per-round statistics going back to 2005. Dispensary customer purchase history normalized across five different POS systems. This data exists on the web. It is not packaged and sold. We go get it and build the database ourselves.
Your data lives in spreadsheets, PDFs, and emails
Operational data that has never been structured: inspection reports in PDFs, customer notes in email threads, pricing history in Excel. This data has real value for analysis and AI applications but requires extraction, parsing, normalization, and schema design before it is queryable. We build pipelines that convert unstructured source material into structured, queryable databases.
What We Build
A complete data platform engagement covers every layer from raw source to queryable API. You do not need to hire separately for scraping, data engineering, database design, and API development.
Custom Scraping Pipelines
We build scrapers for web pages, government portals, JavaScript-rendered applications, paginated APIs, PDF documents, and data exports. We handle rate limiting, session management, CAPTCHA routing, and IP rotation for large-scale collection. All scrapers are built to run on a schedule and are monitored for source changes that break the pipeline.
Cross-Source Normalization
When LinkedIn uses person identifiers, SEC EDGAR uses CIK numbers, and the FEC uses candidate IDs, the same human has three different keys in three different systems. We build the entity resolution and ID bridging logic that unifies data across sources into a single coherent record. This is the hardest and most valuable part of building a proprietary data platform.
PostgreSQL Schema Architecture
Schema design that reflects how you actually query data, not just how it arrives. We design for your specific query patterns: time-series access, entity relationship traversal, aggregate reporting, and ML feature extraction. Pre-computed metrics tables, materialized views, and index strategies are part of the design from the start, not added later when performance becomes a problem.
Automated Refresh Pipelines
Data that does not stay current loses value quickly. We build refresh pipelines on daily, weekly, or event-triggered schedules that pull new data from sources, run normalization, update existing records, and flag conflicts for review. Pipelines run as Supabase cron functions or scheduled jobs and are monitored for failures with alerting.
API Layer for Applications
A Supabase PostgREST API or custom Edge Function API layer exposes your data to applications, dashboards, and AI systems without direct database access. We design the API surface for your actual query patterns, with appropriate row-level security, rate limiting, and authentication. Your data platform becomes a first-class data service your applications depend on.
Data Quality Monitoring
Automated checks that run after each pipeline execution: expected row counts, null rate thresholds, duplicate detection, referential integrity checks, and range validation for numeric fields. When data quality drops below defined thresholds, the system alerts before bad data reaches dashboards or ML models. Data quality is an operational concern, not a one-time cleanup.
Five Data Platforms Built and Running
Every platform below is live, serves real users, and was built without a third-party data vendor. These are proprietary databases we designed, scraped, normalized, and maintain.
UFC Fight Analytics Platform
Fully self-built with no third-party data feed. Every fight result, per-round statistic, and fighter profile was scraped and normalized from public sources. A 4-dimensional Elo rating system tracks strike accuracy, grappling dominance, finishing ability, and championship-level performance separately. A 5-model stacked ML ensemble using LightGBM, XGBoost, CatBoost, Logistic Regression, and a Siamese neural network retrains weekly via GitHub Actions.
Congressional Corruption Prediction Engine
Eleven federal data systems normalized into a single cross-referenced platform: Congress.gov legislative data, FEC campaign finance records, STOCK Act congressional trade disclosures, Senate LDA lobbying filings, SEC EDGAR filings, OGE 278 disclosures, USASpending.gov contract awards, FARA registrations, and public market price data. The entity resolution layer bridges congressional person IDs, FEC candidate IDs, and STOCK Act filer names into unified politician records across all eleven sources.
Real-Time B2B Sales Intelligence
A real-time data aggregation platform that runs on demand per target company or executive. Sources include LinkedIn, SEC EDGAR, Crunchbase, Google News, Glassdoor, and web presence signals, all running simultaneously. AI models synthesize the combined output into three structured deliverables: an executive dossier, an account plan, and a conversation guide. Eight minutes total versus three days of human research.
Cannabis Dispensary Customer Data Platform
POS data from Dutchie, Flowhub, Treez, Jane, and Blaze each use different schemas, product identifiers, and customer record formats. StickySignal normalizes all five into a unified customer database with complete purchase history, product preference signals, visit frequency, and LTV scoring. Each dispensary tenant gets a single unified view regardless of which POS system they run.
AI-Native Construction Ops Platform
Live at cdohr.ai running day-to-day operations for a 65-person California roofing crew. GPS-verified selfie clock-in replaces paper timesheets, and an AI timecard triage layer flags discrepancies and cites the specific CA Labor Code section behind every compliance issue it surfaces. Built on roughly 210 Supabase tables and 153 edge functions.
How We Design Data Platforms
Every platform we build follows the same structural principles. These are not preferences. They are lessons from building five production systems where data quality and maintainability were requirements, not afterthoughts.
Raw Archive Kept Before Normalization
We always store the raw source data before running any normalization or transformation. This means the database is always reprocessable: if we change the normalization logic, we can re-run it against the original data without going back to the source. This is the difference between a data platform you can improve over time and one that is permanently stuck at its first design decisions.
Separate Staging and Production Schemas
Raw and in-process data lives in a staging schema. Validated, normalized data is promoted to the production schema that application queries read from. This separation means a pipeline failure or bad batch never corrupts the live production data. It also makes the quality checks between stages explicit and auditable.
Supabase PostgreSQL with RLS
All platforms are built on Supabase PostgreSQL with row-level security policies controlling data access at the database layer. This means access control is enforced even if application logic has a bug. Multi-tenant platforms use RLS to guarantee tenant isolation without application-layer filtering that can be bypassed or forgotten.
Edge Functions for Transformation Logic
Supabase Edge Functions handle the transformation and normalization logic that runs between ingestion and production. This keeps the transformation code versioned, testable, and separated from the scraping layer. When a source changes its format, we update the Edge Function and reprocess, without touching the raw archive or the production schema structure.
Pre-Computed Metrics Tables
Dashboard queries and API responses that aggregate or join large datasets are pre-computed into metrics tables that refresh on a schedule. This means dashboard load times are milliseconds, not seconds, regardless of how much raw data exists in the platform. GovGreed serves impact scores, mmamodel.ai serves fighter ratings, and StickySignal serves segment counts all from pre-computed tables.
Pipeline Health and Quality Alerts
Every pipeline we build includes run logging, row count validation, null rate checks, and failure alerting. When a scraper fails because a source changed its structure, we know within the hour. When a normalization run produces fewer records than expected, the alert fires before bad data reaches production. We operate these platforms as live infrastructure, not set-and-forget scripts.
Common Questions
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Your Data Entirely?
Tell us what data you need and what you want to do with it. We will scope the platform and tell you exactly what it takes to build it.
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