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Data Platform Engineering

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.

5
Live platforms built
8,500+
Fight records
11
Federal data systems normalized
19
Parallel sources
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The Data Problem

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.

Services

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.

Live Platforms

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.

mmamodel.ai
Live

UFC Fight Analytics Platform

8,533
UFC fight results
69,576
Per-fight stat records
4-dim Elo
Rating engine
5-model
ML ensemble

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.

GovGreed
Live

Congressional Corruption Prediction Engine

11
Federal data systems
42,143
Bills scored by ML model
924,012
Bill→ticker impact mappings
13,052
Active ML predictions

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.

Lead Detective
Live

Real-Time B2B Sales Intelligence

19
Sources per job
8 min
Full dossier
Parallel
Pipeline architecture
3
Structured outputs

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.

StickySignal
Live

Cannabis Dispensary Customer Data Platform

5
POS systems normalized
Unified
Customer database
LTV
Scoring per customer
Multi
Tenant architecture

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.

CDO
Live

AI-Native Construction Ops Platform

65
Person CA roofing crew, live
~210
Database tables
153
Edge functions
GPS
Selfie clock-in

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.

Architecture

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.

INGESTION LAYER

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.

SCHEMA DESIGN

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.

ACCESS CONTROL

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.

TRANSFORMATION

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.

QUERY PERFORMANCE

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.

MONITORING

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.

FAQ

Common Questions

Do I need to have data already, or can you collect it?
You do not need existing data to start a platform project. We build scraping and collection pipelines as part of the engagement. If you know what data you need and where it lives publicly, we build the collection infrastructure. If you have internal data that needs to be combined with external sources, we design the merge architecture. The only thing we need from you at the start is a clear description of what questions you want to be able to answer with the data.
Who owns the database you build?
You own it completely. We build on your Supabase instance using your credentials. We have no access to your customer data or database contents after the engagement ends. There is no IPS-managed infrastructure layer between you and your data. The database lives in your Supabase project, you have full admin access, and you can export, migrate, or shut it down at any time without our involvement. This is the entire premise of building proprietary infrastructure.
How do you handle conflicting data from different sources?
We handle conflicts explicitly and traceably. When two sources return different values for the same field on the same entity, we do not silently pick one. We store both values with source attribution, apply defined precedence rules based on source reliability, and flag conflicts for human review when no automated resolution is appropriate. The GovGreed platform bridges three different identifier systems for politicians across eleven sources. Entity resolution is done with explicit match confidence scores and manual verification queues for low-confidence matches.
What if I need real-time data instead of batch?
We design for the refresh frequency your use case actually requires. Batch pipelines are appropriate for most cases and run on schedules from minutes to weekly. For genuinely real-time requirements, we use Supabase real-time subscriptions, webhook ingestion pipelines, and event-triggered Edge Functions. Lead Detective runs in real-time mode where each job triggers a fresh parallel source pull. The right architecture depends on your latency requirements, source API rate limits, and cost tolerance for real-time infrastructure.
Can you integrate with my existing tools?
Yes. We routinely build data platforms that pull from or push to HubSpot, Salesforce, Airtable, Notion, Stripe, and custom REST APIs. The Supabase PostgREST API and Edge Functions provide standard REST endpoints that any tool with a webhook or HTTP integration can consume. We also build reverse pipelines that push enriched data back into your CRM after normalization and scoring. The platform is an independent layer that connects to your existing stack rather than replacing it.
How much does custom data platform development cost?
Straightforward single-source platforms with standard normalization, a Supabase schema, and a dashboard start around twenty-five thousand dollars. Multi-source platforms with entity resolution, complex normalization, ML scoring layers, and full pipeline monitoring start around sixty thousand dollars and scale from there based on the number of sources and cross-source entity resolution complexity. GovGreed-scale platforms with eleven federal data systems and a scoring layer are in the hundred to one-hundred-fifty thousand dollar range. We will scope your specific project and give you a number before any commitment.
What database technology do you use and why?
We build on Supabase PostgreSQL for every platform. PostgreSQL is the most capable open-source relational database available, with excellent support for JSON, full-text search, time-series patterns, and complex aggregations. Supabase adds row-level security, a PostgREST API layer, real-time subscriptions, Edge Functions for transformation logic, and a managed hosting environment. Critically, Supabase is fully open-source and your database is portable. If you want to migrate off Supabase to self-hosted PostgreSQL at any point, your schema and data move with you without vendor lock-in.
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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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